Why Enterprises should build Platforms and not just Standalone Applications…

                                                    Image Credit – Shutterstock 

Introduction..

The natural tendency in the world of Corporate IT is to create applications in response to business challenges. For instance, take any large Bank or Insurer or Manufacturer – you will find thousands of packaged applications that aim to solve a range of challenges from departmental level issues to enterprise-wide business problems. Over years these have given rise to application and infrastructure sprawl.

The application mindset creates little business value over the long run while creating massive technology headaches. For instance, the rationalization of these applications over time becomes a massive challenge in and of itself. At times, IT does not even understand how relevant some of these applications are to business users, who are even using them and the benefits derived. Over the last 15 years, Silicon Valley players such as Apple, Google, and Facebook et al have begun illustrating the power of building platforms that connect a range of users to the businesses that serve them. As the Network Effects connected to using these platforms have grown exponentially, so have the users.

What Corporate IT & business need to learn to do is to move to a Platform mindset.

The Platform Strategy…

Amazon is the perfect example of how to conceive and execute a platform strategy over a couple of decades. It began life as a retailer in 1994 and over time morphed into other complementary offerings such as Marketplace, AWS, Prime Video, Payments etc. These platforms have led to an ever-increasing panoply of services, higher revenues, promoted more directed consumer interactions and higher network effects. Each platform generates its own revenue stream and is a large standalone corporation in its own right. However, the sum of these platforms is higher than the sum of the individual products and this has led to Amazon becoming the most valuable company in the world (as of late 2017).

So what are the key business benefits and drivers of a platform oriented model?

Driver #1 Platforms enable you to build business ecosystems

Platforms enable enterprise business to orient their core capabilities better and to be able to deliver on those. Once that is done to a high degree of success, partners and other ecosystem players can plug in their capabilities.  The functionality that the platform provides is the ability to inter The challenge most times is that large companies always seem to play catchup with business models of nimbler players. When they do this, they often choose an application based approach which does not enable them to take a holistic view of their enterprise and the business ecosystems around them. In the Platform approach, IT departments move to more of a service model while delivering agile platforms and technology architectures for business lines to develop products around.

E.g. Post the PSD2 regulation, innovators in the European Banking system will become a prime example of platform led business ecosystems.

Why the PSD2 will Spark Digital Innovation in European Banking and Payments….

Driver #2 Platforms enable you to rethink and better the customer experience thus driving new revenue streams

The primary appeal of a platform based architecture is the ability to drive cross-sell and upsell opportunities. This increases not the number of products adopted by a given customer but also (and ultimately) the total revenue per customer.

The below blog post discusses how Payment Providers are increasingly using advanced analytics on their business platforms to generate not only increased topline/sales growth but also to defend against fraud and anti-money laundering (AML).

Payment Providers – How Big Data Analytics Provides New Opportunities in 2017

Driver #3 Platforms enable you to experiment with business models (e.g. Data Monetization)

The next progressive driver in leveraging both internal and external data is to use it to drive new revenue streams in existing lines of business.  This is also termed Data Monetization. Data Monetization is the organizational ability to turn data into cost savings & revenues in existing lines of business and to create new revenue streams. This requires fusing both internal and external data to create new analytics and visualization.

The Tao of Data Monetization in Banking and Insurance & Strategies to Achieve the Same…

Driver #4 Platforms destroy business process silos

One of the chief reasons that hold back an enterprise ability to innovate is the presence of both business and data silos. This is directly a result of an Application based approach. When underlying business processes & data sources are both fragmented, communication between business teams moves over to other internal & informal mechanisms such as email, chat and phone calls etc. This is an overall recipe for delayed business decisions which are ultimately ineffective as they depend more on intuition than are backed by data. The Platforms approach drives the organization towards unification and rationalization of both the data and the business process that creates it thus leading to a unified and consistent view of both across the business.

Why Data Silos Are Your Biggest Source of Technical Debt..

