What Blockchain can do for The Internet Of Things..

Blockchain and IoT are a marriage made in heaven as the former can enable & augment a variety of application scenarios and usecases for the former. No longer are such scenarios futuristic as we will discuss in this post.

IoT meets Blockchain..

Blockchain and Internet Of Things (IoT) are easily the two biggest buzzwords in technology at the moment. The IoT encompasses the world of sensors,moving objects like vehicles & really any device that has embedded electronics  to communicate with the outside world – typically over an IP protocol.

Combine that with Blockchain – a distributed ledger architecture (DLT) pattern.Combining the two can facilitate the entire lifecycle of IoT devices & applications and prove to be the glue for business processes to act on these events. Consider the following scenario – a private blockchain for a driverless connected car that will enable secure and realtime interactions from the car starting with car startup, driver authentication, smart contracts to exchange insurance & maintenance service information and realtime location info to track safety.

Blockchain based distributed ledgers fill in five critical gaps in IoT implementations..

  1. In such typical scenarios as the above, a Blockchain based distributed ledger provides the trust, record of ownership, transparency and the overall (decentralized) communication backbone for IoT.
  2. It needs to be noted that over the years specific IoT communities will develop their own private blockchains that can store transactions in a highly secure manner for their specific applications. IoT architectures that relied on centralized servers to collect and store data will be able to write to local ledgers that will synch with other localized ledgers to maintain a single yet secure copy of the truth.
  3. All IoT transactions on the Blockchain will be timestamped thus ensuring that they are available essentially – for posterity.
  4. Next up, the true innovation of Blockchain – digital agreements or Smart Contracts. Smart contracts can then be applied on the data in the blockchain to enforce business conditions on the IoT interactions.
  5. Finally, one of the big knocks against IoT has been the relative lack of security standards. Blockchain due to its background on high end cryptography actually helps with IoT security. A future post will discuss such a reference architecture.

With that background, let us consider low hanging usecases across key IoT applications in verticals.

blockchain_iot

  1. Industrial Manufacturing – The manufacturing industry is moving to an entirely virtual world across its lifecycle, ranging from product development, customer demand monitoring to production to inventory management. As devices & systems become more interactive and intelligent, the blockchain can serve as a plant level, regional level and global supply chain level ledger. This will dramatically cut costs and drive more efficient just in time (JIT) processes enabling better usage of plant capacity and improved operational efficiencies.
  2. Connected and Driverless VehiclesThe Connected Vehicle enables the car or truck to behave as a giant Smart App. With the passing of every year, vehicles have more automatic features builtin – ranging from navigation, roadside assistance etc. Blockchain will enable these devices to be tracked on the digital mesh thus enabling easy inter vehicle communication as well as automatic tracking of fleet insurance policies, vehicle registration renewals etc
  3. Transportation – IoT + Blockchain = Connected Transportation. A range of scenarios can be imagined around a connected mesh of vehicles that exchange everything from traffic information to avoiding gridlocks & bottlenecks. Extending this to global trade, this mesh can incorporate shipping, air freight as well as ground transportation to track shipments.
  4. Public Infrastructure & Smart CitiesSmart devices are already being used to track the health of bridges, roads, power grids etc. Blockchains can be used to interconnect these to share efficiencies and to conduct maintenance, forecast usage trends for power usage, pollution etc. Another key area of usage would be to help remote areas such as forests to monitor natural incidents and to prevent catastrophic occurrences like large scale forest fires or farm infestations by blight etc.
  5. Financial services and insurance – Banks could use Blockchain backbone to track IoT enabled devices like ATM machines, remote tellers to conduct maintenance. Insurance companies which have already started deploying drones to verify property claims in remote areas can use the Blockchain to validate and verify claims.
  6. Home and Commercial Realestate management – Using sensors deployed on both homes and commercial buildings helps with automated home and office monitoring. The usecases will diverge across both areas but many can be built on having a foundational distributed ledger capability.
  7. Smart Contracts –  Smart contracts are applicable across all of these areas and can be used to keep track of business rules and any patterns that have been breached. E.g A driverless vehicle that has failed an inspection can be grounded, non payment of home owners insurance can trigger an alert to the homeowner etc.
  8. Retail –  Retailers are already using IoT devices and endpoints to help across the business lifecycle – ranging from the shop floor, to tracking product delivery to store, to understand their customer traffic patterns, wearables etc. The vision of the Connected Store with IoT enabled shelves, an ability for customers to perform more actions using smartphone apps to reducing checkout times with self checkout etc are all taking place. The Blockchain can augment all of these usecases by providing the critical link between retailer and consumer in a way that it automates away the middle man- be it a credit card issuer, or a  central server. For instance consumers can store their product preferences, sizes in a Blockchain and the retailer can access these in a seamless and secure manner.

There still exist large technology shortcomings..

Finally, it needs to be mentioned that there still exist critical gaps in Blockchain technology – whether one considers the public Blockchain on which Bitcoin is built or technologies like Etherium – in terms of interoperability, security standards, throughput and mature developer tooling. These will need to be worked on over the next few quarters before we see production grade IoT deployments on Blockchains.

Conclusion..

The ability of Blockchain to enable secure, global & decentralized communication across billions of IoT endpoints is likely to impact many aspects of business operations and strategies in the coming years.

My take on Gartner’s Top 10 Strategic Technology Trends for 2017

We’re only at the very, very beginning of this next generation of computing and I think that every industry leader will be the ones that transforms first. I don’t care what industry you’re talking about” -Kim Stevenson, CIO, Intel, Feb 2016

Gartner Research rolled out their “Top 10 Strategic Technology Trends for 2017” report a few weeks ago. My goal for this blogpost is to introduce these trends to the reader and to examine the potential impact of their recommendations from an enterprise standpoint.

gartner_trends_2017

                                                              Gartner’s Strategic Trends for 2017 

# 1: AI & Advanced Machine Learning

Gartner rightly forecasts that AI (Artificial Intelligence) and Advanced Machine Learning will continue their march into daily applications run by the Fortune 1000. CIOs are coming to realize that most business problems are primarily data challenges. The rapid maturation of scalable processing techniques allows us to extract richer insights from data. What we commonly refer to as Machine Learning – a combination of econometrics, machine learning, statistics, visualization, and computer science – helps extracts valuable business insights hiding in data and builds operational systems to deliver that value.

Deep Machine Learning involves the art of discovering data insights in a human-like pattern. We are, thus, clearly witnessing the advent of modern data applications. These applications will leverage a range of advanced techniques such as Artificial Intelligence and Machine Learning (ML) encompassing techniques such as neural networks, natural language processing and deep learning.

Implications for industry CIOs – Modern data applications understand their environment (e.g customer preferences and other detailed data insights) to be able to predict business trends in real time & to take action based on them to drive revenues and decrease business risk. These techniques will enable applications and devices to operate in an even more smarter manner while saving companies enormous amounts of money on manual costs.

http://www.vamsitalkstech.com/?p=407

# 2: Intelligent Apps

Personal assistants, e.g Apple Siri, Microsoft Cortona in the category of virtual personal assistants (VPAs), have begun transforming everyday business processes easier for their users. VPAs represent the intersection of AI, conversational interfaces and integration into business processes. In 2017, these will begin improving customer experiences for the largest Fortune 100 enterprises. On the more personal front, Home VPAs will rapidly evolve & become even more smarter as their algorithms get more capable and understanding of their own environments.  We will see increased application of smart agents in diverse fields like financial services,healthcare, telecom and media.

Implications for industry CIOs – Get ready to invest in intelligent applications in the corporate intranet to start with.

# 3: Intelligent Things

The rise of the IoT has only been well documented but couple AI with massive data processing capabilities – that makes up Intelligent Things which can interact with humans in new ways. You can add a whole category of things around transportation (self driving cars, connected cars) and Robots that perform key processes in industrial manufacturing, drones etc.

Implications for industry CIOs – These intelligent devices will increasingly begin communicating with their environments in a manner that will encourage collaboration in a range of business scenarios. 2017 should begin the trend of these devices communicating with each other to form the eponymous ‘Digital Mesh’.

# 4: Virtual & Augmented Reality

Virtual reality (VR) and augmented reality (AR) are technologies that are beginning to completely change the way humans interact with one another and with intelligent systems that make up the Digital Mesh. Pokemon GO & Oculus Rift were the first hugely successful consumer facing AR applications – debuting in 2016. Uses of these technologies will include gamification (to improve customer engagement with products and services), other customer & employee facing applications etc. While both these technologies enable us to view the world in different ways – AR is remarkable in its ability to add to our current reality. BMW’s subsidiary Mini has actually developed a driving goggle with AR technology[1].

Implications for industry CIOs – This one is still on the drawing board for most verticals but it does make sense to invest in areas like gamification and in engaging with remote employees using AR.

# 5: Digital Twin

A Digital twin is a software personification of an Intelligent Thing or system. In the manufacturing industry, digital twins can be setup to function as proxies of things like sensors and gauges, Coordinate Measuring Machines, lasers, vision systems, and white light scanning [2]. The wealth of data being gathered on the shop floor will ensure that Digital twins will be used to reduce costs and increase innovation. Data science will soon make it’s way into the shop floor to enable the collection of insights from these software proxies.

Implications for industry CIOs – Invest in Digital capabilities that serve as proxies for physical things.

# 6: Blockchain

The term Blockchain is derived from a design pattern that describes a chain of data blocks that map to individual transactions. Each transaction that is conducted in the real world (e.g a Bitcoin wire transfer) results in the creation of new blocks in the chain. The new blocks so created are done so by calculating a cryptographic hash function of its previous block thus constructing a chain of blocks – hence the name.

