If data analytics has been on your radar recently, you’re in good company.
Data analytics is one of the top three technology investments banks and credit unions have planned over the next two years, according to the 2023 Jack Henry™ Strategic Priorities Benchmark Study.
Our clients have told us they want a better understanding of analytics as a whole – what is data analytics, why data analytics is important, and how to best leverage data to gain insights.
We put together this list of eight frequently asked questions – and answers – to help you bridge this gap and better understand how to use data analytics in banking:
Data analytics is the collection, transformation, and organization of data to draw conclusions, make predictions, and drive informed decision-making.
Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. Data analytics helps you optimize your performance, operate more efficiently, maximize profit, and make more strategically guided decisions.
Typically, the C-suite or high-level personnel who are thinking in terms of years – not months or weeks – are looking for data analytics. These individuals may rely on data scientists to compile years of operational data or streams of big data into visualizations that tell a story over time.
Operational reports provide the data while analytics deliver insights. Operational reports focus on a more granular view of current activity (gridded reports). In contrast, analytics queries give us a long view into trends over time (dashboard visualizations). This type of query reads a lot of historical data. Lastly, operational reports are static and analytics are dynamic.
Static: Reports in your transactional system are defined before you run them, with definitions including which data will be represented in the report. If you want different data, you’ll need to run a different report or use the report writing tool to modify your report and then run it.
Dynamic: On the other hand, analytics display results for the selected parameters in a report or visualization intended to illustrate patterns and trends, bringing insights to the forefront without you or your team having to do any of the heavy lifting.
No. Operational reporting uses a transactional database. While this design works well for operational reporting, it’s a less than optimal solution for data – as it’s not designed for fetching the massive volumes of data an analytics query requires. For data analytics, your most meaningful data is pulled into data marts so it can be aggregated and optimized for transactional reporting. For example, you can have targeted data marts to quickly report on different products (i.e., GL, loans, and more).
A data warehouse is a database designed for data analytics. When data is loaded into a warehouse, it’s pre-aggregated to return results fast for common queries. Oftentimes, database tables are denormalized – with some fields duplicated across several tables to reduce the number of databases required to join. This improves query performance – even when fetching tens of thousands (or millions) of data points.
No. In addition to analytics performance, a data warehouse offers other benefits. Transactional databases tend to be siloed. For instance, accountholder data is held on a completely different database than a general ledger – making it difficult, or impossible, to obtain complete, holistic insights across your organization. A data warehouse can ingest data from different sources and make it available via data marts designed for the needs of different business users.
Aligning your strategic direction with data analytics and knowing how to use data analytics in banking can help you increase revenue, productivity, and innovation all while gaining immediate insights into your data.
Discover how data analytics can empower your decision-making and drive growth. Contact us today to learn more!
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