Driver #5 Platforms move you to become a Real-time Enterprise

Enterprises that are platform oriented does more strategic things right than wrong. They constantly experiment with creating new and existing business capabilities with a view to making them appealing to a rapidly changing clientele. They refine these using constant feedback loops and create platforms comprised of cutting-edge technology stacks that dominate the competitive landscape. The Real-Time enterprise demands that workers at many levels ranging from the line of business managers to executives have fresh, high quality and actionable information on which they can base complex yet high-quality business decisions.

The Three Habits of Highly Effective Real Time Enterprises…

Conclusion..

A business and IT strategy built on platform approaches enable an organization to take on a much wider & richer variety of business challenges.  This enables an organization to achieve outcomes that were not really possible with the Application model.

Demystifying Digital – Reference Architecture for Single View of Customer / Customer 360..(3/3)

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).  This second post in the series discussed the concept of Customer Journey Mapping (CJM) – http://www.vamsitalkstech.com/?p=3099 . We discussed specific benefits from both a business & operational standpoint that are enabled by SVC & CJM. The third & final post will focus on a technical design & architecture needed to achieve both these capabilities.

Business Requirements for Single View of Customer & Customer Journey Mapping…

The following key business requirements need to be supported for three key personas- Customer, Marketing & Customer Service – from a SVC and CJM standpoint.

  1. Provide an Integrated Experience: A fully integrated omnichannel experience for both the customer and internal stakeholder (marketing, customer service, regulatory, managerial etc) roles. This means a few important elements – consistent information across all touchpoints, the right information to the right user at the right time, an ability to view the CJM graph with realtime metrics on Customer Lifetime Value (CLV) etc.
  2. Continuously Learning Customer Facing System: An ability for the customer facing portion of the architecture to learn constantly to fine-tune it’s understanding of the customers real time picture. This includes an ability to understand the customer’s journey.
  3. Contextual yet Seamless Movement across Channels: The ability for customers to transition seamlessly from one channel to the other while conducting business transactions.
  4. Ability to introduce Marketing Programs for existing Customers: An ability to introduce marketing and customer retention and other loyalty programs in a dynamic manner. These include and ability to combine historical data with real time data about customer interactions and other responses like clickstreams – to provide product recommendations and real time offers.
  5. Customer Acquisition: An ability to perform low cost customer acquisition and to be able to run customized offers for segments of customers from a back-office standpoint.

Key Gaps in existing Single View (SVC) Architectures ..

It needs to be kept in mind that every organization is different from an IT legacy investment and operational standpoint. As such, a “one-size-fits-all” architecture is impossible to create. However, highlighted below are some common key data and application architecture gaps that I have observed from a data standpoint while driving to a SVC (Single View of Customer) with multiple leading enterprises.

  1. The lack of a single, unique & global customer identifier – The need to create a single universal customer identifier (based on various departmental or line of business identifiers) and to use it as a primary key in the customer master list
  2. Once the identifier is created in either the source system or in the datalake, organizations need to figure out a way to cascade that identifier into the Book of Record systems (CRM systems, webapps and ERP systems) so that the architecture can begin knitting together a single view of the customer. This may also involve periodically go out across the BOR systems, link all the customers data and pull the data into the lake;
  3. Many companies deal with multiple customer on-boarding systems. At some point, these  on-boarding processes need  to be centralized. For instance in Banking esp In Capital markets, customer on-boarding done in six or seven different areas; all of these ideally need to be consolidated into one.
  4. Graph Data Semantics – Once created, the Master Customer identifier should be mapped to all the other identifiers lines of business use to uniquely identify their customer; the ability to use simple or more complex matching techniques (Rule based matching, machine learning based matching & search based matching) is highly called for.
  5. MDM (Master Data Management) systems have traditionally automated some of this process by creating & owning that unique customer identifier. However Big Data capabilities help by linking that unique customer identifier to all the other ways the customer may be mapped across the organization. To this end,  data may be exported into an MDM system backed by a traditional RDBMS; or; the computation of the unique identifier can be done in a data lake and then exported into an MDM system.