Blockchain is a distributed ledger (DLT) which allows global participants to conduct secure transactions that could be of any type – banking, music purchases, legal contracts, supply chain transactions etc. Blockchain will transform multiple industries in the years to come. Bitcoin is the first application of Blockchain.

How the Blockchain will lead disruption across industry..(5/5)

Implications for industry CIOs – Begin expanding internal knowledge on Blockchain and as to how it can potentially augment or disrupt your vertical industry.

# 7: Conversational Systems

Mobile applications first begun forcing the need for enterprises to begin supporting multiple channels of interaction with their consumers. For example Banking now requires an ability to engage consumers in a seamless experience across an average of four to five channels – Mobile, eBanking, Call Center, Kiosk etc. Conversational Systems take these interactions to the next level and enable humans to communicate with a wide range of Intelligent Things using a range of channels – speech, touch, vision etc.

Implications for industry CIOs – Every touch point matters, and those leading the smart agent transformation should constantly be asking how organizations are removing friction and enhancing the experience for every customer regardless of where they are in the journey.

# 8: Mesh App and Service Architecture

This one is still from last year. The Digital Mesh leads to an interconnected information deluge which encompasses classical IoT endpoints along with audio, video & social data streams. The creation of these smart services will further depend on the vertical industries that these products serve as well as requirements for the platforms that host them. E.g industrial automation, remote healthcare, public transportation, connected cars, home automation etc.The micro services architecture approach which combines the notion of autonomous, cooperative yet loosely coupled applications built as a conglomeration of business focused services is a natural fit for the Digital Mesh.  The most important additive and consideration to micro services based architectures in the age of the Digital Mesh is what I’d like to term –  Analytics Everywhere.

Implications for industry CIOs -The mesh app will require a microservices based architecture which supports multichannel & multi device solutions.

# 9: Digital Technology Platforms

The onset of Digital Architectures in enterprise businesses implies the ability to drive continuous micro level interactions with global consumers/customers/clients/stockholders or patients depending on the vertical you operate in. More information on the core building blocks of Digital Technology Platforms at the below blogpost.

Implications for industry CIOs

http://www.vamsitalkstech.com/?m=201609

# 10: Adaptive Security Architecture

The evolution of the intelligent digital mesh and digital technology platforms and application architectures means that security has to become fluid and adaptive.Traditional solutions cannot handle this challenge which is exacerbated by the expectation that in an IoT & DM world, data flows will be multidirectional across a grid of application endpoints.

Implications for industry CIOs -Expect to find applications in 2016 and beyond incorporating Deep Learning and Real Time Analytics into their core security design with a view to analyzing large scale data at a very low latency. Security in the IoT environment is particularly challenging. Security teams need to work with application, solution and enterprise architects to build security into the overall DevOps process to create a DevSecOps model.

Conclusion..

In this year’s edition, Gartner are clearly forecasting the future ten years out from a mass market standpoint. As we cross this chasm slowly over the next ten years, we will see that IoT begin to emerge and take center stage in every industry vertical. Digital transformation will happen on apps created for and brought together for Smart Agents on the Device Mesh.

These apps will gradually become autonomous, data intensive,server-less, hopefully secure and location independent (data center or cloud). The app can be a sensor or a connected car or a digital twin for a manufacturing technician. So, it’s not just about a single app sitting in a data center or the cloud or on the machine itself. These smart agent apps will data driven, components of a larger mesh, interconnected connected using open interfaces, and resident at the places where it’s optimal for realtime analytics. This may seem like science fiction for the Fortune 1000 enterprise but it is manifest reality at the web scale innovators. The industry will have no choice but to follow.

References..

[1] Cramer – “A lesson in Augmented Realities” –  http://cramer.com/story/the-difference-between-ar-and-vr/

[2] Dr.Michael Grieves – “Digital Twin: Manufacturing Excellence through Virtual Factory Replication” – http://innovate.fit.edu/plm/documents/doc_mgr/912/1411.0_Digital_Twin_White_Paper_Dr_Grieves.pdf

Why Digital Disruption is the Cure for the Common Data Center..

The foundation of digital business is the boundary-free enterprise, which is made possible by an array of time- and location-independent computing capabilities – cloud, mobile, social and data analytics plus sensors and APIs. There are no shortcuts to the digital enterprise.”

— Mike West,Analyst, Saugatack Research,2015

At its core Digital is a fairly straightforward concept. It is essentially about offering customers more contextual and relevant experiences while creating internal teams that can turn on a dime to serve customers. It is clear that these kinds of consumer capabilities just cannot be offered using an existing technology stack. This blogpost seeks to answer what this next generation computing stack may look like.

What Digital has in Store for Enterprises…

Digital transformation is a daily fact of life at web scale shops like Google, Amazon, Apple, Facebook and Netflix. These mega shops have built not just intuitive and appealing applications but have gradually evolved them into platforms that offer discrete marketplaces that serve global audiences. They also provide robust support for mobile applications that deliver services such as content, video, e-commerce, gaming etc via such channels. In fact they have heralded the age of new media and in doing so have been transforming both internally (business models, internal teams & their offerings) as well as externally.

CXOs at established Fortune 1000 enterprises were unable to find resonance in these stories from the standpoint of their enterprise’s reinvention. This makes a lot of sense as these established companies have legacy investments and legacy stakeholders – both of which represent change inhibitors that the FANGs (Facebook Amazon Netflix and Google) did not have. Enterprise practitioners need to understand how Digital technology can impact both existing technology investments and the future landscape.

Where are most Enterprises at the moment…

Much of what exists in the datacenters across organizations are antiquated from a technology stack. These range from hardware platforms to network devices & switches to monolithic applications running on them. Connecting these applications are often proprietary or manual integraton architectures. There are inflexible, proprietary systems & data architectures, lots of manual processes, monolithic applications and tightly coupled integration. Rapid provisioning of IT resources is a huge bottleneck which frequently leads to lines of business adopting the public cloud to run their workloads.  According to Rakesh Kumar, managing vice president at Gartner – “For over 40 years, data centers have pretty much been a staple of the IT ecosystem,Despite changes in technology for power and cooling, and changes in the design and build of these structures, their basic function and core requirements have, by and large, remained constant. These are centered on high levels of availability and redundancy, strong, well-documented processes to manage change, traditional vendor management and segmented organizational structures. This approach, however, is no longer appropriate for the digital world.” [2]

On that note, the below blogpost had captured the three essential technology investments that make up Digital Transformation.

The Three Core Competencies of Digital – Cloud, Big Data & Intelligent Middleware

If Digital has to happen, IT is one of the largest stakeholders…

Digital applications present seamless expereinces across channels & devices, are tailored to individual customers needs, understand their preferences & need to be developed in an environment of constant product innovation.

So, which datacenter capabilities are required to deliver this?

Figuring out the best architectural foundation to support , leverage & monetize on digital experiences is complex.  The past few years have seen the rapid evolution of many transformational technologies—Big Data, Cognitive Computing, Cloud technology (Public clouds, OpenStack, PaaS, Containers, Software-defined networking & storage), the Blockchain – the list goes on and on. These are leading enterprises to a smarter way of developing enterprise applications and to a more modern, efficient, scalable, cloud-based architectures.

So, what capabilities do Datacenters need to innovate towards?

digital_datacenter

                                         The legacy Datacenter transitions to the Digital Datacenter

While, the illustration above is self explanatory. Enterprise IT will need to majorly embrace Cloud Computing – whatever forms the core offering may take – public, private or hybrid. The compute infrastructure ranging from a mix of open source virtualization to Linux containers. Containers essentially virtualize the operating system so that multiple workloads can run on a single host, instead of virtualizing a server to create multiple operating systems. These containers are easily ported across different servers without the need for reconfiguration and require less maintenance because there are fewer operating systems to manage. For instance, the OpenStack Cloud Project specifies Docker (a defacto standard), a Linux format for containers that’s designed to automate the deployment of applications as highly portable, self-sufficient containers.

Cloud computing will also enable the rapid scale up & scale down across the gamut of infrastructure (compute – VM/Baremetal/Containers, storage – SAN/NAS/DAS, network – switches/routers/Firewalls etc) in near real-time (NRT). Investments in SDN (Software Defined Networking) will be de riguer in order to improve software based provisioning, network, time to market and to drive network equipment costs down. The other vector that brings about datacenter innovation is around automation i.e vastly reducing manual efforts in network and application provisioning. These capabilities will be key as the vast majority of digital applications are deployed as Software as a service (SaaS).

An in depth discussion of these Software Defined capabilities can be found at the below blogpost.

Financial Services IT begins to converge towards Software Defined Datacenters..

Applications developed for a Digital infrastructure will be developed as small, nimble processes that communicate via APIs and over infrastructure like service mediation components (e.g Apache Camel). These microservices based applications will offer huge operational and development advantages over legacy applications. While one does not expect legacy but critical applications that still run on mainframes (e.g. Core Banking, Customer Order Processing etc) to move over to a microservices model anytime soon, customer facing applications that need responsive digital UIs will definitely move.

Which finally brings us to the most important capability of all – Data. The heart of any successful Digital implementation is Data. The definition of Data includes internal data (e.g. customer data, data about transactions, customer preferences data), external datasets & other relevant third party data (e.g. from retailers) etc.  While each source of data may not radically change an application’s view of its customers, the combination of all promises to do just that.

The significant increases in mobile devices and IoT (Internet of Things) capable endpoints will ensure exponential increases in data volumes will occur. Thus Digital applications will need to handle this data – not just to process it but also to be able to glean real time insights.  Some of the biggest technology investments in ensuring a unified customer journeys are in the areas of Big Data & Predictive Analytics. Enterprises should be able to leverage a common source of data that transcends silos (a data lake) to be able to drive customer decisions that drive system behavior in real time using advanced analytics such as Machine Learning techniques, Cognitive computing platforms etc which can provide accurate and personalized insights to drive the customer journey forward.

