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How banks can flip the iceberg and focus on advanced analytics

September 21, 2023
Automation inspirationData literacy
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Financial planning and analysis (FP&A) teams provide vital insight to leaders that can shape an entire organization. But when the ratio of time spent preparing data versus actually analyzing it is painfully lopsided, it becomes much harder to focus on initiatives that drive the business forward. (Think of an iceberg: their time spent analyzing data is above water, but the large mass hidden underneath is the heavy lifting of data prep.)

A recent Mckinsey article stated that an integrated, data-driven approach to business plays an essential part in significant productivity gains for banks. Forward-thinking FP&A teams are already on the right track to becoming data driven, and are making their lives easier by optimizing processes in three ways: enabling easier access to all data types and sources, incorporating more automation into data prep, and empowering collaboration of data science across disciplines.

They’re able to operate more efficiently in these ways by integrating a low-code/no-code analytics tool into their technology stack. With these tools, data experts no longer have to focus on tedious and repetitive tasks and are given the ability to collaborate with business leaders on strategic data analysis. And it’s important to note that this approach doesn't just benefit the analytics teams that work with data everyday — it benefits an entire organization. Here’s how:

Enable teams to focus more on advanced analytics

Being that more data can offer teams better insight when trying to assess risk, better understand the customer, or analyze fraud trends, most would think that the sheer volume of data available to banks is a huge advantage. In reality, more data means more to collect, more to combine, more to clean, and more work in the end.

However, when teams have access to automation capabilities that can streamline repetitive and laborious parts of data prep, large amounts of data become less intimidating. Suddenly, they’re able to focus more on developing and maintaining financial models, identifying trends and potential risks to the business, and analyzing the financial impact of strategic initiatives.

Low-code/no-code analytics platforms are what automate repeat tasks like removing outliers or combining columns. With self-documentation capabilities, they save the process (step-by-step) and make it available for reuse. This saves data experts a lot of time each day so they can focus more on advanced techniques and make more use of the company’s data.

Access all company data (types, sources, etc.) for better decision making

According to a 2023 mid-year report on the banking industry conducted by West Monroe: “Just 44% of leaders gave themselves an “A” grade when asked about the maturity of their access to and use of data,” proving banks can still improve in these two areas.

Not having the proper tools in place to access all possible data sources leaves vast amounts unused. And when it’s tucked away in siloed systems, the business can’t make the most informed decisions and mitigate risk where at all possible. When teams can automatically pull data from any source without involving IT - a desktop, software application, data warehouse, Excel files, etc. - ample time is saved and the most up to date information is used during analysis. This change in operations allows more focus on quickly identifying new customer trends; not pulling massive amounts of data to process.

Low-code tools even allow other teams in the organization to pull and work with the data they need self-sufficiently because coding knowledge isn’t required to do so. And when the platform being used is open source, users can be certain it’s always up to date with the latest technologies available. If a new data type is created, the platform can process it. If a new tool becomes available, the platform can integrate with it.

Close the gap: where business minded meets data oriented

Closing the gap between those who can and can’t work with data allows banks to accelerate toward becoming fully data driven. When data science experts can share their knowledge, it allows others to become data literate and more self-sufficient in creating analysis and applying their domain expertise during the process.

Business analysts possess valuable domain experience that helps them successfully identify areas of improvement and other business opportunities. The challenge is applying this knowledge when needing to rely on others for analytics. Similarly, data scientists hold the expertise to build advanced analysis but don’t always know business best practices to provide departments with the results they’re looking for (and without all the back and forth it takes to get there).

Low-code/no-code platforms are what bring business-minded individuals and data-oriented individuals together. Business analysts are enabled to work with the data themselves, apply their expertise to build more insightful analysis, and excel as citizen data scientists. They only need to depend on data experts for advanced tasks, allowing the data experts to focus on machine learning, knowledge discovery, and predictive analytics.

Redefine ‘business as usual’ with a low-code tool

Processing data more efficiently is what frees up time to work on advanced analysis that can bring more value to the business. To get there, organizations need the ability to automate tedious work and enable business analysts to do their own analysis. Soon, FP&A teams are able to work more closely with line of business individuals and help support and inform critical business decisions.

KNIME’s low-code, open source analytics platform allows banks and other finance institutions to automate every aspect of financial analytics - from data access and cleaning to credit risk management and fraud detection. KNIME Business Hub, our enterprise software, allows business and data teams to easily (and quickly) share their analysis across the organization so more people can work with data-driven insight.

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