Key Strategies for Effective Data Governance in a Siloed Data Environment

Data governance within financial services institutions has largely been guided by regulations. Motivations for governance programs have centered around not running afoul of regulations as opposed to extracting business value or improving operational efficiency. Although not determinative, regulators have considerable influence over the activities and initiatives pursued by organizations.

Historically, many financial institutions leveraged a mixture of teams, including consolidated and report specific,to manage data sets and the production of reports. Invariably, this has led to “turf wars” concerning ownership and management of data, general accountability, how data is used, who is responsible for quality assurance, etc. In short, people are managing data and creating reports in siloed worlds to make decisions.

Managing disparate data across an organization can be daunting. Regulators require the tracking and management of countless data points. Organizations must ask themselves

“How are we going to derive value out

of the management of this data”

Traditionally, organizations have used a centralized group to manage data. However, this approach has typically not been effective due to:

Messengers (those that deliver the data/reports) have limited or no vested interest in gathering the data.

Many decisionmakers will choose not to use the data. Instead, they opt for “what they know” about the situation at hand, believing they have an accurate and full understanding of the landscape necessary to make decisions.

Those individuals that create the reports do not own the data and may lack accountability for its integrity.

To reduce maintenance costs and generate value from the data, organizations must shift the framework with which they view data governance. Although traditional frameworks are valuable, the changing governance, regulatory and economic landscapes require a new, more innovative approach and strategies to data governance.

Strategies for Effective Data Governance

Improving Data Governance in Siloed Environments

Shift from a “Data Creator” to “Data Consumer”-centric Model.

  • Data consumers are using elements to make decisions. However, while they have a general understanding of what the element is, they tend to lack insight concerning how their usage differs from others, the quality of information inherent in the element, and what other information others are using to make decisions.
  • As mentioned the our most recent installment, centrally defining Key Business Elements (KBEs) is difficult and not likely to be achieved due to elements being used differently across the organization.
  • To mitigate inconsistencies in use, decisionmakers should be responsible for defining and leveraging KBEs appropriately. Transitioning to a data consumer-centric governance model generates a vested interest in decision-making through improved consumption and data quality.

Shift Ownership of the Governance to the Business

  • Most governance aspects are within the purview of the CIO or CTO while the production of the data heavily exists outside the realm of IT.
  • Many times, IT is aware of what data is being collected, but lacks insight into what data is being used and for what purpose. Often, IT spends its time maintaining “the beast” of systems and applications perpetuating the disconnect between the decision-making process of the business and data governance activity.
  • Shifting governance to the business necessitates a reprioritization of the level of information collected to facilitate decision-making. Instead of collecting everything, the focus becomes collecting what is important as identified by decisionmakers.
  • Accountability for knowing where the information comes from and its quality should lie with decisionmakers.

Institute Arbiters

  • Uncertainties or misalignment in data sets or reports will arise. When they do, who decides the data should match and that the governance in place is adequate? Enter the arbiter, a third-party leadership level resource responsible for ensuring decisionmakers are engaged and are owning data governance.
  • When disputes arise, the role of the arbiter is to weigh all sides, evaluating whether the data goes together, by bringing together the owners of the decisions.

Managing data governance across a siloed organization can be an extremely difficult and complex undertaking – riddled with pitfalls and obstacles. Maintaining what has worked well in the past while adopting a new strategic framework, fundamentally changing the dynamics and accountability of those involved, has the potential to dramatically improve the effectiveness of your data governance and data management strategies across your enterprise.

For more information about how MCG can help solve your data challenges, contact us today at

2018-11-19T10:59:42-04:00 November 12th, 2018|Data Analytics|0 Comments