Can Datacenters incubation innovation ?

Finally, one of the key IT architectural foundation strategies companies need to invest in is modern application development. Gartner calls such a feasible approach “Bimodal IT”. According to Gartner, “infrastructure & operations leaders must ensure that their internal data centers are able to connect into a broader hybrid topology“.[2]  Let us consider Healthcare – a reasonably staid vertical as an example. In a report released by EY, “Order from Chaos – Where big data and analytics are heading, and how life sciences can prepare for the transformational tidal wave,” [1] the services firm noted that an agile environment can help organizations create opportunities to turn data into innovative insights. Typical software development life cycles that require lengthy validations and quality control testing prior to deployment can stifle innovation. Agile software development, which is adaptive and is rooted in evolutionary development and continuous improvement, can be combined with DevOps, which focuses on the the integration between the developers and the teams who deploy and run IT operations. Together, these can help life sciences organizations amp up their application development and delivery cycles. EY notes in its report that life sciences organizations can significantly accelerate project delivery, for example, “from three projects in 12 months to 12 projects in three months.”

Finally, Big Data has evolved to enable the processing of data in a batch, interactive, low latency manner depending on the business requirements – which is a massive gain for Digital projects. Big Data and DevOps will both go hand in hand to deliver new predictive capabilities.

Further, business can create digital models of client personas and integrate these with predictive analytic tiers in such a way that an API (Application Programming Interface) approach is provided to integrate these with the overall information architecture.

Conclusion..

More and more organizations are adopting a Digital first business strategy.  The current approach as in vogue – to treat these as one-off, tactical project investments – does not simply work or scale anymore. There are various organizational models that one could employ from the standpoint of developing analytical maturity. These ranging from a shared service to a line of business led approach. An approach that I have seen work very well is to build a Digital Center of Excellence (COE) to create contextual capabilities, best practices and rollout strategies across the larger organization.

References –

[1] E&Y – “Order From Chaos” http://www.ey.com/Publication/vwLUAssets/EY-OrderFromChaos/$FILE/EY-OrderFromChaos.pdf

[2] Gartner – ” Five Reasons Why a Modern Data Center Strategy Is Needed for the Digital World” – http://www.gartner.com/newsroom/id/3029231

Why Big Data & Advanced Analytics are Strategic Corporate Assets..

The industry is all about Digital now. The explosion in data storage and processing techniques promises to create new digital business opportunities across industries. Business Analytics concerns itself from deriving insights from data that is produced as a byproduct of business operations as well as external data that reflects customer insights. Due to their critical importance in decision making, Business Analytics is now a boardroom matter and not just one confined to the IT teams. My goal in this blogpost is to quickly introduce the analytics landscape before moving on to the significant value drivers that only Predictive Analytics can provide.

The Impact of Business Analytics…

The IDC “Worldwide Big Data and Analytics Spending Guide 2016”, predicts that the big data and business analytics market will grow from $130 billion by the end of this year to $203 billion by 2020[1] at a   compound annual growth rate (CAGR) of 11.7%. This exponential growth is being experienced across industry verticals such as banking & insurance, manufacturing, telecom, retail and healthcare.

Further, during the next four years, IDC finds that large enterprises with 500+ employees will be the main driver in big data and analytics investing, accounting for about $154 billion in revenue. The US will lead the market with around $95 billion in investments during the next four years – followed by Western Europe & the APAC region [1].

The two major kinds of Business Analytics…

When we discuss the broad topic of Business Analytics, it needs to be clarified that there are two major disciplines – Descriptive and Predictive. Industry analysts from Gartner & IDC etc. will tell you that one also needs to widen the definition to include Diagnostic and Prescriptive. Having worked in the industry for a few years, I can safely say that these can be subsumed into the above two major categories.

Let’s define the major kinds of industrial analytics at a high level –

Descriptive Analytics is commonly described as being retrospective in nature i.e “tell me what has already happened”. It covers a range of areas traditionally considered as BI (Business Intelligence). BI focuses on supporting operational business processes like customer onboarding, claims processing, loan qualification etc via dashboards, process metrics, KPI’s (Key Performance Indicators). It also supports a basic level of mathematical techniques for data analysis (such as trending & aggregation etc.) to infer intelligence from the same.  Business intelligence (BI) is a traditional & well established analytical domain that essentially takes a retrospective look at business data in systems of record. The goal of the Descriptive disciplines is to primarily look for macro or aggregate business trends across different aspects or dimensions such as time, product lines, business units & operating geographies.

  • Predictive Analytics is the forward looking branch of analytics which tries to predict the future based on information about the past. It describes what “can happen based on the patterns in data”. It covers areas like machine learning, data mining, statistics, data engineering & other advanced techniques such as text analytics, natural language processing, deep learning, neural networks etc. A more detailed primer on both along with detailed use cases are found here –

The Data Science Continuum in Financial Services..(3/3)

The two main domains of Analytics are complementary yet different…

Predictive Analytics does not intend to, nor will it, replace the BI domain but only adds significant sophisticated analytical capabilities enabling businesses to be able to do more with all the data they collect. It is not uncommon to find real world business projects leveraging both these analytical approaches.

However from an implementation standpoint, the only common area of both approaches is knowledge of the business and the sources of data in an organization. Most other things about them vary.

For instance, predictive approaches both augment & build on the BI paradigm by adding a “What could happen” dimension to the data.

The Descriptive Analytics/BI workflow…

BI projects tend to follow a largely structured process which has been well defined over the last 15-20 years. As the illustration below describes it, data produced in operational systems is subject to extraction, transformation and eventually is loaded into a data warehouse for consumption by visualization tools.

descriptive_analytics

                                                                       The Descriptive Analysis Workflow 

Descriptive Analytics and BI add tremendous value to well defined use cases based on a retrospective look at data.

However, key challenges with this process are –

  1. the lack of a platform to standardize and centralize data feeds leads to data silos which cause all kinds of cost & data governance headaches across the landscape
  2. complying with regulatory initiatives (such as Anti Money Laundering or Solvency II etc.) needs the warehouse to handle varying types of data which is a huge challenge for most of the EDW technologies
  3. the ability to add new & highly granular fields to the data feeds in an agile manner requires extensive relational modeling upfront to handle newer kinds of schemas etc.

Big Data platforms have overcome past shortfalls in security and governance and are being used in BI projects at most organizations. An example of the usage of Hadoop in classic BI areas like Risk Data Aggregation are discussed in depth at the below blog.

http://www.vamsitalkstech.com/?p=2697

That being said, BI projects tend to follow a largely structured process which has been well defined over the last 20 years. This space serves a large existing base of customers but the industry has been looking to Big Data as a way of constructing a central data processing platform which can help with the above issues.

BI projects are predicated on using an EDW (Enterprise Data Warehouse) and/or RDBMS (Relational Database Management System) approach to store & analyze the data. Both these kinds of data storage and processing technologies are legacy in terms of both the data formats they support (Row-Column based) as well as the types of data they can store (structured data).

Finally, these systems fall short of processing data volumes generated by digital workloads which tend to be loosely structured (e.g mobile application front ends, IoT devices like sensors or ATM machines or Point of Sale terminals), & which need business decisions to be made in near real time or in micro batches (e.g detect credit card fraud, suggest the next best action for a bank customer etc.) and increasingly cloud & API based to save on costs & to provide self-service.

That is where Predictive Approaches on Big Data platforms are beginning to shine and fill critical gaps.

The Predictive Analytics workflow…

Though the field of predictive analytics has been around for years – it is rapidly witnessing a rebirth with the advent of Big Data. Hadoop ecosystem projects are enabling the easy ingestion of massive quantities of data thus helping the business gather way more attributes about their customers and their preferences.

data_science_process

                                                                    The Predictive Analysis Workflow

The Predictive Analytics workflow always starts with a business problem in mind. Examples of these would be “A marketing project to detect which customers are likely to buy new products or services in the next six months based on their historical & real time product usage patterns – which are denoted by x, y or z characteristics” or “Detect real-time fraud in credit card transactions.”

In use cases like these, the goal of the data science process is to be able to segment & filter customers by corralling them into categories that enable easy ranking. Once this is done, the business is involved to setup easy and intuitive visualization to present the results.

A lot of times, business groups have a hard time explaining what they would like to see – both data and the visualization. In such cases, a prototype makes things easier from a requirements gathering standpoint.  Once the problem is defined, the data scientist/modeler identifies the raw data sources (both internal and external) which comprise the execution of the business challenge.  They spend a lot of time in the process of collating the data (from Oracle/SQL Server, DB2, Mainframes, Greenplum, Excel sheets, external datasets, etc.). The cleanup process involves fixing a lot of missing values, corrupted data elements, formatting fields that indicate time and date etc.

The data wrangling phase involves writing code to be able to join various data elements so that a single client’s complete dataset is gathered in the Data Lake from a raw features standpoint.  If more data is obtained as the development cycle is underway, the Data Science team has no option but to go back & redo the whole process. The modeling phase is where algorithms come in – these can be supervised or unsupervised. Feature engineering takes in business concepts & raw data features and creates predictive features from them. The Data Scientist takes the raw & engineered features and creates a model using a mix of various algorithms. Once the model has been repeatedly tested for accuracy and performance, it is typically deployed as a service. Models as a Service (MaaS) is the Data Science counterpart to Software as a Service. The MaaS takes in business variables (often hundreds of inputs) and provides as output business decisions/intelligence, measurements, & visualizations that augment decision support systems.

