Managing data and analysis in the increasingly complex financial services landscape is more critical than ever. New legislation, increased compliance requirements and continued risk due to uncertain economic times have necessitated significant additional IT investment. Leaders in the financial services industry are under intense pressure to not only meet compliance and mitigate risk, but also to fully leverage IT investments and drive growth and profitability.
BI is Boss
At the center of managing critical data and its analysis is Business Intelligence (BI). Often referred to as any tool that can provide query-able access to business data by business users, technically BI is the actual query engine, and its value should not be underestimated.
Consider the many variables used to determine whether to buy or sell an investment: information about each company, financial data impacting the business, current or predicted economic conditions, changing regulatory environments, disruptions in the company’s supply chain and global impact. The complexity of these issues requires your team to have data that can instantly be analyzed and immediately acted on – because pennies and seconds can actually impact millions of dollars.
The work involved in managing and analyzing financial data runs the gamut from validating the integrity of the data, to executing the BI “engine”, to managing systems for analyzing and reporting. This lifecycle includes:
- Managing the sources of the data
- Data quality and cleansing
- Managing the definition of included data elements
- Tracking ownership of the data element definition
- Metadata management
Often, however, the IT systems responsible for data management are already overly complex and serve too many disparate functions, as they’ve usually evolved over a long period of time by many, many people.
In turn, the IT department’s management of these systems is hyper-focused on routine execution of processes that could and should be automated, and ends up expending its resources on troubleshooting and supporting. Statisticians, who ideally evaluate the trends and spot opportunities for improvement, may spend more time manually calculating standard statistics than doing the value-added analysis they were hired to do.
The prospect of adding even one new report or compliance measurement means the exponential addition of – potentially – thousands of data points, and incorporation into an IT system that is already overburdened. And with an exponential increase in complexity comes an exponential increase in management costs.
Welcome to Master Data Management (MDM)
The starting point to alleviating the strained data management system is to create a structured, common language (XML), understandable to all stakeholders and then build execution engines (BI) to translate that language into actual activity.
When all parties can understand and speak the same language, life for all stakeholders becomes less about tedious maintenance and troubleshooting, and more about value-add and market opportunities. The underlying technology (BI) that executes the data management pipeline can be updated with little or no need redevelop the data definitions, and the potential for interpretation errors is dramatically minimized.
The beauty of a properly built and executed BI strategy is that you can leverage the talent and skills you already have using well-proven technology. There’s no compelling reason to invest in risky, cutting edge technology because you already know exactly what you need.
Are you ready to join the BI gathering? This readiness starts with the understanding that an investment into IT infrastructure has proven ROI. The BI model underscores the value IT brings to financial services (and any industry whose data and its management are so critical to success.)
Between government mandated financial reforms and internal, business goals of increasing profitability while minimizing and managing risk, financial industry companies are recognizing both the need, and the opportunity to deliver better results through IT.