Considering how competitive the finance industry is, if banks strive to be successful they should use BI tools.
In our previous article we looked at how BI can help banks to improve their operations, learn more about customers’ habits, boost marketing and sales.
Data is a valuable asset that most banks have in abundance. However, the problem is many organizations still cannot properly use this pool of potentially valuable information. Simply having a lot of data won’t bring any real benefits. The key is not only implement a BI solution, but also have a skillset to do it, and the right kind of data management system.
The right kind of data management system is imperative. In fact, this is the most important step of any BI integration project. Before you can start building dashboards, put in place all aspects of data management. This includes collection, cleaning, structuring and monitoring data quality.
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 equal in terms of quality. 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 the best BI solution is useless when you have poor data quality. You simply won’t be able to feed poor data into BI software.
Unfortunately, many banks have a problem with keeping their data “clean.” During a BI implementation project, data analysts spend the majority of time manually cleaning and transforming data before it can be used.
To avoid wasting time doing this manual job, you can apply certain master data management rules and tagging. Tagging should follow a standard format and everyone must know the standard. This is especially true when you have multiple staff responsible for data tagging. For example, if a name is written as “Josh Thompson” one cannot write it “J. Thompson” or “Thompson, Josh.” Humans can recognize this is the same person. However, 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. The company will create new roles, departments and procedures as a result of these new changes. This can be quite cumbersome, especially in large, non-agile banks.
We recommend building a new BI unit within a bank dedicated to gathering, managing and analyzing all the data. It is a bad idea when each functional department of a bank has their own data in a silo. In this case, 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.