 How Predictive Analytics changes the game…

Predictive analytics can bring about transformative benefits in the following six ways.

  1. Predictive approaches can be applied to a much wider & richer variety of business challenges thus enabling an organization to achieve outcomes that were not really possible with the Descriptive variety. For instance, these use cases range from Digital Transformation to fraud detection to marketing analytics to IoT (Internet of things) across industry verticals. Predictive approaches are real-time and not just batch oriented like the Descriptive approaches.
  2. When deployed strategically – they can scale to enormous volumes of data and help reason over them reducing manual costs.  It can take on problems that can’t be managed manually because of the huge amount of data that must be processed.
  3. They can predict the results of complex business scenarios by being able to probabilistically predict different outcomes across thousands of variables by perceiving minute dependencies between them. An example is social graph analysis to understand which individuals in a given geography are committing fraud and if there is a ring operating
  4. They are vastly superior at handling fine grained data of manifold types than can be handled by the traditional approach or by manual processing. The predictive approach also encourages the integration of previously “dark” data as well as newer external sources of data.
  5. They can also suggest specific business actions(e.g. based on the above outcomes) by mining data for hitherto unknown patterns. The data science approach constantly keeps learning in order to increase its accuracy of decisions
  6. Data Monetization–  they can be used to interpret the mined data to discover solutions to business challenges and new business opportunities/models

References

[1] IDC Worldwide Semiannual Big Data and Business Analytics Spending Guide – Oct 2016 “Double-Digit Growth Forecast for the Worldwide Big Data and Business Analytics Market Through 2020 Led by Banking and Manufacturing Investments, According to IDC

http://www.idc.com/getdoc.jsp?containerId=prUS41826116

 

Demystifying Digital – the importance of Customer Journey Mapping…(2/3)

The first post in this three part series on Digital Foundations @ http://www.vamsitalkstech.com/?p=2517 introduced the concept of Customer 360 or Single View of Customer (SVC).  We discussed specific benefits from both a business & operational standpoint that are enabled by SVC. This second post in the series introduces the concept of a Customer Journey. The third & final post will focus on a technical design & architecture needed to achieve both these capabilities.

Introduction to Customer Journey Mapping…

The core challenge many Banks have is their ability to offer a unified customer experience for banking services across different touch points. The lack of such a unified experience negatively impacts the quality of the overall banking experience.

Thus, Customer Journey Mapping refers to the process of creating a visual depiction of a customers adoption and usage of banking products across different channels or touch points(branch,mobile,phone,chat,email etc). The journey provides dynamic & realtime insight into the total customer lifetime value (CLV) as the person has progressed in her or his life journey. The goal of the customer journey mapping is to provide the bank personnel with a way of servicing the customer better while increasing the bank’s net economic value from servicing this customer.

The result of the journey mapping process is to drive overall engagement model from the customers perspective and not solely the Banks internal processes.

Banks may be curious as to why they need a new approach to customer centricity? Quite simple, just consider the sheer complexity for signing up for new banking products such as checking or savings accounts or receiving credit for a simple checking deposit. At many banks these activities can take a couple of days. Products with higher complexity like home mortgage applications can take weeks to process even for those consumers with outstanding credit. Consumers are beginning to constantly compare these slow cycle times to the realtime service they commonly obtain using online services such as Amazon or Apple Pay or Google Wallet or Airbnb or even FinTechs. For internal innovation to flourish, customer centric mindset rather than an internal process centric mindset is what is called for at most incumbent Banks.

The Boston Consulting Group (BCG) has proposed a six part program for Banks to improve their customer centricity as a way of driving increased responsiveness and customer satisfaction[1]. This is depicted in the below illustration.

customer_journey_mapping

Customer Journey Mapping in Banking involves six different areas. Redrawn & readapted from BCG Analysis [1]
  1. Invest in intuitive interfaces for both customer & internal stakeholder interactions–  Millenials who use services like Uber, Facebook, Zillow, Amazon etc in their daily lives are now very vocal in demanding a seamless experience across all of their banking services using digital channels.  The first component of client oriented thinking is to provide UI applications that smoothly facilitate products that reflect individual customers lifestyles, financial needs & behavioral preferences. The user experience will offer different views to business users at various levels in the bank – client advisors, personal bankers, relationship managers, brach managers etc.  The second component is to provide a seamless experience across all channels (mobile, eBanking, tablet, phone etc) in a way that the overall journey continuous and non-siloed. The implication is that clients should be able to begin a business transaction in channel A and be able to continue them in channel B where it makes business sense.
  2. Technology Investments – The biggest technology investments in ensuring a unified customer journey are in the areas of Big Data & Predictive Analytics. Banks should be able to leverage a common source of data that transcends silos to be able to drive customer decisions that drive system behavior in real time using advanced analytics such as Machine Learning techniques, Cognitive computing platforms etc which can provide accurate and personalized insights to drive the customer journey forward. Such platforms need to be deployed in strategic areas such as the front office, call center, loan qualification etc. Further, business can create models of client personas and integrate these with the predictive analytic tier in such a way that an API (Application Programming Interface) approach is provided to integrate these with the overall information architecture.
  3. Agile Business Practices–  Customer Journey Mapping calls for cross organizational design teams consisting of business experts, UX designers, Developers & IT leaders. The goal is to create intuitive & smart client facing applications using a rapid and agile business & development lifecycle. 
  4. Risk & Compliance –  Scalable enterprise customer journey management also provides a way to integrate risk and compliance functions such as customer risk, AML compliance into the process. This can be achieved using a combination of machine learning & business rules.
  5. Process Workflow – It all starts with the business thinking outside the box and bringing in learnings from other verticals like online retailing, telecom, FinTechs etc to create journeys that reimagine existing business processes using technology. An example would be to reimagine the mortgage application process by having the bank grant a mortgage using a purely online process by detecting that this may be the best product for a given consumer. Once the initial offer is validated using a combination of publicly available MLS (Multi Listing Scheme) data & the consumer’s financial picture, the bank can team up with realtors to provide the consumer with an online home shopping experience and help take the process to a close using eSigning.
  6. Value Proposition – It is key for financial services organizations to identify appropriate usecases as well as target audiences as they begin creating critical customer journeys. First identifying & then profiling these key areas such as customer onboarding, mortgage/auto loan application, fraud claims management workflows in the retail bank, digital investment advisory in wealth management etc are key. Once identified, putting in place strong value drivers with demonstrable ROI metrics is critical in getting management buy in. According to BCG,banks that have adopted an incremental approach to customer journey innovation have increased their revenues by 25% and their productivity by 20% to 40% [1].

Conclusion..

As financial services firms begin to embark on digital transformation, they will need to transition to a customer oriented mindset. Along with a Single View of Client, Customer Journey Mapping is a big step to realizing digitization. Banks that can make this incremental transition will surely realize immense benefits in customer lifetime value & retention as compared to their peers.Furthermore, when a Bank embarks on Data Monetization – using the vast internal data (about customers, their transaction histories, financial preferences, operational insights etc) to create new products or services or to enhance the product experience – journey mapping is a foundational capability that they need to possess.

References..

[1] Boston Consulting Group 2016- “How digitized Customer Journeys can help Banks win hearts, minds and profits”

A POV on European Banking Regulation.. MAR, MiFiD II et al

Today’s European financial markets hardly resemble the ones from 15 years ago. The high speed of electronic trading, explosion in trading volumes, the diverse range of instruments classes & a proliferation of trading venues pose massive challenges.  With all this complexity, market abuse patterns have also become egregious. Banks are now shelling out millions of euros in fines for market abuse violations. In response to this complex world, European regulators thus have been hard at work. They have created rules for surveillance of exchanges with a view to detecting suspicious patterns of trade behavior & increase market transparency. In this blogpost,we will discuss the state of the regulatory raft as well as propose a Big Data led reengineering of techniques of data storage, records keeping & forensic analysis to help Banks meet the same.

A Short History of Market Surveillance Regulation in the European Union..

A visitor passes a sign in the lobby of the European Securities and Markets Authority's (ESMA) headquarters in Paris, France, on Thursday, June, 20, 2013. French gross domestic product will probably drop this year after stalling in 2012 as households trim spending and companies slash investment, national statistics office Insee predicted. Photographer: Balint Porneczi/Bloomberg
The lobby of the European Securities and Markets Authority’s (ESMA) headquarters in Paris, France. Photographer: Balint Porneczi/Bloomberg

As we have seen in previous posts, firms in typically most riskiest part of Banking – Capital Markets – deal in complex financial products in a dynamic industry. Over the last few years, Capital Markets have been undergoing a rapid transformation  – at a higher rate perhaps than Retail Banking or Corporate Banking. This is being fueled by technology advances that produce ever lower latencies of trading, an array of financial products, differing (and newer) market participants, heavy quant based trading strategies and multiple venues (exchanges, dark pools etc) that compete for flow based on new products & services.

The Capital Markets value chain in Europe encompasses firms on the buy side (e.g wealth managers), the sell side (e.g broker dealers) & firms that provide custodial services as well as technology providers who provide platforms for post trade analytics support. The crucial link to all of these is the execution venues themselves as well as the clearing houses.With increased globalization driving the capital markets and an increasing number of issuers, one finds an ever increasing amount of complexity across a range of financial instruments assets (stocks, bonds, derivatives, commodities etc).

