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The “Big” Debate: Classifying Data for Better Decision Making

After passing into the new millennium, myself and many of my peers saw an increase in the use of innovative marketing terminology relative to technology.  Just around the turn of the century, the term “Big Data” surfaced in conjunction with analytics. Soon thereafter, it was being applied to large volumes of data exceeding 1 terabyte in size.  Then its use evolved and became applicable when discussing data warehouses. BI_shutterstock_47913211sm

I don’t know about you, but from my perspective, data volumes in excess of 1 terabyte are not new.  True, new hardware and software technology has enabled companies to process, move and manage what is now called “Big Data” much better than ever before.  Many large companies in financial services – insurance companies, wealth management, banks, and credit card companies – have been contending with the processing, manipulation and analysis of large amounts of data for decades.  Fundamentally, data is derived from a variety of sources and is ultimately used by businesses across industries to make smart decisions.

It seems other leading IT leaders in banking feel the same.  Our team was very interested in a recent American Banker interview with Bank of America’s, Catherine Bessant.  She states she hates the phrase “Big Data” and just prefers to use the term “data.”  She went on to explain the following:

“As a term, it means different things to different people, and it creates a black box or a mystery around data.”

So, the question; rather the “Big” question is whether the term “Big Data” is appropriate and considered a meaningful use of classifying data by business leaders.  Let’s explore the context of different business problems associated with “data” and its various uses from a business function perspective.

Data is derived from the following areas:

  • Transactional & Application Data: A variety of input systems capture information in its various forms by financial services company where sources are from customers, retailers, processors, payment networks, financial exchanges, and others that contain proprietary payment information about the customer.
  • Documents: Whether initially captured electronically or scanned at a later time, documentation in this category emanates from loans, investments, contracts, disclosures, and other material required for safekeeping.
  • Enterprise Content:  Going beyond web content management, every business and organization contains rich content about their customers that must be organized, maintained, and made available for use by the business.
  • Machine & Sensor Data: Derived from servers, applications, sensors, web feeds, networks, and service platforms, this type of data contains a wealth of information on customer and consumer behavior and location, consumer quality of experience, financial transactions, security and compliance breaches, as well as the state of industrial processes, transportation networks and vehicle health.
  • Social Data: Information gleamed from Twitter, Facebook, LinkedIn, and other social media applications can be effective in understanding customer sentiment.  As the payments chain continues to evolve, retailers will become a rich source of consumer spending pattern data.  Moreover, further maturity of navigation based technologies with point of sale solutions will place the merchant data in a much higher value bracket than today.
  • Web Data:  Any information sourced from the web through search engine technologies consumed by the business for further use in analytics.

It is then used in some of the following areas:

  • Operations Management: Supporting all aspects of day-to-day management in financial services, activities benefit from Risk Management, Regulatory Compliance, Operations Decision Management, Exceptions Management, Finance Management, Alerting, and other decision support systems. 
  • Business Analytics:  Encompasses the use of enterprise data from all sources from within the business to understanding customer trends and patterns.  Providing the means for interpreting data that has been collected and organized into structured components for in depth quantitative analysis to support and aid in decision making is now the mainstream.  Whether its use is at the department, divisional or holding company level, the ability for self-service to various forms of real-time data for analysis is essential.
  • Forecasting:  The accuracy and use of historical operational data is vital to trending analysis and forecasting.  Emphasis will continue to improved budgeting, support financial management, product profitability, sales pro-formas, marketing campaigns and other functions.
  • Predictive Modeling:  Not only have competitive pressures to improve bank profitability warrant this capability, but the advent of Dodd-Frank with its mandatory stress-testing frameworks have made this essential for large financial institutions.  Leveraging data from internal and external sources to test product models, assess Basel II capital requirements, conducting risk modeling, and for other functions warrants investments in this area.
  • Performance Optimization:  Managing the performance of each line of business and measuring their effectiveness using a multitude of data metrics is key to improving overall shareholder value.  While it is important to monitor performance through an enterprise reporting framework, the foundation to performance is excelling at Business Process Management and continuing to identify areas to improve information flows and work processes to bring about improved efficiency.
  • Social Analytics:  Exploring and understanding how personal banking preferences and motivations through the use of social media analytics can empower financial institutions to drive growth in new customer segments. Social media analytics also helps financial institutions develop new marketing programs and fuel merchant-funded rewards programs based on the social activities and chatter.
  • Web Analytics: A rich source for identifying where your customers are utilizing your services along with the related attributes is through your web analytics.  Financial institutions must evolve their use of Know Your Customer information to improve channel delivery for customers and reduce redundant services.

Analytics Use Cases in Financial Services

  • Using advanced analytics to profile and segment target customers to improve marketing effectiveness.  MasterCard mines transaction data to help marketers deliver targeted advertising using its extensive database of retail purchases.
  • Analyzing data to determine how people will interact in new channels. Bank of America’s rollout of their new ATMs with Teller Assist used analytics to determine whether it would decrease or increase teller transaction time.
  • BI delivered around social conversations is helping companies like Walmart measure customer sentiment on their Bluebird product and uncover new opportunities for serving their unbanked and underbanked customers.
  • Using financial based analytics to streamline statement creation, financial institutions can accelerate month-end updates.
  • Using BI technologies to create an online credit tool, financial institutions can generate increased annual business value on average for each customer.
  • Modeling data to conduct test marketing on solicitations can aid financial institutions in identifying campaigns that will yield higher response rates.  Facebook has done work with Datalogix to track whether people buy a product after viewing an ad on the social networking site. Similar strategies can be tested by banks on social networks and their websites.

I think it’s safe to say the ways in which data is used in financial services has evolved as a result of new technology.  Yet, the core components for action remain the same – the ability to sense and take action or respond.  Why not classify data by how it is being used in new ways as opposed to generalizing challenges and labeling them with the term “Big Data”?  Financial institutions will see greater executive sponsorship and funding on projects where the potential value can be defined based on a new core concept.

What has been your view on the rationale for continued use and applicability of the term “Big Data”?  How have you approached this subject within your organization?  For more of our team’s insights and strategies with “data” (or Big Data depending on your preferred choice in wording), I’d suggest you download Perficient’s Big Data Planning Guide for Financial Services.

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