Data Governance Challenges and
Data Management

Data Governance Challenges & Best Practices

With the proliferation of data, it is imperative for businesses to ensure that the data they have is high quality, secure, and relevant. This is where data governance comes in, but it is not without its challenges.



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Date Published: 5th Jan 2023

16 min read


Subhadeep Bhattacharjee
Content Writer
Subhadeep Bhattacharjee is a content writer who follows the world of technology and business. With a passion for both cutting-edge tech and the fascinating world of business, he is on a mission to explore the intersection of these two worlds and share his opinion. When not poring over the latest advancements in AI, marketing automation, and cybersecurity, he is following cricketing action from around the world.

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Frequently Asked Questions

Data governance is the process of managing data within an organization to ensure its availability, usability, integrity, and security. It involves defining roles, policies, metrics, and standards to govern the use of data, minimizing risks, complying with data regulations, increasing data value, and reducing data management costs.

The common challenges of data governance include:
Absence of Data Strategy: Limited resources may lead to a lack of prioritizing data governance, resulting in the absence of a data strategy.
Poor Data Quality: As data volumes increase, maintaining data quality becomes a challenge, with approximately 30% of data decaying annually. Focusing on data hygiene can improve data governance. Lack of Leadership: Data governance requires leadership to provide direction and ensure the effectiveness of the program.
Siloed Data: Data collected from various sources can become siloed, hindering a holistic view of the market.
Lack of Control: Some organizations lack proper rules and protocols for data access, use, storage, management, and retrieval.
Data Security Issues: Data security ambiguity poses a significant challenge to data governance, as storing data in the cloud alone doesn't guarantee security.

The best practices for data governance include:
Set Up an Organization-Wide Goal: Establish a strategic goal that covers all aspects of the business process, including revenue, growth, risk mitigation, and efficiency.
Corral Data from Different Sources: Collect and consolidate data from various sources and domains to gain a comprehensive view of the market.
Always Focus on The Big Picture: Create synergy between people, processes, and technologies by hiring the right individuals, selecting suitable tools, and defining a well-defined data governance process.
Build Data Governance Framework: Develop clear policies, rules, and roles for data usage, access, quality standards, compliance, and deliverables.
Incorporate Metrics into Data Governance: Use measurement metrics and analytics to objectively track progress and align data governance with organizational goals.
Educate the Team on the Importance of Governance: Promote a culture of data governance within the organization by ensuring all members understand its significance.

Data governance benefits organizations by:
Improving understanding of data and its quality
Enhancing business processes, productivity, and profitability
Enabling the capture of macro and micro market trends
Facilitating compliance with data regulations
Supporting digital transformation and data-driven decision-making

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