Let us discuss the generic design of the architecture (depicted above) with a focus on the following subsystems –

A Reference Architecture for Single View of Customer/ Customer 360
  1. At the very top, different channels depict with different touch points In today’s connected world, the customer experience spans multiple different touch points throughout the customer lifecycle. A customer should be able to move through multiple different touch points during the buying process. Customers should be able to start, pause transactions (e.g. An Auto Loan application) from one channel and restart/complete them from another.
  2. A Big Data enabled application architecture is chosen. This needs to account for two different data processing paradigms. The first is a realtime component. The architecture must be capable of handling events within a few milliseconds. The second is an ability to handle massive scale data analysis in a retrospective manner. Both these components are provided by a Hadoop stack. The real time component leverages – Apache NiFi, Apache HBase, HDFS, Kafka, Storm and Spark. The batch component leverages  HBase, Apache Titan, Apache Hive, Spark and MapReduce.
  3. The range of Book of Record and external systems send data into the central datalake. Both realtime and batch components highlighted above send the data into the lake. The design of the lake itself will be covered in more detail in the below section.
  4. Starting from the upper-left side, we have the Book of Record Systems sending across transactions. These are ingested into the lake using any of the different ingestion frameworks provided in Hadoop. E.g. Flume, Kafka, Sqoop, HDFS API for batch transfers etc.  The ingestion layer depicted is based on Apache NiFi and is used to load data into the data lake.  Functionally, it is made up of real time data loaders and end of day data loaders. The real time loaders load the data as it is created in the feeder systems, the EOD data loaders will adjust the data end of the day based on the P&L sign off and the end of day close processes.  The main data feeds for the system will be from the book of record transaction systems (BORTS) but there may also be multiple data feeds from transaction data providers and customer information systems.
  5. The UI Framework is standardized across all kinds of clients. For instance this could be an HTML 5 GUI Framework that contains reusable widgets that can be used for mobile and browser based applications.  The framework also need to deal with common mobile issues such as bandwidth and be able to automatically throttle the data back where bandwidth is limited.It also needs to facilitate the construction of large user defined pivot tables for ad hoc reporting. It utilizes UI framework components for its GUI construction and communicates with the application server via the web services layer.
  6. API access is also provided by Web Services for partner applications to leverage: This is the application layer that that provides a set of RESTful web services that control the GUI behavior and that control access to the persistent data and the data that is cached on the data fabric.
  7. The transactions are taken through the pipeline of enrichment and the profiles of customers are stored in HBase. .
  8. The core data processing platform is then based on a datalake pattern which has been covered in this blog before. It includes the following pattern of processing.
    1. Data is ingested real time into a HBase database (which uses HDFS as the underlying storage layer). Tables are designed in HBase to store the profile of a trade and it’s lifecycle.
    2. Producers are authenticated at the point of ingest.
    3. Once the data has been ingested into HDFS, it is taken through a pipeline of processing (L0 to L3) as depicted in the below blogpost.

      http://www.vamsitalkstech.com/?p=667

  9. Speed Layer: The computational grid that makes up the Speed layer can be a distributed in memory data fabric like Infinispan or GemFire, or a computation process can be overlaid directly onto a stateful data fabric technology like Spark or GemFire. The choice is dependent of the language choices that have been made in building the other key analytic libraries. If multiple language bindings are required (e.g. C# & Java) then the data fabric will typically be a different product than the Grid.

Data Science for Customer 360

 Consider the following usecases that are all covered under Customer 360 –

  1. The ability to segment customers into categories based on granular data attributes
  2. Improve customer targeting for new promotions & increasing acquisition rate
  3. Increasing cross sell and upsell rates
  4. Understanding influencers among customer segments & helping these net promoters recommend products to other customers
  5. Performing market basket analysis of what products/services are typically purchased together
  6. Understanding customer risk profiles
  7. Creating realtime views of customer lifetime value (CLV)
  8. Reducing customer attrition

The obvious capability that underlies all of these is Data Science. Thus, Predictive Analytics is the key compelling paradigm that enables the buildout of the dynamic Customer 360.

