Data Governance, Taking Control of Your Data in 2022

Bhavika Unnadkat, Head Of Data Governance And Knowledge Management, Energyaustralia

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Bhavika Unnadkat, Head Of Data Governance And Knowledge Management, Energyaustralia

Data governance is a requirement in today's fast-moving and highly competitive enterprise environment. Now that organizations can capture massive amounts of diverse internal and external data, they need a discipline to maximize their value, manage risks, and reduce cost.

We have entered the era of the information economy, where data has become the most critical asset of every organization. Data-driven strategies are now competitive imperative to succeed in every industry. To support business objectives such as revenue growth, profitability,
and customer satisfaction, organizations are increasingly relying on data to make decisions. Data-driven decision-making is at the heart of your digital transformation initiatives.

But to provide the business with the data it needs to fuel digital transformation, organizations must solve two problems at the same time. The data must be timely, because digital transformation is all about speed and accelerating time to market — whether that is providing real-time answers for business teams or delivering personalized customer experiences. However, most companies are behind the curve when it comes to delivering technology initiatives quickly. But while speed is critical, it is not enough. For data to enable effective decision-making and deliver remarkable customer experiences, organizations need data they can trust. This is also a major challenge for organizations. Being able to trust your data is about remaining on the right side of regulation and customer confidence, and it is about having the right people using the right data to make the right decisions. And this too is a major challenge for organizations. According to the Harvard Business Review, on average, 47% of data records are created with critical errors that impact work.

Data governance is not only about control and data protection; it is also about enablement and crowd sourcing insights. Data governance is a requirement in today’s fast-moving and highly competitive enterprise environment. Now that organizations can capture massive amounts of diverse internal and external data, they need the discipline to maximize that data’s value, manage its risks, and reduce the cost of its management. Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals. It establishes the processes and responsibilities that provide the quality and security of the data used across a business. Data governance defines who can take what action upon what data, in which situations, and using what methods.

A well-crafted data governance strategy is fundamental for any organization that works with data. It underpins how your business benefits from consistent, standard processes and responsibilities. Business drivers highlight what data needs to be carefully controlled in your data governance strategy and the benefits expected from this effort. This strategy becomes the basis of your data governance framework. For example, if a business driver for your data governance strategy is to ensure the privacy of health care-related data, patient data will need to be securely managed as it flows through your business. Retention requirements (e.g., history of who changed what information and when) will need to be defined to ensure compliance with relevant government requirements, such as the GDPR and the CCPA. Data governance ensures that roles related to data are clearly defined and that responsibility and accountability are agreed upon across the enterprise. A well-planned data governance framework covers strategic, tactical, and operational roles and responsibilities.

An effective data governance strategy provides so many crucial benefits to your organization that it is hard to live without one. These benefits include:

• A common understanding of data:

Data governance offers a consistent view of, and common terminology for, data, while individual business units retain appropriate flexibility.

• Improved quality of data:

Data governance creates a plan that ensures data accuracy, completeness, and consistency.

• A data map::

Data governance provides an advanced ability to understand the location of all data related to critical entities, which is necessary for data integration.

Data governance is not only about controlled data protection; it is also about enablement and crowd sourcing insights

Like a GPS that can represent a physical landscape and help people find their way in unknown territory, data governance makes data assets usable and easier to connect with business outcomes.

• A 360-degree view of each customer and other business entities:

Data governance establishes a framework so an organization can agree on “a single version of the truth” for critical business entities. The organization can then create an appropriate level of consistency across entities and business activities.

• Consistent compliance:

Data governance provides a platform for meeting the demands of government regulations, such as the EU General Data Protection Regulation (GDPR), the CCPA (the California Consumer Protection Act), the US HIPAA (Health Insurance Portability and Accountability Act), and industry requirements, such as PCI DSS (Payment Card Industry Data Security Standards).

• Improved data management:

Data governance brings a human dimension into a highly automated, data-driven world. It establishes codes of conduct and best practices in data management, making sure that the concerns and needs beyond traditional data and technology areas — including areas such as legal, security, and compliance — are addressed consistently.

• Easy access:

A data governance framework ensures data is trusted, well-documented, and easy to find within your organization, and that it is kept secure, compliant, and confidential.

Data Governance when implemented correctly results in data assets that are less cluttered, processes and procedures that leave no doubt about who is in charge of what, and superior data analytics that can cope with the demands of big data and data silos in every point and business unit across an organization.