Fill Your Big #Data Toolbox with the Right Tools

Choosing to make data analytics a part of their operational strategy helps community FIs remain competitive in the ever-evolving world of financial services. Before taking the plunge into data analytics, however, it is vital FIs have the right tools in place to do so.

Data analytics has the power to take financial institutions (FIs) to the next level of decision making. Data can provide valuable insights into consumers’ behavior, revenue and lifetime value. In turn, this helps FIs determine the best methods to retain and engage their existing pool of consumers.

As more FIs look to embrace data-driven decisions, it is important to remember becoming analytically proficient is not a single step process. Advancing an FI’s data capabilities takes time, strategy and the correct tools.

To effectively reap the benefits from data analytics, a recent CUinsight article recommends FIs build their data toolbox by doing the following:

  1. Gaining access – Without being able to access the necessary data, an FI’s data strategy may be rather short-lived. Consider engaging a designated analyst or third-party vendor to extract the data.
  2. Developing a solid infrastructure – Establishing a data model or warehouse provides the foundation for all future analysis. This is perhaps the most crucial step as an FI builds its data strategy.
  3. Starting small with analyticsDescriptive analytics, which provides insights into the past, is the simplest form of analytics. FIs that have yet to dabble in data analytics should start here to gain valuable information about historical consumer trends.
  4. Advancing to predictions – The next step up from descriptive analytics is predictive analytics. This form of analytics can help an FI better understand the future and answer questions like: What is the lifetime value of a particular consumer? Which consumers are likely to switch FIs?
  5. Optimizing the data – Beyond predictions, data can be used for prescriptions—meaning FIs can predict what will happen and recommend actions based on those predictions. Taking data this step further can involve optimization tools like Hadoop and Tableau. These tools utilize machine learning to identify patterns and create predictive models.