Business intelligence in banking: How to optimize bank data with BI
Our previous blog article looked at the challenges of implementing business intelligence, or BI, in insurance, and its impact on claims processing. In this article, we’re going to focus our lens on business intelligence in banking.
What is business intelligence in banking?
Business intelligence in banking is defined as the use of analytics software, or SAAS (software as a service), to create data visualizations that are interactive and can be created at the desk top level by end users for banks and financial service companies. Commonly used banking business intelligence software includes: Microsoft Power BI, Tableau, Tibco Spotfire, and Domo. Banking business intelligence applications can be hosted on the cloud and configured to run private dedicated servers for financial services companies that are strict on data security requirements.
Reviews of the top 4 banking business intelligence SAAS providers rank as follows:
- Microsoft Power BI – 4.5 stars from PC Mag
- Tableau – 4.5 stars from PC Mag
- Domo – 3.5 stars from PC Mag
- TIBCO Spotfire – 3 stars from PC Mag
We are not surprised that Microsoft Power BI is at the top of the list for the best business intelligence in banking software. It is our go-to choice in-house at The Lab.
We originally started our business intelligence in banking adventure using Tableau 5 years ago and found that the learning curve was steep, the price extremely expensive, and the market wasn’t mature enough for competitors to step in with different features and functions. But, as time passed and new releases of competitor software from the king of software companies (Microsoft) became available, we transitioned away from Tableau and focused on Power BI as our choice. We tested many of the lesser-known and more niche BI companies, but they just couldn’t keep up with banking data and will face an up-hill battle as users consolidate into bigger mainstream banking business intelligence platforms.
Top 5 benefits of business intelligence in banking
Business intelligence offers banks the adaptability needed to excel in both business-as-usual conditions and in more turbulent economic times. Worldwide, BI processes and software give banks deeper understanding of their business, their customers, and their future. It can also open the door for efficiency by shining a light on areas ready for cost-cutting measures, new business prospects, and more.
Examples of banking business intelligence benefits include:
- Feed as much data as you want into BI software, it will never get overloaded as long as it’s good, clean data (a process we’ll get into later).
- Faster reporting – Banking BI allows organizations to visualize both historical and current data in real time. This makes spotting patterns, potential bottlenecks, and setting goals easier based on historic metrics. No more waiting for a report for 2 months after you requested it from finance.
- Business intelligence in banking connects across disparate systems, removing the need to generate reporting from each one individually.
- Business intelligence in banking allows organizations to measure big data on their customers in quantities never seen before to help increase customer satisfaction. Banks can have a deeper understanding of their customers with banking BI, allowing them to address concerns proactively.
- More accurate reporting – business intelligence in banking removes the need to manually wrangle data by plugging directly into core systems databases.
How business intelligence works in banking and finance – a detailed business intelligence in banking case study
Banking business intelligence implementation enables users to connect multiple and disparate system sets together to display interactive data visualization dashboards that would normally not be able to communicate across platforms. In this banking business intelligence case study, we’ll follow a consumer and retail banking executive in charge of multiple product lines. Mortgage loans, home equity loans, car loans, and credit cards all fall under the supervision of our executive and they must report the monthly results of each on an ongoing basis.
Now, imagine that all the data for those is in completely different core IT systems and must be extracted every time it needs to be analyzed. Consolidating that banking data is a monumental task and takes four people two weeks to complete each month. That is the current state of most banks trying to get business intelligence in banking going.
Imagine being able to install a software layer on top of all those separate core banking systems and databases that connects all of them to enable “live” reporting of all the data at once. Ladies and gentlemen, that is how banking business intelligence works. While that might sound like the simplest fix of all time, a lot of work must be done in standardizing the underlying data before it can function in a useful way.
As you can see, financial services is a ripe target for a BI transformation. There’s data to be scoured, efficiencies to be uncovered… why, then, do so many banks struggle to get a return on their business-intelligence investment? And more importantly, how can banks move forward from current roadblocks to succeed in BI in banking?
Let’s look at the causes, the solutions, and the benefits of BI in banking.
Business intelligence in banking: Where the important operations big data is hiding
At The Lab Consulting, we like to say that we can help you “achieve top-quartile business intelligence analytics, using bottom-quartile data quality.” What does that mean for business intelligence and banking operations?
Well, when we show up at a bank for a business intelligence engagement—and we’ve done this for scores of leading banks, from regionals to multi-nationals—we’ll always find that they have lots of data on hand. This would seem to be all that’s needed, right? After all, a leading banking business intelligence solution such as Microsoft Power BI or Oracle Business Intelligence can pull from a virtually limitless supply of sources, and then let you choose the filters you want to see the stunning business intelligence revelations that empower business-strengthening decisions. You plug it in, and get your “aha moment.” Right?