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 realtime fraud in credit card transactions.” or “Perform certain algorithms based on the predictions”. In usecases 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. In the machine learning process, an entire spectrum of algorithms can be tried to solve such business problems.

A lot of times, business groups working on Customer 360 projects have a hard time explaining what they would like to see – both data and the visualization. In such cases, a prototype makes things way more easy 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, DB2, Mainframe, 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 Scientist working with the business needs to determine how much of this raw data is useful and how much of it needs to be massaged to create a Customer 360 view. Some of this data needs to be extrapolated to form the features using formulas – so that a model can be created. The models created often involve using languages such as R and Python.

Feature engineering takes in business features in the form of feature vectors and creates predictive features from them. The Data Scientist takes the raw 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.

The transformation phase involves writing code to be able to to join up like 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.

Models as a Service (MaaS) is the Data Science counterpart to Software as a Service.The MaaS takes in business variables (often hundreds of them as inputs) and provides as output business decisions/intelligence, measurements, visualizations that augment decision support systems.

Once these models are deployed and updated nightly based on their performance – the serving layer takes advantage of them to drive real time 360 decisioning.

To Sum Up…

In this short series we have discussed that customers and data about their history, preferences, patterns of behavior, aspirations etc are the most important corporate asset. Big Data technology and advances made in data storage, processing and analytics can help architect a dynamic Single View that can help maximize competitive advantage across every industry vertical.

Demystifying Digital – Why Customer 360 is the Foundational Digital Capability – ..(1/3)

The first post in this three part series on Digital Foundations introduces the concept of Customer 360 or Single View of Customer (SVC). We will discuss the need for & the definition of the SVC as part of the first step in any Digital Transformation endeavor. We will also discuss specific benefits from both a business & operational state that are enabled by SVC. The 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.
 
In an era of exploding organizational touch points, how many companies can truly claim that they know & understand their customers, their needs & evolving preferences deeply and from a realtime perspective?  
How many companies can claim to keep up as a customers product & service usage matures and keep them engaged by cross selling new offerings. How many can accurately predict future revenue from a customer based on their current understanding of their profile?
The answer is not at all encouraging.
Across industries like Banking, Insurance, Telecom & Manufacturing, the ability to get a unified view of the customer & their journey is at the heart of the the enterprise ability to promote relevant offerings & detect customer dissatisfaction. 
  • Currently most industry players are woeful at putting together this comprehensive Single View of their Customers (SVC). Due to operational silos, each department possess a siloed & limited view of the customer across multiple channels. These views are typically inconsistent, lack synchronization with other departments & miss a high amount of potential cross-sell and up-sell opportunities.
  • The Customer Journey problem has been an age old issue which has gotten exponentially more complicated over the last five years as the staggering rise of mobile technology and the Internet of Things (IoT) have vastly increased the number of enterprise touch points that customers are exposed to in terms of being able to discover & purchase new products/services. In an OmniChannel world, an increasing number of transactions are being conducted online. In verticals like Retail and Banking, the number of online transactions approaches an average of 40%. Adding to the problem, more and more consumers are posting product reviews and feedback online. Companies thus need to react in realtime to piece together the source of consumer dissatisfaction.
Another large component of customer outreach are Marketing analytics & the ability to run effective campaigns to recruit customers.