In this process, over the last few years the ESMA (European Securities and Markets Authority) has slowly begin to harmonize various pieces of legislation that were originally intend to protect the investor. We will focus on two major regulations that market participants in the EU now need to conform with. These are the MiFID II (Markets in Financial Instruments Directive) and the MAR (Market Abuse Regulation). While both these regulations have different effective dates, together they supplant the 2003 passage of the original MAD (Market Abuse Directive). The global nature of capital markets ensured that the MAD was outdated to the needs to today’s financial system. A case in point is the manipulation of the LIBOR (London Interbank Offered Rate) benchmark & the FX Spot Trading scandal in the UK- both of which clearly illustrated the limitations of dated regulation passed a decade ago.  The latter is concerned with the FX (Foreign Exchange) market which is largest yet most liquid financial markets in the world. The turnover approaches around $5.3 trillion as of 2014 with the bulk of it concentrated in London. In 2014, the FCA (Financial Control Authority) fined several leading banks 1.1 billion GBP for market manipulation. All of that being said, let us quickly examine the two major areas of regulation before we study the downstream business & technology ramifications.

Though we will focus on MiFiD II and MAR in this post, the business challenges and technology architecture are broadly applicable across areas such as Dodd Frank CAT in the US & FX Remediation in the UK etc.

MiFiD,MiFiD II and MAR..

MiFiD (Markets in Financial Instruments Directive) originally started as the investment services directive in the UK in the early 90s. As EU law # (2004/39/EC), it has been applicable across the European Union since November 2007. MiFiD is a cornerstone of the EU’s regulation of financial markets seeking to improve the competitiveness of EU financial markets by creating a single market for investment services and activities and to ensure a high degree of harmonised protection for investors in financial instruments.MiFiD sets out basic rules of market participant conduct across the EU financial markets.It is intended to cover market type issues – best execution, equity & bond market supervision. It also incorporates statues for Investor Protection.

The financial crisis of 2008 (http://www.vamsitalkstech.com/?p=2758) led to a demand by G20 leaders to create more safer and resilient financial markets. This was for multiple reasons – ranging from overall confidence in the integrity of the markets to exposures of households & pension funds to these markets to ensuring the availability of capital for businesses to grow. Regulators across the globe thus began to address these changes to create safer capital markets. After extensive work, it has been concluded from a political standpoint and has evolved into two separate areas – MiFiD II & MiFiR.  MiFID II expands on the original MiFID & goes live in 2018 [1], has rules built in that deal with breaching thresholds, disorderly trading and other potential abuse[2].

The FX market is one of the largest and most liquid markets in the world with a daily average turnover of $5.3 trillion, 40% of which takes place in London. The spot FX market is a wholesale financial market and spot FX benchmarks (also known as “fixes”) are used to establish the relative value of two currencies.  Fixes are used by a wide range of financial and non-financial companies, for example to help value assets or manage currency risk.

MiFiD II transparency requirements cover a whole range of organizations in a very similar way including –

  1. A range of trading venues including Regulated Markets (RM), Multilateral trading facilities (MTF) & Organized trading facilities (OTF)
  2. Investment firms (any entity providing investment services) and the Systematic internalizers (clarified as any firm designated as a market maker or a bank that has an ability net out counterparty positions due to it’s order flow)
  3. Ultimately, MiFiD II affects the complete range of actors in the EU financial markets. This covers a range of asset managers, custodial services, wealth managers etc irrespective of where they are based (EU or no-EU)

The most significant ‘Transparency‘ portion of MiFID II expands the regime that was initially created for equity instruments in the original directive. It adds reporting requirements for both bonds and derivatives. Similar to the reporting requirements under Dodd Frank, this includes both trade reporting – public reporting of trades in realtime, and transaction reporting, – regulatory reporting no later than T+1.

Beginning early January 2018[1] when MiFID II goes into effect – both EU firms & regulators will be required to monitor a whole range of transactions as well as store more trade data across the lifecycle. Firms are also required to file Suspicious Transaction Reports (STR) as and when they detect suspicious trading patterns that may connote forms of market abuse.

The goal of the Market Abuse Regulation (MAR) is to ensure that regulatory rules stay in lockstep with the tremendous technological progress around trading platforms especially High Frequency Trading (HFT). The Market Abuse Directive (MAD) complements the MAR by ensuring that all EU member states adopt a common taxonomy of definitions for a range of market abuse. The MAR

Meanwhile, MAR defines inside information & trading with concrete examples of rogue behavior including collusion, ping orders, abusive squeeze, cross-product manipulation,  floor/ceiling price pattern, ping orders, phishing,  improper matched orders, concealing ownership, wash trades, trash and cash, quote stuffing, excessive bid/offer spread, and ‘pump and dump’ etc.

The MAR went live on July 2016. It’s goal is to ensure that rules keep pace with market developments, such as new trading platforms, as well as new technologies, such as high frequency trading (HFT) and Algorithmic trading. The MAR also requires identification requirements on the trader or algorithm that is responsible for an investment decision.

MiFID II clearly requires that firms have in place systems and controls that monitor such behaviors and are able to prevent disorderly markets.

The overarching intent of both MiFiD II & MAR is to maintain investor faith in the markets by ensuring market integrity, transparency and by catching abuse as it happens. Accordingly, the ESMA has asked for sweeping changes across how transactions on a range of financial instruments – equities, OTC traded derivatives etc – are handled. These changes have ramifications for Banks, Exchanges & Broker Dealers from a record keeping, trade reconstruction & market abuse monitoring, detection & prevention standpoint.

Furthermore, MiFID II enhances requirements for transaction reporting by including venues such as High Frequency Trading , Direct electronic access (DEA) providers &  General clearing members (GCM). The reporting granularity has also been extended to identifying the trader and the client across the order lifecycle for a given transaction.

Thus, beginning early 3rd January 2018 when MiFiD II goes into effect , both firms and regulators will be required to capture & report on detailed order lifecycle for trades.

Key Business & Technology Requirements for MiFid II and MAR Platforms..

While these regulations have broad ramifications across a variety of key functions including compliance, compensation policies, surveillance etc- one of the biggest obstacles is technology which we will examine as well as provide some guidance around.

Some of the key business requirements that can be distilled from these regulatory mandates include the below:

  • Store heterogeneous data – Both MiFiD II and MAR mandate the need to perform trade monitoring & analysis on not just real time data but also historical data spanning a few years. Among others this will include data feeds from a range of business systems – trade data, valuation & position data, reference data, rates, market data, client data, front, middle & back office, data, voice, chat & other internal communications etc. To sum up, the ability to store a range of cross asset (almost all kinds of instruments), cross format (structured & unstructured including voice), cross venue (exchange, OTC etc) trading data with a higher degree of granularity – is key.
  • Data Auditing – Such stored data needs to be fully auditable for 5 years. This implies not just being able to store it but also putting in place capabilities in place to ensure  strict governance & audit trail capabilities.
  • Manage a huge volume increase in data storage requirements (5+ years) due to extensive Record keeping requirements
  • Perform Realtime Surveillance & Monitoring of data – Once data is collected,  normalized & segmented, it will need to support realtime monitoring of data (around 5 seconds) to ensure that every trade can be tracked through it’s lifecycle. Detecting patterns that could perform surveillance for market abuse and monitor for best execution are key.
  • Business Rules  – Core logic that deals with identifying some of the above trade patterns are created using business rules. Business Rules have been covered in various areas in the blog but they primarily work based on an IF..THEN..ELSE construct.
  • Machine Learning & Predictive Analytics – A variety of supervised ad unsupervised learning approaches can be used to perform extensive Behavioral modeling & Segmentation to discover transactions behavior with a view to identifying behavioral patterns of traders & any outlier behaviors that connote potential regulatory violations.
  • A Single View of an Institutional Client- From the firm’s standpoint, it would be very useful to have a single view capability for clients that shows all of their positions across multiple desks, risk position, KYC score etc.

 The Design Ramifications of MiFiD II and MAR..

The below post captures the design of a market surveillance system to a good degree of detail. I had originally proposed it in the context of Dodd Frank CAT (Consolidated Audit Trail) Reporting in the US but we will extend these core ideas to MiFiD II and MAR as well. The link is reproduced below for review.

Design & Architecture of a Next Gen Market Surveillance System..(2/2)

Architecture of a Market Surveillance System..

The ability perform deep & multi level analysis of trade activity implies the capability of not only storing heterogeneous data for years in one place as well as the ability to perform forensic analytics (Rules & Machine Learning) in place at very low latency. Querying functionality ranging from interactive (SQL like) needs to be supported as well as an ability to perform deep forensics on the data via Data Science. Further, the ability to perform quick & effective investigation of suspicious trader behavior also requires compliance teams to access and visualize patterns of trade, drill into behavior to identify potential compliance violations. A Big Data platform is ideal for these complete range of requirements.

