Big or small, does your data have it all?

Access to ever-increasing amounts of data can cause anxiety, and sometimes action paralysis. Many marketing professionals ask, “Is there more data I could look at before making a decision?” While large amounts of data may allow for progress on some business questions because more data is available, a recent Forrester Report indicates that while 74% of companies want to be more data driven, only 26% are good at connecting data to actions, citing a lack of resources for expert data interpretation, organization, and management.

How, then, should successful management teams utilize data to craft action plans that deliver tangible value from new knowledge? What are the different types of data that can be used for solution analyses? When is enough (data) enough?

Two terms are common in the data analytics arena. The term “Small Data” most often refers to business data that exists in current, in-house management systems, consisting of data points from the processing core, member demographic and psychographic profiles, surveys, transactional data and member feedback that is already in place. Additionally, there is easy access to the data, and in most cases, established resources, that can organize, maintain, and interpret the data into meaningful information. The financial institution can take immediate actions with good assurance that the data is current and valid.

“Big Data” is usually raw information obtained via subscription service that provides meta-amounts of data, often from multiple and changing sources in real time designed to be used with advanced, predictive software that identifies patterns. While it makes exponentially more data available, it comes with the challenges of requiring cloud technology, specialized analysis applications and the resources to curate, filter, process, and analyze the vast amount of data that is gathered.

So, is bigger better?

Data is a useful tool when, and only when, it provides insights and supports change. By focusing on the end goal of your analysis, you can review existing data to determine if it supports your needs and then acquire more only if needed.

Some questions to ask about your existing data are:

  • Does the data that you currently have allow you to make actionable decisions?
  • Is the data you have accurate (number-based, up to date)?
  • Do you have the detail needed for your analyses?
  • Can existing data be analyzed outside of “canned” reports?
  • Does it allow comparisons and trending that predict change?
  • Can any external influences (seasons, pandemic, disasters) be incorporated?

Until you can answer at least some of these questions on a continuous basis, more advanced analytics and exercises in optimization may simply be allowing you to do the wrong things faster and more efficiently. Most marketing questions will find actionable answers with “Small Data.” Demographic, psychographic and propensity data are cost effective, easy to understand and are actionable examples of small data sets. Benefits include speed-to-solution, cost effectivity and successful audience segmentation. The kinds of questions Small Data answers lead to strategic clarity and excellence in execution and can deliver near-term value in a competitive market. If the data you have does not provide meaningful information, outlining your specific needs and acquiring the additional data can be the next step on your data journey.

If the size of the solution warrants the investment of time and broader, more real-time data sets, then “Big Data” may be the answer. Larger institutions are more likely to depend on “Big Data” that can incorporate member purchase histories, web searches and social media activity along with your organization’s sales and marketing costs compared to that of your competition. It will still be critical to define the question and end goal of any analysis in order stay focused on solutions and not be distracted by the amount of available, but maybe unnecessary, data.

In addition to the previous questions above, consider these points:

  • Is the additional data meaningful and actionable (not “clutter”)?
  • Will the delay in implementing a larger solution impact market share?
  • How will constant updates be incorporated into existing programs?
  • What is the ROI needed for the investment required to implement new systems?

Perhaps the best answer to “Small” or “Big” data, is neither option because the real questions are how to connect data to actions, how to do it quickly and how to keep doing it. Big data and predictive analytics can help organizations do those things that you are already doing faster, more efficiently, or in a more targeted way. Small data can often tell you whether you are doing the right things in the first place.

Starting with core enterprise information, and delivering key analytics to the organization, without over-engineering the process and infrastructure is an important step on the journey to a more comprehensive approach that encompasses both “Big” and “Small” data.