Data Modernization

Understanding How Data Governance Mitigates Risks and Accelerates Insights

17 January 2022

Are you sure the data you are using is high quality, valid, and accessible? Good data governance practices are crucial to ensure data sustainability and accessibility in the long term for large, complex, or small datasets.

Organizations today receive an incredible amount of data about their business, operations, performance, customers, suppliers, prospects, and more. An organization will be more successful when the information is properly used to understand market trends and the target audience. A well-framed data governance plan can ensure that your data is trusted, easy to find, and accessible and that it is secure, compliant, and confidential.

Data governance is not optional

As companies grow and scale, they accumulate more data sources and assets daily, for which they need an appropriate big data environment for storage and access. To govern these data sources, integrate and make them available across the organization, clean data architecture is required, which can be achieved through effective data governance principles.

Data governance is a set of policies, standards, processes, and metrics to support enterprise data management to build security, understanding, and trust around an organization’s data among its stakeholders and enable them to achieve company goals. It is essential to an organization’s overall strategy as it helps determine what data you have, where it resides, and how it can be used. Furthermore, it lays the foundation for applying a set of well-defined rules to accelerate analytics and growth initiatives.

Data governance is fundamental

When you establish processes and responsibilities to ensure the quality and security of enterprise data, you can clearly define who can take action on what data using what methods in what situations. A well-crafted data governance strategy is fundamental for organizations to determine what data needs to be controlled and the benefits expected from this effort. For different business areas, you can examine the specific business drivers that are the source of that data to see if they are being managed correctly or produce the data you need.

For instance, if you are a healthcare organization, the business driver for your data governance strategy is patient data. Are you properly managing the retention requirements such as the history of information, who changed the data, why and when to ensure compliance with company and government policies?  

A well-planned data governance framework ensures strategic, tactical, and operational roles related to data are clearly defined, responsibility and accountability are agreed upon across the enterprise.

Reasons why organizations need to govern data

As organizations rely highly on data analytics to streamline operations, optimize performance and drive business decision-making, it is essential to employ a well-tuned data governance program to ensure that data is consistent and trustworthy.

Listed below are the key reasons why organizations should implement a data governance program:

  • Eliminate inconsistent data silos across different departments and business units.
  • Agree on common data definitions to ensure a shared understanding of data.
  • Identify and fix errors in data sets and improve data quality.
  • Improve analytics accuracy and empower decision-makers with reliable data.
  • Enforce policies that prevent data errors, misuse, and exploitation.
  • Ensure compliance with data privacy laws and other regulations.

Data governance is not data management

Oftentimes, people mistake data governance for data management, but the former is the subset of the latter. Data governance is one of the core components of data management, knitting together other disciplines such as data quality, data security, database operations, metadata management, and data warehousing. Data management refers to managing the entire data lifecycle needs of an organization. In contrast, data governance covers strategy, roles, and policies to ensure that the right people are assigned the right data responsibilities.

However, there’s no data management without proper governance as it ensures that the actual data is consistent with a thoughtful plan that considers and controls data quality, security, and compliance. An effective data governance program:

  • defines roles and responsibilities for data authoring, curation, and access
  • defines the master data models
  • details data retention policies

Features of successful data governance

1. End-to-end approach

Data governance ensures that the KPIs and metrics on which you ground your business decisions are accurate and secure data is delivered to end-users. It’s important that you implement an end-to-end data governance framework that encapsulates your entire data landscape from the data warehouse to the business intelligence solution. If you do not govern the process all the way, then you cannot control the end result.  

2. BI and data governance

Before the days of digital transformation and the spread of data growth, governance was required only at the data warehouse side because as the data leaves the data warehouse, nothing altered the data. But modern BI tools like Power BI, Qlik, and Tableau enable users to directly modify data inside the tools, making it necessary to include the BI solution in the data governance framework. That’s the reason why it is important to have end-to-end data governance, including your visualization tools.

3. Automation is the key

You will never achieve 100% coverage with a manual approach to data governance, especially with the reliance on scarce human resources. When you automate your data governance processes, you can trust your data, remove uncertainty, and make better business decisions.

4. Outside-in data governance approach

An approach to data governance begins with a business perspective, looking at your entire data landscape to identify inconsistencies, errors, and objectives and then build your efforts from there. Make sure you align the overarching business objectives with the data governance plan. Other issues, gaps, and targets will follow suit when you focus on these.

5. Test your business intelligence solution

The entire development team tests their code and its results by default but not BI data. It’s not a common practice to test BI data as it is driven from the business side—an entity that is not used to governing or testing their data processes. BI tools are yet to adopt this because when BI users test their solution, they know that all their data is correct and aligned with the data governance framework.  

PreludeSys’s data governance expertise

PreludeSys understands the purpose and importance of data governance—to provide security to an organization’s data among its stakeholders and build trust in the data being used to make decisions. We employ the right data governance approach depending on the enterprise business model and requirements; ensure the data protection policy is in place to prevent data leaks; and secure the necessary data. With many years of expertise in delivering successful data governance solutions to clients across retail, healthcare, banking, and other sectors, we can help address your data governance needs!

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