SPONSORED CONTENT PRESENTED BY JACK HENRY
Understanding data intelligence and analytics is increasingly critical to being able to serve today’s consumers at their moment of need. Yet it’s an area many in banking leadership feel unprepared to capitalize on.
If you fall into this category, you are certainly not alone.
Most banking leaders don’t consider themselves to be data experts. Data analytics is, in fact, one of the top technology investments banking industry executives plan to make over the next two years.
Picture it:
- What could you achieve with your data if you could see changes happening in your system as they occur?
- What if you could easily identify trends and security threats happening in the moment and make more-informed decisions?
Today it’s critical for your bank to effectively serve – and quickly answer questions for – your customers when they need it most.
Here are eight frequently asked questions (and answers!) to help you better understand data analytics, why it’s is increasingly important, and how to best leverage it to gain key insights and efficiencies.
1. What is data analytics?
Data analytics is the collection, transformation, and organization of data to draw conclusions, make predictions, and drive informed decision-making.
2. Why is data analytics important?
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.
3. Who is looking for data analytics?
Typically, your 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.
4. What’s the difference between operational reporting and data analytics?
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 provide a long view into trends over time (dashboard visualizations). This type of query reads a lot of historical data. Lastly, operational reports are static whereas analytics are dynamic.
5. Static vs. dynamic: What’s the difference?
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: This is when 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.
6. Do data analytics and operational reporting use the same data source?
No. Operational reporting uses a transactional database. While this design works well for operational reporting, it’s a less than optimal solution for data analytics. 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 (general ledger, loans, and more).
7. What is a data warehouse?
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. Often, 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.
8. Is query performance the only benefit a data warehouse offers?
No. In addition to analytics performance, a data warehouse offers other benefits. Transactional databases tend to be siloed. For instance, customer 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 it can help you increase operational efficiency, improve productivity, and boost innovation. And by gaining immediate insights into your data, you can also identify trends and security threats happening in the moment to make more-informed business decisions.
Learn more today about how your bank can use metrics to see changes over time, harness the latest data intelligence innovations, and move forward with confidence through 2024 and beyond.
