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

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

by vamsi_cz5cgo

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

 

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