The most common questions that a lot of enterprises fail to answer accurately are –

  1. Is the Customer happy with their overall relationship experience?
  2. What mode of contact do they prefer? And at what time? Can Customers be better targeted at these channels at those preferred times?
  3. What is the overall Customer Lifetime Value (CLV) or how much profit we are able to generate from this customer over their total lifetime?
  4. By understanding CLV across populations, can we leverage that to increase spend on marketing & sales for products that are resulting in higher customer value?
  5. How do we increase cross sell and up-sell of products & services?
  6. Does this customer fall into a certain natural segment and if so, how can we acquire most customers like them?
  7. Can different channels (Online, Mobile, IVR & POS) be synchronized ? Can Customers begin a transaction in one channel and complete it in any of the others without having to resubmit their data?

The first element in Digital is the Customer Centricity & it must naturally follow that a 360 degree view is a huge aspect of that.

single-view-of-the-customer

                                       Illustration – Customer 360 view & its benefits

So what information is specifically contained in a Customer 360 –

The 360 degree view is a snapshot of the below types of data –

  • Customer’s Demographic information – Name, Address, Age etc
  • Length of the Customer-Enterprise relationship
  • Products and Services purchased overall
  • Preferred Channel & time of Contact
  • Marketing Campaigns the customer has responded to
  • Major Milestones in the Customers relationship
  • Ongoing activity – Open Orders, Deposits, Shipments, Customer Cases etc
  • Ongoing Customer Lifetime Value (CLV) Metrics and the Category of customer (Gold, Silver, Bronze etc)
  • Any Risk factors – Likelihood of Churn, Customer Mood Alert, Ongoing issues etc
  • Next Best Action for Customer

How Big Data technology can help..

Leveraging the ingestion and predictive capabilities of a Big Data based platform, banks can provide a user experience that rivals Facebook, Twitter or Google and provide a full picture of customer across all touch points.

Big Data enhances the Customer 360 capability in the following ways  –  

  1. Obtaining a realtime Single View of the Customer (typically a customer across multiple channels, product silos & geographies) across years of account history 
  2. Customer Segmentation by helping businesses understand customer segments down to the individual level as well as at a segment level
  3. Performing 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.

Customer 360 can help improve the following operational metrics of a Retailer or a Bank or a Telecom immensely.

  1. Cost to Income ratio; Customers Acquired per FTE; Sales and service FTE’s (as percentage of total FTE’s), New Accounts Per Sales FTE etc
  2.  Sales conversion rates across channels, Decreased customer attrition rates etc.
  3. Improved Net promotor scores (NPS), referral based sales etc

Customer 360 is thus basic digital capability every organization needs to offer their customers, partners & internal stakeholders. This implies a re-architecture of both data management and business processes automation.

The next post will discuss the second critical component of Digital Transformation – the Customer Journey.

Capital Markets Pivots to Big Data in 2016

Previous posts in this blog have discussed how Capital markets firms must create new business models and offer superior client relationships based on their vast data assets. Firms that can infuse a data driven culture in both existing & new areas of operation will enjoy superior returns and raise the bar for the rest of the industry in 2016 & beyond. 

Capital Markets are the face of the financial industry to the general public and generate a large percent of the GDP for the world economy. Despite all the negative press they have garnered since the financial crisis of 2008, capital markets perform an important social function in that they contribute heavily to economic growth and are the primary vehicle for household savings. Firms in this space allow corporations to raise capital using the underwriting process. However, it is not just corporations that benefit from such money raising activity – municipal, local and national governments do the same as well. Just that the overall mechanism differs – while business enterprises issue both equity and bonds, governments typically issue bonds. According to the Boston Consulting Group (BCG), the industry will grow to annual revenues of $661 billion in 2016 from $593 billion in 2015 – a healthy 12% increase. On the buy side, the asset base (AuM – Assets under Management) is expected to reach around $100 trillion by 2020 up from $74 trillion in 2014.[1]

Within large banks, the Capital Markets group and the Investment Banking Group perform very different functions.  Capital Markets (CM) is the face of the bank to the street from a trading perspective.  The CM group engineers custom derivative trades that hedge exposure for their clients (typically Hedge Funds, Mutual Funds, Corporations, Governments and high net worth individuals and Trusts) as well as for their own treasury group.  They may also do proprietary trading on the banks behalf for a profit – although it is this type of trading that Volcker Rule is seeking to eliminate.

