Efficient Data Warehouse Use through Strategic Data Management

In today’s cut throat world of financial services provision, the ability to rapidly respond to changing market conditions is fundamental.

Unfortunately, the need to be first off the blocks can have negative repercussions. And in probably no other area is this more evident than in the constant tweaking of enterprise systems and data warehouses. Market pressure, business urgency and new regulations (like Basel III, the Dodd-Frank Act and FATCA) routinely drives financial institutions into stop gap, tactical, work around responses for their data management problems.

However, following this path of least resistance rarely, if ever, addresses the root cause of the organisation’s systems and data in a long term and sustainable way. On the contrary, it only provides temporary relief, leads to problematic data fragmentation and in the long term, compounds the bank’s data challenges. This is why there is a need to have a strategic approach to data management as this ensures cost efficient, collaborative, sustainable and worthwhile solutions.

But for strategic data management to bear the desired results, it must take into consideration several factors including data variegation, organizational culture, business complexity and senior sponsorship. More specifically, organisations must deal with the following challenges:

• Vast scope of enterprise data – Banks are heavily dependent on data. In fact, a financial institution’s most important asset outside its customers is the information it possesses. For today’s bank operating in multiple countries and with several business lines, vital data is generated on multiple fronts. The sheer volume and diversity of data can make determining where to start, a difficult affair.

• Organization silos – Whether out of functional necessity or out of the need to retain power, certain departments may insist on retaining their data independent of the rest of the organization including having their own data definitions, data management standards, data controls and data warehouse.

• Obscure responsibility and data ownership – Without an organization-wide view of enterprise data, there is unlikely to be a clear definition of data ownership, accountability and authority. In such scenarios, the data is often viewed as the responsibility of the IT department who, not being subject matter experts, are unable to define just what good and accurate data should look like.

• Interference with standard business processes – Even as the bank embarks on an elaborate process for defining its data, business processes must continue. The need to balance such an expansive project with the urgency of day-to-day operations is a delicate one.

• Absence of global financial data management standards – There are several standards on data management often addressing a specific area or narrow niche. There is however none that goes into as much breadth and depth as to cover every area of the financial data management universe.

But these challenges can be surmounted by using a systematic approach. This includes:

• Developing an initial scope – Banks collect an enormous amount of data. But not all data is equally important. The first step to data management is defining the different types of enterprise data and establishing the data that’s strategically important for the institution. Scope definition should encompass capturing the regional businesses, distinct divisions and specific jobs affected by each type of data.

• Align data with business objectives – Of what value is each form of data to the attainment of business goals? As opposed to a general statement of objectives, considerable effort should go into tying data to affected stakeholders and processes (e.g. revenue, risk management, regulatory reporting, cost control etc). As much as possible, define quantitative metrics with specific values of just what factors characterize business goal achievement e.g. expense, revenue etc.

• Obtain Senior Management Sponsorship – In modern organizations, competing interests are an inevitable reality. Few businesses have the luxury of retaining staff that are not fully engaged in creating value for customers and shareholders. As such, staff will often give priority to activities that clearly have the support of senior management. Obtaining senior sponsorship for a strategic data management project is therefore vital in getting sustainable impetus and achieving sufficient organization-wide visibility. Due to the all pervasive nature of data, senior sponsorship must include all departments whose input will be vital.

• Develop a network of data champions – Senior sponsorship is crucial. But senior managers usually have too much on their plate to have the time to devote their entire energies to championing a cause – even when that cause is as crucial as data management. As such, data champions should be identified in each unit with their primary purpose being to not only serve as a technical liaison with specific departments, regions or product teams, but also to ensure continued awareness of strategic data management at the process level.

• Develop standards – Agree on standard data definitions and terminology whose meaning will be the same irrespective of the product, process or division within which they are used. Avoid reinventing the wheel or contradicting already existing industry standards (e.g. as set out in a specific regulation such as Basel III or Solvency II). Map out the impact of changes to reference data on business operations and ensure these standards are reflected in the data warehouse.

• Execute and Monitor – Plenty of data comes from outside the institution e.g. market data. Banks should collaborate with other industry participants to increase automation of data exchange. Acquire systems and data warehouses that actualize the issues agreed upon as outlined in the preceding points above. Once the new data management strategy is in effect, develop Key Performance Indicators (KPIs) that ensure successful data management is not only achieved but sustained.