Monthly Archives: September 2015

Big Data in Banking and Finance

The movement from in-person to on-line banking and the regulatory requirement to maintain historical data has lead to huge and diverse volume of data being collected by Banks and financial institutions. In simple terms, data collected by banks is estimated to grow by 76% in 12-18 months. Though challenging, this exponential growth in volume, variety and velocity of data (3Vs of Big Data) offers extraordinary opportunity to trigger growth with new products and services.

Many players in the financial vertical, though wary at the start, have become enthusiastic adopters of big data technologies such as Hadoop.

Banks and financial companies are using innovative technologies to consolidate data that lies in different silos, department and regions, to analyze them holistically and derive enterprise-level analytics.

Those institutions that had employed relational database management systems found it challenging to process and analyze growing amount of data and have since moved to network of data stores to solve this problem.

Based on the early adopters, here are some broad developing trends in the area of big data, particularly for the financial vertical.

  • Bigger and better the data set, accurate the prediction – Data collected with greater granularity over longer period of time from diverse sources, make for better predictive models and forecasts.
  • Better predictive models, particularly the ones related to customer behaviour leading to launch of innovative products/services that have increased conversion rates.
  • Reduction in cost of doing business, if Operations can use data to understand how customers do business with them.
  • Predict non-performance of assets by analysing payment pattern, prioritize collection and reduce chance of non-collection.

Big data is driving innovation in today’s financial services market particularly in the area of capital management, regulatory compliance, corporate performance, trade execution, security, fraud management, and other instrumental disciplines. It is particularly so in uncertain times, companies that flourish are the ones that employ statistical modeling, and predictive analytics for real-time decision making.


Business Intelligence and Business Analytics, how are they different?

Many Business Intelligence marketers have been moving onto the term “Business Analytics” but users are confused what exactly is Business Analytics and how does it differ from Business Intelligence.

My search took me to this blog by Justin Heinze, “Business Intelligence vs. Business Analytics: What’s The Difference?” out of the seven expert definitions, I liked the following three differentiations,

Pat Roche, VP – Engineering, Noetix Products, Magnitude Software, explains “Business Intelligence is needed to run the business while Business Analytics are needed to change the business.”

“…Business Intelligence is looking in the rear-view mirror and …Business Analytics is looking in front of you to see what is going to happen. This (Analytics) will help you anticipate in what’s coming, while BI will tell you what happened.”, says Mark van Rijmenam, CEO / Founder, BigData-Startups

Francois Ajenstat, Senior Director, Product Management, Tableau, puts it rather clearly when he says, “Traditional business intelligence (BI) has been focused mostly on reporting. In this approach to BI, highly-formatted reports are created by a few people—typically report developers—and distributed to an entire department or organization. More recently, the trend in analytics has been instead to provide the people who have questions about their data with the tools to get their own answers. It’s now about letting business people become analysts themselves.”

But experts are clearly divided as to whether Business Intelligence is a subset of Business Analytics or the other way round.

Timo Elliot, Innovation Evangelist, SAP, says “…when SAP says “business analytics” instead of “business intelligence”, it’s intended to indicate that business analytics is an umbrella term including data warehousing, business intelligence, enterprise information management” where as Dr. Rado Kotorov, Chief Innovation Officer of Information Builders, says “Analytics is a function of BI, BI used to refer to platform capabilities to access data, manage metadata, development tools for reports, dashboards, and applications, and publishing, scheduling and distribution capabilities. Analytics referred to either methods of analyzing information (i.e., descriptive, predictive, regression, neural networks, etc.) or the tools to perform those methods. Thus, analytics is a subset of the broader platform capabilities.”

So where do we stand, we fall back on the original definition of the word “analytics” as described in Wikipedia, “Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight.”

And “Intelligence” gives “the cognitive abilities to learn, form concepts, understand, and reason, including the capacities to recognize patterns, comprehend ideas, plan, problem solve, and use language to communicate.”

So, Business Intelligence offers dashboards/reports recording trends/patterns of the data at hand while Business Analytics offers Data Discovery/Analysis tools to ask intelligent questions and get intelligent answers, the quality of the data, the tool and the intelligence of the user defines the output.