If a Bank uses dark liquidity pools (DLP) they funnel their Brokerage trades through the CM group to avoid the fees associated with executing an exchange trade on the street.  Such activities can also be used to hide exchange based trading activity from the Street.  In the past, Banks used to make their substantial revenues by profiting from their proprietary trading or by collecting fees for executing trades on behalf of their treasury group or other clients.

Banking and within it, capital markets continues to generate insane amounts of data. These producers range from news providers to electronic trading participants to stock exchanges which are increasingly looking to monetize data. And it is not just the banks, regulatory authorities like the FINRA in the US are processing peak volumes of 40-75 billion market events a day http://www.vamsitalkstech.com/?p=1157 [2]. In addition to data volumes, Capital Markets has always  possessed a variety challenge as well. They have tons of structured data around traditional banking data, market data, reference data & other economic data. You can then factor in semi-structured data around corporate filings,news,retailer data & other gauges of economic activity. An additional challenge now is the creation of data from social media, multimedia etc – firms are presented with significant technology challenges and business opportunities.

Within larger financial supermarkets, the capital markets group typically leads the way in  being forward looking in terms of adopting cutting edge technology and high tech spends.  Most of the compute intensive problems are generated out of either this group or the enterprise risk group. These groups own the exchange facing order management systems, the trade booking systems, the pricing libraries for the products the bank trades as well as the tactical systems that are used to manage their market and credit risks, customer profitability, compliance and collateral systems.  They typically hold about one quarter of a Banks total IT budget. Capital Markets thus has the largest number of use cases for risk and compliance.

Players across value chain on the buy side, the sell side, the intermediaries (stock exchanges & the custodians) & technology firms such as market data providers are all increasingly looking at leveraging these new data sets that can help unlock the value of data for business purposes beyond operational efficiency.

So what are the  different categories of applications that are clearly leveraging Big Data in production deployments.

CapMkts_UseCases

                      Illustration – How are Capital Markets leveraging Big Data In 2016

I have catalogued the major ones below based on my work with the majors in the spectrum over the last year.

