THE DYNAMICS OF DATA MANAGEMENT, DATA GOVERNANCE AND LOGISTICS
Research from Deloitte showed that 42 percent of firms do not monitor what is actually going on in their data management processes*. At the same time, 62 percent feel this is the biggest thing set to improve in 2014.
That 62 percent are right.
Effective data management is fundamental to an effective risk management policy. Unmonitored data management is uncontrolled data management. And uncontrolled data management is uncontrolled risk management. In modern capital markets, data management is a very real challenge. Transparency-seeking regulators are flexing their investor-protecting muscles and uncontrolled data management is an unsustainable position for any financial organization to be in.
The traditional vision of data management in financial services has typically focused on aggregation and cleansing. These processes, important as they are, are just two elements of the complete data supply chain. Focusing solely on these runs the risk of overlooking the big picture: the crucial role that delivery, logistics and governance play; ensuring that data does its share to effectively support key operations within businesses.
Data governance is, in essence, a quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information. In other words, it’s not just about the quality of the data that feeds various operational decisions – although this is of course critical. It’s also about the way data moves through the organization, and what happens to it when its hits key touch-points. It’s about the infrastructure that ensures an organization’s IP rights are protected and about managing costs and risks effectively. Effectively addressing data governance is central to regulation, compliance and risk functions, as well as quality, accuracy and completeness of data, and structures, standards, policies and controls.
This is why the concept of data governance is fast gaining momentum, particularly in financial services circles.
Envisioning a data management model that is truly dynamic requires a data supply chain. One which is flexible, adaptable and re-configurable to the needs of the users. Understanding the variety of needs and systems within financial institutions means understanding that aggregation and cleansing alone do not guarantee the integrity of data from start to finish. The entire data supply chain must be the focus.
Inevitably, firms will need the right tools for this practice, and effective data management solutions are the essential foundation on which to build data governance and ensure appropriate data logistics. These systems have already delivered exemplary levels of transparency, visibility and highly granular control over data consumption and distribution, and enable organizations to embed data management disciplines into formal technology practice. Usage and change management functions ensure that data governance benefits the organization by enabling cost control, cost allocation and contractual compliance.
Critically they also break down the barriers between individual information silos that reside within individual business units, which makes it easier to access data and use it to serve the organization’s business goals.
But addressing data governance is more than a technology issue. Data governance is a state of mind. It’s about having the right mentality within the organization and focusing on achieving and maintaining best practice. Firms may develop a business case for outsourcing data management, but firms still need to know how data is touched, changed and distributed throughout their operations.
One of the major data management challenges financial institutions face is that no individual or department takes ownership of critical information and its quality and integrity. As a result, IT departments often become the owner by default. For a discipline that touches on meta data management, security policies, business process management and risk management, a central data team and solution can ensure successful data governance.
While the road to cultural change within financial services organizations may be long, technology helps speed the journey. Centralized data management systems address much of the structural requirements of a sound data governance program by cleansing information, mapping its delivery to critical systems and measuring its consumption. With the technology in place, executives can set about implementing data governance strategies that will stick across the organization.
I like to think of this as data logistics: the process of ensuring that users get the data they need, on time, all the time.
Logistics and governance have become indispensable. The dynamics of data management have changed forever.
*Research from Deloitte as reported by Rimes – Benchmark Data Governance: Understanding the Costs and Risks