The Case for Data Governance

Data governance

Data is a corporate asset but is still often treated as a waste product. Many organizations lack the controls necessary to maintain data integrity and provide insight into business operations. Left unchecked, systemic risks perpetuate, constraining business growth.

Today, business missteps are costly and could be catastrophic. Guessing is unacceptable. With increased regulatory scrutiny, market unpredictability, insatiable internal demand for answers to increasingly complex questions, and shareholder demand for better returns and greater traceability to financial results, organizations need better data stewardship.

Data governance is about establishing data management disciplines to address these challenges.

Why is managing data such a challenge?

Poor data management impairs an organization’s ability to conduct business; that is, effectively manage customers, deliver timely product services, and grow profitably. More seriously, the lack of appropriate data controls impacts regulatory compliance, including Basel, Solvency, Anti Money Laundering, and KYC. Data management challenges can manifest themselves in different ways including:

  • Unclear stewardship of data and accountability to remediate systems risks
  • Redundant, inaccurate data and proliferated across disparate data silos
  • Misaligned initiatives as a result of uncoordinated funding
  • Missed performance targets
  • An inability to adapt to the accelerating rate of market and regulatory change
  • Inappropriate use of technology to manage data across the enterprise
  • Political and organizational imbalance

What evidence exists to support these observations?

A recent survey of 200 organizations by Kalido indicated a number of significant market trends:

  • 54% have no means to measure the performance of their data management activities, and 76% have no framework to assign business value
  • 49% do not manage master data anywhere but in their disparate applications
  • 22% maintain a rigid boundary between the business and IT
  • 75% of business users have no insight into who or what has produced the data fueling their business processes
  • 88% felt that data quality was either poor or narrowly managed in an ad hoc manner
  • While nearly 70% have policies and rules, they don’t have the structure around them to drive behavioral change

In an independent recent survey published, 36% of the financial institutions surveyed, the IT department assumes responsibility for data and 16% admitted that none had data ownership specific responsibility identified. And, in 2010 the Data Governance Institute survey indicated that only 29 % of respondents measure the monetary cost of poor data quality, and only 23% of successful programs had the following characteristics:

1) A data governance mission statement;

2) A clear and documented process for resolving disputes;

3) Good policies for controlling access to business data;

4) Effective logical models for key business data domains;

5) Either business processes defined at a high level or fully documented at several levels and available for data governance;

6) Data quality assessments that were undertaken on a regular basis; and,

7) A link between program objectives and team or personal objectives

What should organizations do?

To remediate these challenges, I recommend the following:

  • Business Case – Establish quantifiable business value to the necessary transformation required to improve information management practices
  • Solution Blueprint – Establish a holistic view of the information requirements and performance measures required by each business process and stakeholder
  • Data Management – Identify how best to organize, maintain and sustain the underlying data to achieve the information management capabilities defined in the solution blueprint
  • Risk Management – Necessarily, improvements will mean change. Identify the risk mitigation strategies to address change and adoption as solutions are deployed
  • Operating Model – Formalize the cross-functional teams and clearly identify the decision-making authorities to steward data transformation projects
  • Technology and Automation – Select the appropriate tools for both the business and IT to mitigate system risks, manage common definitions, and reduce ineffective, expensive manual intervention
  • Continuous Improvement – Recognize that change is inherently part of each asset and process. Institute regular and frequent updates to optimize the approach, deliverables, organization, resources, and skills

With the increasing market and regulatory volatility, Financial Services companies are finding it much harder to improve data integrity across their business operations. Leading organizations are employing these strategies to create a more “active” data governance framework.

 

Author:

Chethan Laxman, Digital Transformation Executive at Infostretch

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