Top 10 Challenges In Building Data Warehouse For Large Banks
Top 10 Challenges In Building Data Warehouse For Large Banks
A data warehouse is the core of business today, irrespective of the industry. It is the basis of every decision, analysis, and Business Intelligence (BI) model adopted by the enterprise. Forecasting, planning, and strategizing are all heavily dependent on it. Therefore, building a data warehouse is a tactical and calculated initiative.
The finance industry and specifically the banking sector depends very heavily on tools and techniques that enhance and improve the decision-making process. With the help of big data, they can improve and communicate better with their top and bottom lines. Also, it provides them the ability to cohesively combine data from different sources and use it for financial decision-making.
The data warehouse of a bank is unique because it deals with people and finance as well (both as a product and service). Hence, the number of stakeholders involved is high. The other problem is that the data warehouse projects don’t usually pay much attention to front-end applications. Their emphasis is on the client supporting backend infrastructure. That does not work for banks as the front-end application of the data warehouse is equally important to them. This calls for inventive data warehousing.
Challenges In Building Data Warehouse For Large Banks
1. Demanding In Terms Of Time, Effort, And Cost – The data needed by the banks is immense. The bigger the bank, the more data it has. As a bank increases its operations, the complexity of the data also increases. It would not be incorrect to say that it takes a bank anywhere from a few years to at least five years to build its Enterprise Data Warehouse (EDW). Since the quantity of data is large, it takes a lot of effort to manage it. When time and effort increase, so does the related expense.
2. Lacking In Strategic Focus – One can’t build a data warehouse without inviting a cultural change because it is not just a technical project. It is also a business development project. For a bank, building an EDW means that the CXOs need to tighten their belts to monitor the progress and, at the same time, break down the barriers by providing full executive support.
3. Lacking In Divisional Collaboration – Building the EDW of a large bank is all about collaboration across all divisions. The architecture and design teams, source system teams, vendor teams, project teams, and business divisions need to work as a cohesive force. The sad part is that it is easier said than done. Achieving divisional collaboration in banks is an uphill task, but the data warehouse project will not succeed without it. Finding that common ground where all divisions can come together to supplement each other is a challenge.
4. Dynamic Environment And Frequently Changing Requirements – The banking sector has a dynamic environment that keeps on changing. It is continuously evolving, and so its requirements or business needs also keep on shifting and transforming. When change is so frequent, defining the data requirements is not easy. Herein lies trouble because the technical team needs clarity on data requirements before it can proceed with designing and building the data warehouse. If they remain vague and cannot define them precisely, the data warehouse project is bound to suffer a setback. The other option is to be creative, which makes for a thrilling ride.
5. Complexities Arising From Multiple Database Technologies – A large bank usually does not have all its data stored in one operating system. It uses several database technologies which are spread over many operating systems. The tools employed for analysis, reporting, data integration, etc., are also multiple. Therefore, deciding which tool to use is also a complicated process of trial and error. The task is made even more difficult by evolving and emerging technologies like columnar database, data virtualization, parallel processing, etc. The call of the time is integrating Big Data technologies with IoT (Internet of Things) for better and more in-depth insights.
6. Questionable Data Quality – The banking sector, in general, suffers from age-old practices that often lead to non-unified data capturing, and this problem persists across organizations. Therefore, often critical information is missed, and emphasis is instead laid on non-essential statistics. Hence, data which is an organizational asset, lands up becoming its curse. Until the processes become well-defined and technologies get aligned for ensuring data quality, the struggle to build a data warehouse for banks is long.
7. No Clarity In True Data Source – Banks operate across multiple locations geographically. They also work with all segments of the people. But that is not the limit, for their history is all about mergers and acquisitions. Suffice to say, data is scattered all over. Missing documentation and poor integration add to the chaos such that there is no way to figure out the true or precise source of data.
8. Dependency On Divisional Data Marts Instead Of Enterprise Data Warehouse – Banks usually build divisional data marts to fulfill the reporting needs of each division. They find it comfortable to work with “known” rather than the complete enterprise view, which is “unknown.”
9. Vendors More Interested In Promoting Own Products – Data warehouse service providers or vendors have their own suite of solutions. Like a good businessman, each vendor only promotes that which is his own because it will increase his earnings. The problem is that what the vendor is offering may not be a good fit for the client. So the customer interest takes a backseat.
10. Under Utilization Of Data Warehouse – Having a well-structured data warehouse but not using it to its full capacity is a waste of time, money, and effort. One can only stand to gain if one can harness and unleash its full capability. Banks have so much data that often large parts of it remain forgotten or underutilized. Sometimes the divisions fall back on their old practices and fail to use the data warehouse for analytics, BI, and reporting.
The finance sector is one of the unique industries today, where both traditional and modern practices have come to coexist. At one time, banks wouldn’t touch technology even with a long stick. Today it has become the basis of every process and decision. Banks are increasingly depending on data warehouses for implementing progressive change management practices. Nevertheless, the sheer volume of data, its quality, and source continue to give the data handlers nightmares. Though things are steadily improving, there is a long way they still have to go before achieving seamless integration and utilization of data. Until then, being proactive in sensing problem areas and coming up with creative solutions is their only saving grace.