Not exactly. As we’ve said, we’ll see banks boasting lots of financial indicators: whether for sales, loans, deposits, or financial information. But the devil is in the details: the “bottom-quartile data” we’d mentioned above. Banks, typically, lack the ability to drill down into their banking operations and see “who or what is performing at the right level, compared to expectations.” Sure, they’ll be able to count things after they occur. They’ll generate an overall forecast and some passable reports, but can’t see financial information down at the region, branch, or individual-knowledge-worker level.
That’s the kind of info that matters in banking business intelligence. It’s the kind of BI input that’s needed, and we help to make available: essential ratios, such as how many sales per individual? How many sales per region? Overall costs vs. salaries per individual. Overall cost to generate a loan vs. the yields and spreads by individual. You get the idea.
These are some of the essential banking KPIs needed when talking about BI in banking, and what will ultimately earn you the full benefits of business intelligence in banking.
Does business intelligence in banking work when employees police their own data?
We’ll see banks whose, say, commercial divisions are exceeding the stated revenue goal. Sounds great, right? But once you unbundle by region, and dig down into individual banking operations, you’ll see a whole different story. For four regions, for example, you might see one that’s hitting the ball out of the park (and, of course, making the others look better than they are, once they’re all averaged). The next may be slightly ahead. And the third and fourth regions may be lagging—by a lot. Amazingly, most banks fail to drill down to this level of detail; we usually end up taking the lead and showing them how much this reveals. It’s the crux of successful banking business intelligence and key to understanding and improving banking operations.
Often, banks will tell us, “We can do business intelligence on our own!” Which begs the question: Then why don’t they? Why don’t they unpack each region, drill down to the data of the individual producers within each one, and create BI in banking reports? Well, it will often turn out that their individual employee data resides in Excel spreadsheets…. which are maintained by those exact same individual employees! They’re keeping their own statistics on their own production. We, then, need to go in and retrieve it… which is often easier said than done, especially in the case of the low performers.
The low performers — whether regions, branches, or individuals — can sap the performance of the larger organization. And this drag can be hard to spot, even hidden, when a bank only uses “averages” to feed its BI system. As we said, “Achieve top-quartile business intelligence analytics, using bottom-quartile data quality.” Otherwise, you can’t truly see the importance of business intelligence in banking.
Business intelligence in banking: The case of the dirty data
Here’s a dirty little secret: When we request that a bank hand us over its data so that we can employ business intelligence to build dashboards, we simply won’t get it.
“Do you have salary information?”
“Where is it?”
And then when you find “it,” “it” isn’t what you expected. And all too often, it’s flat-out unusable as-is for BI in banking.
Often, we’ll be able to wrangle the data. We may find one database in which an employee is listed as “John Smith.” Yet another lists the same employee as “J. Smith,” and a third calls him “Smith, John.” A human can recognize the same employee. Databases, unfortunately, cannot. Thus, the databases need to be cleaned to become useful for BI in banking.
It gets worse. When a bank entrusts the building of its crucial databases solely to its IT team, with insufficient management input, you’ll find data with incomprehensible tags such as “47ABG9.” That’s confusing unto itself. But what if “47ABG9”is the title of a column of data? It means that the entire column is unusable. And the taller the column, the greater the loss to a business intelligence project.
The ultimate irony is that BI-crippling wounds like these are invariably self-inflicted.
Operational business intelligence in banking: dashboard design
Defined as an analytical display tool linked to multiple banking data sets across multiple systems, a banking dashboard is an integral element in business intelligence in banking. If you’ve got the (enriched, standardized) data to crunch, they can track and display anything from business process results to financial performance to key performance indicators, as in our example below.
Key performance indicators, or KPIs, are specific quantitative measurements of your business’s effectiveness. Being able to visualize them allows you to track your organization’s performance both currently and historically. It can also let you use predictive analysis to see your future performance, to an extent.
Below is a typical example of what a business intelligence in banking dashboard looks like when used for KPI visualization.
Grabbing banking business intelligence by the (long) tail
Business intelligence in banking relies on data; indeed, “data enrichment” is the art of making it more useful than ever, to generate the essential banking KPIs that executives can use to improve banking operations. That’s where we come in. We’ve helped banks far and wide get their wild banking data warehouses under control. For a quick example of how we get your data ready for a robust business intelligence in banking project, just see below.
Does your banking business-intelligence initiative lack the “intelligent input” it needs? Consider contacting The Lab. In one 30-minute screen-sharing demo, we can show you how we can help you use BI to transform your banking operations, just as we have with Fortune 500 clients worldwide. Even better, our business-intelligence engagements self-fund — that’s right, they pay for themselves — in just six months or less. And that promise is money-back guaranteed. Contact us to book your demo call today.