Winning with Big Data

Data is more prevalent than ever, but according to a report from EMC and Forrester Research organizations are just starting to scratch the surface with only utilizing less than 5% of their available information. The biggest barrier holding organizations back is cost of implementation. However, the advances in Big Data technologies and techniques are making it easier than ever for companies to tap into that previously elusive 95 percent of data.

Those organizations navigating this change well are discovering that the more big data they can leverage for strategy and insight, the greater success they have in taking their business to the next level. 

In a recent post, we explored the significance of Big Data in today’s marketplace, as well as the challenge bank’s face in effectively utilizing the immense amount of information. We now turn our attention to the risks and advantages of Big Data.

As with any rapid, large scale effort, executing a Big Data strategy can mean dealing with risk. First and foremost, handling large volumes of intelligence also means protecting sensitive information that would damage the enterprise if leaked:

  • Private information such as credit card numbers, bank account numbers or personally identifiable information (PII) such as Social Security numbers.
  • Strategic information like intellectual property, customer analytics or business plans.
  • Performance information including sales figures, financial metrics and customer metrics used to make critical decisions. 

More likely, your bank may lack the internal governance and infrastructure necessary to organize volumes of stored data in order to capitalize the vast opportunity presented by Big Data. In fact, few regional or community banks have in-house experts able to use data effectively, because these analysts can be costly and scarce. 

But these risks are mainly attributable to growing pains that can be managed with a solid Big Data approach built on a few core strategies:

  1. Identify business requirements for data analytics based on strategic goals.
  2. Identify and train “Big Data Champions” in IT and various business units.
  3. Clearly outline your data sources and align those resources to meeting their stakeholder needs.
  4. Consistently assess the value of data against the associated costs of storage and retrieval, considering compliance, privacy and regulatory concerns.
  5. Maintain a scalable budget for infrastructure and database analysis tools.
  6. Create data sources and analytics, beginning with those that bring the highest value to the organization (increase customer retention, reduce customer acquisition risk, reduce fraud).
  7. Maintain a repeatable process for acquisition and usage of data. 

It’s worth the effort. Managing change and navigating the growing amount of intelligence has an ever-greater potential to pave the way for growth and stability: 

    • Profitability assessment of current and future service offerings.
    • Use of Customer Scorecards to build snapshots of sample or specific populations, helping you determine if a customer will be “desirable or undesirable.” Using statistical and mathematical algorithms to organize and analyze results, scorecards help predict short and long-term outcomes of various target customer profiles.
    • Nurturing customer relations and loyalty programs to gain valuable insight into customer purchasing trends and improving communication with customers.
    • Applying publicly obtained unstructured data (e.g., Facebook and Twitter) to improve process of choosing and targeting customers.
    • Ability to identify and mitigate fraud quickly by monitoring customer usage and spending data.
    • Ability to categorize and engage with customer information:
      • Volunteered Data, created and openly shared by individuals (e.g., social network profiles).
      • Observed Data, captured by recording the actions of individuals (e.g., purchasing pattern information).
      • Inferred Data, which profiles individuals based on analysis of volunteered or observed information (e.g., credit scores).

Have you identified your challenges and optimal growth opportunities? What other benefits and risks do you see as your bank begins to rely on Big Data for an intelligence driven strategy?

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Topics: Healthcare Data, Fintech

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