market_surveillance_system_v1

                       Design and Architecture of a Market Surveillance System for MiFiD II and MAR

The most important technical features for such a system are –

  1. Support end to end monitoring across a variety of financial instruments across multiple venues of trading. Support a wide variety of analytics that enable the discovery of interrelationships between customers, traders & trades as the next major advance in surveillance technology. HDFS is the ideal storage repository of this data.
  2. Provide a platform that can ingest from tens of millions to billions of market events (spanning a range of financial instruments – Equities, Bonds, Forex, Commodities and Derivatives etc) on a daily basis from thousands of institutional market participants. Data can be ingested using a range of tools – Sqoop, Kafka, Flume, API etc
  3. The ability to add new business rules (via either a business rules engine and/or a model based system that supports machine learning) is a key requirement. As we can see from the above, market manipulation is an activity that seems to constantly push the boundaries in new and unforseen ways. This can be met using open source languages like Python and R. Multifaceted projects such as Apache Spark allow users to perform exploratory data analysis (EDA), data science based analysis using language bindings with Python & R etc for a range of investigate usecases.
  4. Provide advanced visualization techniques thus helping Compliance and Surveillance officers manage the information overload.
  5. The ability to perform deep cross-market analysis i.e. to be able to look at financial instruments & securities trading on multiple geographies and exchanges 
  6. The ability to create views and correlate data that are both wide and deep. A wide view is one that helps look at related securities across multiple venues; a deep view will look for a range of illegal behaviors that threaten market integrity such as market manipulation, insider trading, watch/restricted list trading and unusual pricing.
  7. The ability to provide in-memory caches of data  for rapid pre-trade & post tradecompliance checks.
  8. Ability to create prebuilt analytical models and algorithms that pertain to trading strategy (pre- trade models –. e.g. best execution and analysis). The most popular way to link R and Hadoop is to use HDFS as the long-term store for all data, and use MapReduce jobs (potentially submitted from Hive or Pig) to encode, enrich, and sample data sets from HDFS into R.
  9. Provide Data Scientists and Quants with development interfaces using tools like SAS and R.
  10. The results of the processing and queries need to be exported in various data formats, a simple CSV/txt format or more optimized binary formats, JSON formats, or even into custom formats.  The results will be in the form of standard relational DB data types (e.g. String, Date, Numeric, Boolean).
  11. Based on back testing and simulation, analysts should be able to tweak the model and also allow subscribers (typically compliance personnel) of the platform to customize their execution models.
  12. A wide range of Analytical tools need to be integrated that allow the best dashboards and visualizations. This can be supported by platforms like Tableau, Qlikview and SAS.
  13. An intelligent surveillance system needs to store trade data, reference data, order data, and market data, as well as all of the relevant communication from a range of disparate systems, both internally and externally, and then match these things appropriately. The matching engine can be created using languages supported in Hadoop – Java, Scale, Python & R etc.
  14. Provide for multiple layers of detection capabilities starting with a) configuring business rules (that describe a trading pattern) as well as b) dynamic capabilities based on machine learning models (typically thought of as being more predictive). Such a system can also parallelize execution at scale to be able to meet demanding latency requirements for a market surveillance platform.

References..

[1] http://europa.eu/rapid/press-release_IP-16-265_en.htm
[2] http://europa.eu/rapid/press-release_MEMO-13-774_en.htm

How Big Data & Predictive Analytics transform AML Compliance in Banking & Payments..(2/2)

The first blog in this two part series (Deter Financial Crime by Creating an effective AML Program) described how Money Laundering (ML) activities employed by nefarious actors (e.g drug cartels, corrupt public figures & terrorist organizations) have gotten more sophisticated over the years. Global and Regional Banks are falling short of their compliance goals despite huge technology and process investments. Banks that fail to maintain effective compliance are typically fined hundreds of millions of dollars. In this second & final post, we will examine why Big Data Analytics as a second generation effort can become critical to efforts to shut down the flow of illicit funds across the globe thus ensuring financial organizations are compliant with efforts to reduce money laundering.

Where current enterprisewide AML programs fall short..

As discussed in various posts and in the first blog in the series (below), the Money Laundering (ML) rings of today are highly sophisticated in their understanding of the business specifics across the domains of Banking  – Capital Markets, Retail & Commercial banking. They are also very well versed in the complex rules that govern global trade finance.

Deter Financial Crime by Creating an Effective Anti Money Laundering (AML) Program…(1/2)

Further, the more complex and geographically diverse a financial institution is, the higher it’s risk of AML (Anti Money Laundering) compliance violations. Other factors such as an enormous volume of transactions across multiple distribution channels, across geographies between thousands of counter-parties always increases money laundering risk.

Thus, current AML programs fall short in five specific areas –

  1. Manual Data Collection & Risk Scoring – Bank’s response to AML statutes has been to bring in more staff typically in hundreds at large banks. These staff perform rote but key processes in AML such as Customer Due Diligence (CDD) and Know Your Customer (KYC).  These staff extensively scour external sources like Lexis Nexis, Thomson Reuters, D&B etc to manually scoring of risky client entities often pairing these with internal bank data. They also use AML watch-lists to perform this process of verifying individuals and business customers so that AML Case Managers can review it before filing Suspicious Activity Reports (SAR). On an average, about 50% of the cost of AML programs is incurred in terms of the large headcount requirements. At large Global Banks where the number of accounts are more 100 million customers the data volumes can get real big real quick causing all kinds of headaches for AML programs from a data aggregation, storage, processing and accuracy standpoint. There is a crying need to automate AML programs end to end to not only perform accurate risk scoring but also to keep costs down.
  2. Social Graph Analysis in areas such as Trade finance helps model the complex transactions occurring between thousands of entities. Each of these entities may have a complex holding structure with accounts that have been created using forged documents. Most fraud also happens in networks of fraud. An inability to dynamically understand the topology of the financial relationships among thousands of entities implies that AML programs need to develop graph based analysis capabilities .
  3. AML programs extensively deploy rule based systems or Transaction Monitoring Systems (TMS) which allow an expert system based approach to setup new rules. These rules span areas like monetary thresholds, specific patterns that connote money laundering & also business scenarios that may violate these patterns. However, fraudster rings now learn (or know) these rules quickly & change their fraudulent methods constantly to avoid detection. Thus there is a significant need to reduce a high degree of dependence on traditional TMS – which are slow to adapt to the dynamic nature of money laundering.
  4. The need to perform extensive Behavioral modeling & Customer Segmentation to discover transactions behavior with a view to identifying behavioral patterns of entities & outlier behaviors that connote potential laundering.
  5. Real time transaction monitoring in areas like Payment Cards presents unique challenges where money laundering is hidden within mountains of transaction data. Every piece of data produced as a result of bank operations needs to be commingled with historical data sets (for customers under suspicion) spanning years in making a judgment call about filing a SAR (Suspicious Activity Report).

How Big Data & Predictive Analytics can help across all these areas..

aml_predictiveanalytics

  1. The first area where Big Data & Predictive Analytics have a massive impact is around Due Diligence data of KYC (Know Your Customer) data. All of the above discussed data scraping from various sources can be easily automated by using tools in a Big Data stack to ingest information automatically. This is done by sending requests to data providers (the exact same ones that Banking institutions are currently using) via an API. Once this data is obtained, they can use real time processing tools (such as Apache Storm and Apache Spark) to apply sophisticated algorithms to that collected data to transform that data to calculate a Risk Score or Rating. In Trade Finance, Text Analytics can be used to process a range of documents like invoices, bills of lading, certificates of shipping etc to enable Banks to inspect a complex process across hundreds of entities operating across countries.  This approach enables Banks to process massive amounts of diverse data in quick time (even seconds) to synthesize it to accurate risk scores. Implementing Big Data in this very important workstream can help increase efficiency and reduce costs.
  2. The second area where Big Data shines at is in the space of helping create a Single View of a Customer as depicted below. This is made possible by doing advanced entity matching with the establishment and adoption of a lightweight entity ID service. This service will consist of entity assignment and batch reconciliation. The goal here is to get each business system to propagate the Entity ID back into their Core Banking, loan and payment systems, then transaction data will flow into the lake with this ID attached providing a way to do Customer 360.single-view-of-the-customer
  3. To be clear, we are advocating for a mix of both business rules and Data Science. Machine Learning is recommended as enables a range of business analytics across AML programs overcoming the limitations of a TMS. The first usecase is around Data Science for  – which is – Give me all transactions in one place, give me all the Case Mgmt files in one place, give me all of the customer data in one place and give me all External data (TBD) in one place. And the reason I want all of this is to perform Exploratory, hypothesis Data Science with the goal being to uncover areas of risk that one possibly missed out on before, find out areas that were not as risky as they thought were before so the risk score can be lowered and really constantly finding out the real Risk profile that your institution bears. E.g. Downgrading investment in your Trade financing as you are find a lot of Scrap Metal based fraudulent transactions.
  4. The other important value driver in deploying Data Science is to perform Advanced Transaction Monitoring Intelligence.  The core idea is to get years worth of Banking data in one location (the datalake) & then applying  unsupervised learning to glean patterns in those transactions. The goal is then to identify profiles of actors with the intent of feeding it into downstream surveillance & TM systems. This knowledge can then be used to –
  • Constantly learn transaction behavior for similar customers is very important in detecting laundering in areas like payment cards. It is very common to have retail businesses setup with the sole purpose of laundering money.
  • Discover transaction activity of trade finance customers with similar traits (types of businesses, nature of transfers, areas of operations etc.)
  • Segment customers by similar trasnaction behaviors
  • Understand common money laundering typologies and identify specific risks from a temporal and spatial/geographic standpoint
  • Improve and lear correlations between alert accuracy and suspicious activity reports (SAR) filings
  • Keep the noise level down by weeding out false positives

Benefits of a forward looking approach..  

We believe that we have a fresh approach that can help Banks with the following value drivers & metrics –

  • Detect AML violations on a proactive basis thus reducing the probability of massive fines
  • Save on staffing expenses for Customer Due Diligence (CDD)
  • Increase accurate production of suspicious activity reports (SAR)
  • Decrease the percent of corporate customers with AML-related account closures in the past year by customer risk level and reason – thus reducing loss of revenue
  • Decrease the overall KYC profile update backlog across geographies
  • Help create Customer 360 views that can help accelerate CLV (Customer Lifetime Value) as well as Customer Segmentation from a cross-sell/up-sell perspective

Big Data shines in all the above areas..

Conclusion…

The AML landscape will rapidly change over the next few years to accommodate the business requirements highlighted above. Regulatory authorities should also lead the way in adopting a Hadoop/ ML/Predictive Analytics based approach over the next few years. There is no other way to do tackle large & medium AML programs in a lower cost and highly automated manner.

