In our previous article we looked at how business intelligence (BI) can help banks to improve their operations, learn more about customers’ purchasing habits, boost marketing and sales. Considering how competitive the banking sector is, if banks strive to be successful they should already be using BI tools.
Data is a valuable asset that most banks have in abundance; however, the real problem is that many organizations still have trouble with properly using all of this pool of potentially valuable information. Simply having a lot of data won’t bring any real benefits, as the key here is to not only implement a BI solution, but also have a skillset to do it and the right kind of data management system within a company.
The right kind of data management system is imperative, as this is the first and most important step of any BI integration project. Before you can start building insightful dashboards and analyzing trends, all aspects of data management, including collection, cleaning, structuring and constant monitoring of data must be put in place.
The devil here is in the details. Everyone these days can point at big data, but the real challenge here is finding the needle in the haystack of data.
You see, not all data is created equal. When it comes to it, quality is more important than quantity. Low quality, messy and incorrectly formatted data won’t be of any use to anybody even with the best BI solution available on the market. You simply won’t be able to feed poor data into BI software. Well, at least right away.
Unfortunately, many banks have a problem with keeping their data “clean.” Because of that during a BI implementation project over half of the time could be spent manually cleaning and standardizing available data before it can be used.
To avoid wasting time doing this manual job certain master data management rules and tagging should be applied. Tagging should follow a standard format and everyone must know the standard, especially when you have multiple staff responsible for data tagging. For example, if the name of a bank’s employee is written as “Josh Thompson” one cannot write it “J. Thompson” or “Thompson, Josh.” Humans can recognize this is the same person, but for BI software these three spelling variations mean different entries.
Other factors to consider
Besides the above-mentioned challenges there are other important non-technical issues to consider. One of them is setting a right data culture within a bank . During and after a BI integration process an organization will go through a series of changes in both its culture and structure. There will be new roles, departments and procedures appearing as a result of new changes. This can be quite cumbersome, especially in large, non-agile banks.
It is highly recommended to build a new BI unit within a bank dedicated to gathering, managing and analyzing all the data. If each functional departments of a bank are left with analyzing their own data, there won’t be a centralized data ownership and it will be challenging to find a single source of truth.
Finally, for banks that choose to hire external contractors to do the integration, choosing a right BI vendor could also end up being a challenge. One vendor can lack the IT skillset; another cannot provide a right design and architecture, while the third one lacks knowledge in the banking sector. The best way is to opt for a firm that knows both the finance industry and IT side.
Centida is one of those few specialized niche firms that follow the principle of “from finance practitioners to finance practitioners.” Our advisory services focus on the improvement of operational, day-to-day activities in finance and controlling departments. Having an extensive experience in the financial services, Centida consultants can dig deep into the balance sheet and provide data-driven solutions across multiple dimensions.