  1. Client Profitability Analysis or Customer 360 view:  With the passing of the Volcker Rule, the large firms are now moving over to a model based on flow based trading rather than relying on prop trading. Thus it is critical for capital market firms to better understand their clients (be they institutional or otherwise) from a 360-degree perspective so they can be marketed to as a single entity across different channels—a key to optimizing profits with cross selling in an increasingly competitive landscape. The 360 view encompasses defensive areas like Risk & Compliance but also the ability to get a single view of profitability by customer across all of their trading desks, the Investment Bank and Commercial Lending.
  2. Regulatory Reporting –  Dodd Frank/Volcker Rule Reporting: Banks have begun to leverage data lakes to capture every trade intraday and end of day across it’s lifecycle. They are then validating that no proprietary trading is occurring on on the banks behalf.  
  3. CCAR & DFast Reporting: Big Data can substantially improve the quality of  raw data collected across multiple silos. This improves the understanding of a Bank’s stress test numbers.
  4. Timely and accurate risk management: Running Historical, stat VaR (Value at Risk) or both to run the business and to compare with the enterprise risk VaR numbers.
  5. Timely and accurate liquidity management:  Look at the tiered collateral and their liquidity profiles on an intraday basis to manage the unit’s liquidity.  They also need to look at credit and market stress scenarios and be able to look at the liquidity impact of those scenarios.
  6. Timely and accurate intraday Credit Risk Management:  Understanding when  & if  deal breaches a tenor bucketed limit before they book it.  For FX trading this means that you have about 9 milliseconds  to determine if you can do the trade.  This is a great place to use in memory technology like Spark/Storm and a Hadoop based platform. These usecases are key in increasing the capital that can be invested in the business.  To do this they need to convince upper management that they are managing their risks very tightly.
  7. Timely and accurate intraday Market Risk Management:  Leveraging Big Data to market risk computations ensures that Banks have a real time idea of any market limit breaches for any of the tenor bucketed market limits.
  8. Reducing Market Data costs: Market Data providers like Bloomberg, Thomson Reuters and other smaller agencies typically charge a fee each time data is accessed.  With a large firm, both the front office and Risk access this data on an ad-hoc fairly uncontrolled basis. A popular way to save on cost is to  negotiate the rights to access the data once and read it many times.  The key is that you need a place to put it & that is the Data Lake.
  9. Trade Strategy Development & Backtesting: Big Data is being leveraged to constantly backtest trading strategies and algorithms on large volumes of historical and real time data. The ability to scale up computations as well as to incorporate real time streams is key to
  10. Sentiment Based Trading: Today, large scale trading groups and desks within them have begun monitoring economic, political news and social media data to identify arbitrage opportunities. For instance, looking for correlations between news in the middle east and using that to gauge the price of crude oil in the futures space.  Another example is using weather patterns to gauge demand for electricity in specific regional & local markets with a view to commodities trading. The realtime nature of these sources is information gold. Big Data provides the ability to bring all these sources into one central location and use the gleaned intelligence to drive various downstream activities in trading & private banking.
  11. Market & Trade Surveillance:Surveillance is an umbrella term that usually refers to a wide array of trading practices that serve to distort securities prices thus enabling market manipulators to illicitly profit at the expense of other participants, by creating information asymmetry. Market surveillance is generally out by Exchanges and Self Regulating Organizations (SRO) in the US – all of which have dedicated surveillance departments set up for this purpose. However, capital markets players on the buy and sell side also need to conduct extensive trade surveillance to report up internally. Pursuant to this goal, the exchanges & the SRO’s monitor transaction data including orders and executed trades & perform deep analysis to look for any kind of abuse and fraud.
  12. Buy Side (e.g. Wealth Management) – A huge list of usecases I have catalogued here – https://dzone.com/articles/the-state-of-global-wealth-management-part-2-big-d 
  13. AML Compliance –  Covered in various blogs and webinars.
    http://www.vamsitalkstech.com/?s=AML
    https://www.boozallen.com/insights/2016/04/webinar-anti-money-laudering – 

The Final Word

A few tactical recommendations to industry CIOs:

  • Firstly, capital markets players should look to create centralized trade repositories for Operations, Traders and Risk Management.  This would allow consolidation of systems and a reduction in costs by providing a single platform to replace operations systems, compliance systems and desk centric risk systems.  This would eliminate numerous redundant data & application silos, simplify operations, reduce redundant quant work, improve and understanding of risk.
  • Secondly, it is important to put in place a model to create sources of funding for discretionary projects that can leverage Big Data.
  • Third, Capital Markets groups typically have to fund their portion of AML, Dodd Frank, Volcker Rule, Trade Compliance, Enterprise Market Risk and Traded Credit Risk projects.  These are all mandatory spends.  After this they typically get to tackle discretionary business projects. Eg- fund their liquidity risk, trade booking and tactical risk initiatives.  These defensive efforts always get the short end of the stick and are not to be neglected while planning out new initiatives.
  • Finally, an area in which a lot of current players are lacking is the ability to associate clients using a Lightweight Entity Identifier (LEI). Using a Big Data platform to assign logical and physical entity ID’s to every human and business the bank interacts can have salubrious benefits. Big Data can ensure that firms can do this without having to redo all of their customer on-boarding systems. This is key to achieving customer 360 views, AML and FATCA compliance as well as accurate credit risk reporting.

It is no longer enough for CIOs in this space to think of tactical Big Data projects, they must be thinking around creating platforms and ecosystems around those platforms to be able to do a variety of pathbreaking activities that generate a much higher rate of return.

References

[1] “The State of Capital Markets in 2016” – BCG Perspectives