Design and Architecture of A Robo-Advisor Platform..(3/3)

This three part series explores the automated investment management or the “Robo-advisor” (RA) movement. The first post in this series @- http://www.vamsitalkstech.com/?p=2329 – discussed how Wealth Management has been an area largely untouched by automation as far as the front office is concerned. As a result, automated investment vehicles have largely begun changing that trend and they helping create a variety of business models in the industry esp those catering to the Millenial Mass Affluent Segment. The second post @- http://www.vamsitalkstech.com/?p=2418  focused on the overall business model & main functions of a Robo-Advisor (RA). This third and final post covers a generic technology architecture for a RA platform.

Business Requirements for a Robo-Advisor (RA) Platform…

Some of the key business requirements of a RA platform that confer it advantages as compared to the manual/human driven style of investing are:

  • Collect Individual Client Data – RA Platforms need to offer a high degree of customization from the standpoint of an individual investor. This means an ability to provide a preferably mobile and web interface to capture detailed customer financial background, existing investments as well as any historical data regarding customer segments etc.
  • Client Segmentation – Clients are to be segmented  across granular segments as opposed to the traditional asset based methodology (e.g mass affluent, high net worth, ultra high net worth etc).
  • Algorithm Based Investment Allocation – Once the client data is collected,  normalized & segmented –  a variety of algorithms are applied to the data to classify the client’s overall risk profile and an investment portfolio is allocated based on those requirements. Appropriate securities are purchased as we will discuss in the below sections.
  • Portfolio Rebalancing  – The client’s portfolio is rebalanced appropriately depending on life event changes and market movements.
  • Tax Loss Harvesting – Tax-loss harvesting is the mechanism of selling securities that have a loss associated with them. By doing so or by taking  a loss, the idea is that that client can offset taxes on both gains and income. The sold securities are replaced by similar securities by the RA platform thus maintaining the optimal investment mix.
  • A Single View of a Client’s Financial History- From the WM firm’s standpoint, it would be very useful to have a single view capability for a RA client that shows all of their accounts, interactions & preferences in one view.

User Interface Requirements for a Robo-Advisor (RA) Platform…

Once a customer logs in using any of the digital channels supported (e.g. Mobile, eBanking, Phone etc)  – they are presented with a single view of all their accounts. This view has a few critical areas – Summary View (showing an aggregated view of their financial picture), the Transfer View (allowing one to transfer funds across accounts with other providers).

The Summary View lists the below

  • Demographic info: Customer name, address, age
  • Relationships: customer rating influence, connections, associations across client groups
  • Current activity: financial products, account interactions, any burning customer issues, payments missed etc
  • Customer Journey Graph: which products or services they are associated with since the time they became a customer first etc,

Depending on the clients risk tolerance and investment horizon, the weighted allocation of investments across these categories will vary. To illustrate this, a Model Portfolio and an example are shown below.

Algorithms for a Robo-Advisor (RA) Platform…

There are a variety of algorithmic approaches that could be taken to building out an RA platform. However the common feature of all of these is to –

  • Leverage data science & statistical modeling to automatically allocate client wealth across different asset classes (such as domestic/foreign stocks, bonds & real estate related securities) to automatically rebalance portfolio positions based on changing market conditions or client preferences. These investment decisions are also made based on detailed behavioral understanding of a client’s financial journey metrics – Age, Risk Appetite & other related information. 
  • A mixture of different algorithms can be used such as Modern Portfolio Theory (MPT), Capital Asset Pricing Model (CAPM), the Black Litterman Model, the Fama-French etc. These are used to allocate assets as well as to adjust positions based on market movements and conditions.
  • RA platforms also provide 24×7 tracking of market movements to use that to track rebalancing decisions from not just a portfolio standpoint but also from a taxation standpoint.

Model Portfolios…

  1. Equity  

             A) US Domestic Stock – Large Cap, Medium Cap , Small Cap, Dividend Stocks 

             B) Foreign Stock – Emerging Markets, Developed Markets

       2. Fixed Income

             A) Developed Market Bonds 

             B) US Bonds

             C) International Bonds

             D) Emerging Markets Bonds

      3. Other 

             A) Real Estate  

             B) Currencies

             C) Gold and Precious Metals

             D) Commodities

       4. Cash

Sample Portfolios – for an aggressive investor…

  1. Equity  – 85%

             A) US Domestic Stock (50%) – Large Cap – 30%, Medium Cap – 10% , Small Cap – 10%, Dividend Stocks – 0%

             B) Foreign Stock – (35%) –  Emerging Markets – 18%, Developed Markets – 17% 

       2. Fixed Income – 5%

             A) Developed Market Bonds  – 2%

             B) US Bonds – 1%

             C) International Bonds – 1%

             D) Emerging Markets Bonds – 1%

      3. Other – 5%

             A) Real Estate  – 3%

             B) Currencies – 0%

             C) Gold and Precious Metals – 0%

             D) Commodities – 2%

       4. Cash – 5%

Technology Requirements for a Robo-Advisor (RA) Platform…

An intelligent RA platform has a few core technology requirements (based on the above business requirements).

  1. A Single Data Repository – A shared data repository called a Data Lake is created, that can capture every bit of client data (explained in more detail below) as well as external data. The RA datalake provides more visibility into all data to a variety of different stakeholders. Wealth Advisors access processed data to view client accounts etc. Clients can access their own detailed positions,account balances etc. The Risk group accesses this shared data lake to processes more position, execution and balance data.  Data Scientists (or Quants) who develop models for the RA platform also access this data to perform analysis on fresh data (from the current workday) or on historical data. All historical data is available for at least five years—much longer than before. Moreover, the Hadoop platform enables ingest of data across a range of systems despite their having disparate data definitions and infrastructures. All the data that pertains to trade decisions and lifecycle needs to be made resident in a general enterprise storage pool that is run on the HDFS (Hadoop Distributed Filesystem) or similar Cloud based filesystem. This repository is augmented by incremental feeds with intra-day trading activity data that will be streamed in using technologies like Sqoop, Kafka and Storm.
  2. Customer Data Collection – Existing Financial Data across the below categories is collected & aggregated into the data lake. This data ranges from Customer Data, Reference Data, Market Data & other Client communications. All of this data, can be ingested using a API or pulled into the lake from a relational system using connectors supplied in the RA Data Platform. Examples of data collected include – Customer’s existing Brokerage accounts, Customer’s Savings Accounts, Behavioral Finance Suveys and Questionnaires etc etc. The RA Data Lake stores all internal & external data.
  3. Algorithms – The core of the RA Platform are data science algos. Whatever algorithms are used – a few critical workflows are common to them. The first is Asset Allocation is to take the customers input in the “ADVICE” tab for each type of account and to tailor the portfolio based on the input. The others include Portfolio Rebalancing and Tax Loss Harvesting.
  4. The RA platform should be able to store market data across years both from a macro and from an individual portfolio standpoint so that several key risk measures such as volatility (e.g. position risk, any residual risk and market risk), Beta, and R-Squared – can be calculated at multiple levels.  This for individual securities, a specified index, and for the client portfolio as a whole.

roboadvisor_design_arch

                      Illustration: Architecture of a Robo-Advisor (RA) Platform 

The overall logical flow of data in the system –

  • Information sources are depicted at the left. These encompass a variety of institutional, system and human actors potentially sending thousands of real time messages per hour or by sending over batch feeds.
  • A highly scalable messaging system to help bring these feeds into the RA Platform architecture as well as normalize them and send them in for further processing. Apache Kafka is a good choice for this tier. Realtime data is published by a range of systems over Kafka queues. Each of the transactions could potentially include 100s of attributes that can be analyzed in real time to detect business patterns.  We leverage Kafka integration with Apache Storm to read one value at a time and perform some kind of storage like persist the data into a HBase cluster.In a modern data architecture built on Apache Hadoop, Kafka ( a fast, scalable and durable message broker) works in combination with Storm, HBase (and Spark) for real-time analysis and rendering of streaming data. 
  • Trade data is thus streamed into the platform (on a T+1 basis), which thus ingests, collects, transforms and analyzes core information in real time. The analysis can be both simple and complex event processing & based on pre-existing rules that can be defined in a rules engine, which is invoked with Apache Storm. A Complex Event Processing (CEP) tier can process these feeds at scale to understand relationships among them; where the relationships among these events are defined by business owners in a non technical or by developers in a technical language. Apache Storm integrates with Kafka to process incoming data. 
  • For Real time or Batch Analytics, Apache HBase provides near real-time, random read and write access to tables (or ‘maps’) storing billions of rows and millions of columns. In this case once we store this rapidly and continuously growing dataset from the information producers, we are able  to do perform super fast lookup for analytics irrespective of the data size.
  • Data that has analytic relevance and needs to be kept for offline or batch processing can be stored using the Hadoop Distributed Filesystem (HDFS) or an equivalent filesystem such as Amazon S3 or EMC Isilon or Red Hat Gluster. The idea to deploy Hadoop oriented workloads (MapReduce, or, Machine Learning) directly on the data layer. This is done to perform analytics on small, medium or massive data volumes over a period of time. Historical data can be fed into Machine Learning models created above and commingled with streaming data as discussed in step 1.
  • Horizontal scale-out (read Cloud based IaaS) is preferred as a deployment approach as this helps the architecture scale linearly as the loads placed on the system increase over time. This approach enables the Market Surveillance engine to distribute the load dynamically across a cluster of cloud based servers based on trade data volumes.
  • It is recommended to take an incremental approach to building the RA platform, once all data resides in a general enterprise storage pool and makes the data accessible to many analytical workloads including Trade Surveillance, Risk, Compliance, etc. A shared data repository across multiple lines of business provides more visibility into all intra-day trading activities. Data can be also fed into downstream systems in a seamless manner using technologies like SQOOP, Kafka and Storm. The results of the processing and queries can be exported in various data formats, a simple CSV/txt format or more optimized binary formats, json formats, or you can plug in custom SERDE for custom formats. Additionally, with HIVE or HBASE, data within HDFS can be queried via standard SQL using JDBC or ODBC. The results will be in the form of standard relational DB data types (e.g. String, Date, Numeric, Boolean). Finally, REST APIs in HDP natively support both JSON and XML output by default.
  • Operational data across a bunch of asset classes, risk types and geographies is thus available to investment analysts during the entire trading window when markets are still open, enabling them to reduce risk of that day’s trading activities. The specific advantages to this approach are two-fold: Existing architectures typically are only able to hold a limited set of asset classes within a given system. This means that the data is only assembled for risk processing at the end of the day. In addition, historical data is often not available in sufficient detail. Hadoop accelerates a firm’s speed-to-analytics and also extends its data retention timeline
  • Apache Atlas is used to provide Data Governance capabilities in the platform that use both prescriptive and forensic models, which are enriched by a given businesses data taxonomy and metadata.  This allows for tagging of trade data  between the different businesses data views, which is a key requirement for good data governance and reporting. Atlas also provides audit trail management as data is processed in a pipeline in the lake
  • Another important capability that Big Data/Hadoop can provide is the establishment and adoption of a Lightweight Entity ID service – which aids dramatically in the holistic viewing & audit tracking of trades. The service will consist of entity assignment for both institutional and individual traders. The goal here is to get each target institution to propagate the Entity ID back into their trade booking and execution systems, then transaction data will flow into the lake with this ID attached providing a way to do Client 360.
  • Output data elements can be written out to HDFS, and managed by HBase. From here, reports and visualizations can easily be constructed. One can optionally layer in search and/or workflow engines to present the right data to the right business user at the right time.  

Conclusion…

As one can see clearly, though automated investing methods are still in early stages of maturity – they hold out a tremendous amount of promise. As they are unmistakably the next big trend in the WM industry industry players should begin developing such capabilities.

The Three Core Competencies of Digital – Cloud, Big Data & Intelligent Middleware

Ultimately, the cloud is the latest example of Schumpeterian creative destruction: creating wealth for those who exploit it; and leading to the demise of those that don’t.” – Joe Weiman author of Cloudonomics: The Business Value of Cloud Computing

trifacta_digital

The  Cloud As a Venue for Digital Workloads…

As 2016 draws to a close, it can safely be said that no industry leader questions the existence of the new Digital Economy and the fact that every firm out there needs to create a digital strategy. Myriad organizations are taking serious business steps to making their platforms highly customer-centric via a renewed operational metrics focus. They are also working on creating new business models using their Analytics investments. Examples of these verticals include Banking, Insurance, Telecom, Healthcare, Energy etc.

As a general trend, the Digital Economy brings immense opportunities while exposing firms to risks as well. Customers now demanding highly contextual products, services and experiences – all accessible via an easy API (Application Programming Interfaces).

Big Data Analytics (BDA) software revenues will grow from nearly $122B in 2015 to more than $187B in 2019 – according to Forbes [1].  At the same time, it is clear that exploding data generation across the global economy has become a clear & present business phenomenon. Data volumes are rapidly expanding across industries. However, while the production of data itself that has increased but it is also driving the need for organizations to derive business value from it. As IT leaders know well, digital capabilities need low cost yet massively scalable & agile information delivery platforms – which only Cloud Computing can provide.

For a more detailed technical overview- please visit below link.

http://www.vamsitalkstech.com/?p=1833

Big Data & Big Data Analytics drive consumer interactions.. 

The onset of Digital Architectures in enterprise businesses implies the ability to drive continuous online interactions with global consumers/customers/clients or patients. The goal is not just provide engaging visualization but also to personalize services clients care about across multiple channels of interaction. The only way to attain digital success is to understand your customers at a micro level while constantly making strategic decisions on your offerings to the market. Big Data has become the catalyst in this massive disruption as it can help business in any vertical solve their need to understand their customers better & perceive trends before the competition does. Big Data thus provides the foundational  platform for successful business platforms.

The three key areas where Big Data & Cloud Computing intersect are – 

  • Data Science and Exploration
  • ETL, Data Backups and Data Preparation
  • Analytics and Reporting

Big Data drives business usecases in Digital in myriad ways – key examples include  –  

  1. Obtaining a realtime Single View of an entity (typically a customer across multiple channels, product silos & geographies)
  2. Customer Segmentation by helping businesses understand their customers down to the individual micro level as well as at a segment level
  3. Customer sentiment analysis by combining internal organizational data, clickstream data, sentiment analysis with structured sales history to provide a clear view into consumer behavior.
  4. Product Recommendation engines which provide compelling personal product recommendations by mining realtime consumer sentiment, product affinity information with historical data.
  5. Market Basket Analysis, observing consumer purchase history and enriching this data with social media, web activity, and community sentiment regarding past purchase and future buying trends.

Further, Digital implies the need for sophisticated, multifactor business analytics that need to be performed in near real time on gigantic data volumes. The only deployment paradigm capable of handling such needs is Cloud Computing – whether public or private. Cloud was initially touted as a platform to rapidly provision compute resources. Now with the advent of Digital technologies, the Cloud & Big Data will combine to process & store all this information.  According to the IDC , by 2020 spending on Cloud based Big Data Analytics will outpace on-premise by a factor of 4.5. [2]

Intelligent Middleware provides Digital Agility.. 

Digital Applications are applications modular, flexible and responsive to a variety of access methods – mobile & non mobile. These applications are also highly process driven and support the highest degree of automation. The need of the hour is to provide enterprise architecture capabilities around designing flexible digital platforms that are built around efficient use of data, speed, agility and a service oriented architecture. The choice of open source is key as it allows for a modular and flexible architecture that can be modified and adopted in a phased manner – as you will shortly see.

The intention in adopting a SOA (or even a microservices) architecture for Digital capabilities is to allow lines of business an ability to incrementally plug in lightweight business services like customer on-boarding, electronic patient records, performance measurement, trade surveillance, risk analytics, claims management etc.

Intelligent Middleware adds significant value in six specific areas –

  1. Supports a high degree of Process Automation & Orchestration thus enabling the rapid conversion of paper based business processes to a true digital form in a manner that lends itself to continuous improvement & optimization
  2. Business Rules help by adding a high degree of business flexibility & responsiveness
  3. Native Mobile Applications  enables platforms to support a range of devices & consumer behavior across those front ends
  4. Platforms As a Service engines which enable rapid application & business capability development across a range of runtimes and container paradigms
  5. Business Process Integration engines which enable rapid application & business capability development
  6. Middleware brings the notion of DevOps into the equation. Digital projects bring several technology & culture challenges which can be solved by a greater degree of collaboration, continuous development cycles & new toolchains without giving up proven integration with existing (or legacy)systems.

Intelligent Middleware not only enables Automation & Orchestration but also provides an assembly environment to string different (micro)services together. Finally, it also enables less technical analysts to drive application lifecycle as much as possible.

Further, Digital business projects call out for mobile native applications – which a forward looking middleware stack will support.Middleware is a key component for driving innovation and improving operational efficiency.

Five Key Business Drivers for combining Big Data, Intelligent Middleware & the Cloud…

The key benefits of combining the above paradigms to create new Digital Applications are –

  • Enable Elastic Scalability Across the Digital Stack
    Cloud computing can handle the storage and processing of any amount of data & any kind of data.This calls for the collection & curation of data from dynamic and highly distributed sources such as consumer transactions, B2B interactions, machines such as ATM’s & geo location devices, click streams, social media feeds, server & application log files and multimedia content such as videos etc. It needs to be noted that data volumes here consist of multi-varied formats, differing schemas, transport protocols and velocities. Cloud computing provides the underlying elastic foundation to analyze these datasets.
  • Support Polyglot Development, Data Science & Visualization
    Cloud technologies are polyglot in nature. Developers can choose from a range of programming languages (Java, Python, R, Scala and C# etc) and development frameworks (such as Spark and Storm). Cloud offerings also enable data visualization using a range of tools from Excel to BI Platforms.
  • Reduce Time to Market for Digital Business Capabilities
    Enterprises can avoid time consuming installation, setup & other upfront procedures. consuming  can deploy Hadoop in the cloud without buying new hardware or incurring other up-front costs. On the same vein, even big data analytics should be able to support self service across the lifecycle – from data acquisition, preparation, analysis & visualization.
  • Support a multitude of Deployment Options – Private/Public/Hybrid Cloud 
    A range of scenarios for product development, testing, deployment, backup or cloudbursting are efficiently supported in pursuit of cost & flexibility goals.
  • Fill the Talent Gap
    Open Source technology is the common thread across Cloud, Big Data and Middleware. The hope is that the ubiquity of open source will serve as a critical level in enabling the filling up of the IT-Business skills scarcity gap.

As opposed to building standalone or one-off business applications, a ‘Digital Platform Mindset’ is a more holistic approach capable of producing higher rates of adoption & thus revenues. Platforms abound in the web-scale world at shops like Apple, Facebook & Google etc. Digital Applications are constructed like lego blocks  and they reuse customer & interaction data to drive cross sell and up sell among different product lines. The key components here are to ensure that one starts off with products with high customer attachment & retention. While increasing brand value, it is key to ensure that customers & partners can also collaborate in the improvements in the various applications hosted on top of the platform.

References

[1] Forbes Roundup of Big Data Analytics (BDA) Report

http://www.forbes.com/sites/louiscolumbus/2016/08/20/roundup-of-analytics-big-data-bi-forecasts-and-market-estimates-2016/#b49033b49c5f

[2] IDC FutureScape: Worldwide Big Data and Analytics 2016 Predictions