Imagine you and a bunch of your colleagues are attending a networking event. You’ve been assigned three booths spread out in different locations across the arena. You and one of your colleagues take booth 1, and your other colleagues split themselves between booths 2 and 3. The event is flooded with professionals from your field, and soon, people start flocking into your booth – Booth 1. They’re impressed with your services and say they want to pursue business opportunities with you. As a result, you start taking down the prospect details in the following format.

The event ends, and you are thrilled to have received a promising number of leads with good revenue potential. The next day, while at work, you notice that the details collected from the three booths are not uniform. While some parameters are the same, others are different, and some are missing. With inconsistent prospect information, selling services will be a challenge. But this inconsistency could have been mitigated had you and your colleagues discussed before the conference the specific details that you would collect from the prospects.

Juxtapose this with your enterprise data – It is a multi-faceted, multi-layered, complex asset that also serves as a critical tool for enterprise agility and innovation. When utilized right, it can help businesses optimize performance, drive better outputs, and improve business practices. But for this to happen, the data must be available, of sufficient quality, uniform in nature, well understood, and with no signs of inconsistency. 

While Data Management strategies and solutions can help create a single source of truth for all enterprise data, Data Governance provides the foundational principles of how data should be collected, assessed, cataloged, accessed, and maintained. Enterprise data that does not align with these common principles will have less business relevance and eventually lead to bad decisions and outcomes. For organizations wanting to optimize results, it is imperative to adopt strong Data Governance practices in combination with the efficient uses of Data Governance tooling.

The enterprise value derived from Data Governance initiatives can be difficult to assess and quantify, but organizations must attach their Data Governance efforts to real business value and/or initiatives. Data Governance must be seen as an enterprise imperative that is ultimately owned and driven by the business, not as a large, expensive IT initiative. 

So, what are some key goals a sound Data Governance strategy achieves for your organization? Here’s a breakdown:

1. Data Quality

More and more organizations are turning to data analytics to improve productivity and drive better results. In such cases, inconsistent, low-quality, and error-laden data is not ideal for deep analytics. In fact, it’s counterproductive and ends up increasing costs, decreasing productivity and the quality of the results. Clear-cut Data Governance best practices demand the ability to assess data quality, grade it against common quality standards, and implement processes to remediate data quality issues at the source. As a result, data trust and reliability are improved, leading to better downstream analytics and business outcomes.

2. Uniform Data Language

An organization is typically made up of multiple functional teams and/or business units that are organized around overlapping responsibilities. Many organizations struggle with data inconsistencies that arise from how different functional teams create and use data. For example, the North American marketing team thinks about “Customers” very differently from how a European Accounts Payable department thinks about Customers. Aligning data generated from multiple divergent and overlapping teams requires consistent data policies and processes. To get data providers and consumers on the same page, a unifying Data Governance framework is essential to speak the same data language. So, enterprise-wide data consistency is necessary to eliminate any unnecessary disruptions and enhance data value. 

3. Indisputable Sources of Data

An indisputable data source can power business decisions with predictable outcomes. Analysts who are hard at work, trying to identify meaningful patterns and insights in enterprise data, can’t be held back by incoherent, untrustworthy data. Your enterprise needs Data Governance to ensure data is in harmony with the best practices and digital ethics laid down in the framework. Without the presence of such policies, your data runs the risk of losing authenticity and producing unreliable business insights. 

4. Data Contextualization

Any data that does not provide business-relevant context, fails to deliver value in driving outcomes. Contextual data presents critical background information related to an event, person, or entity. Providing context to data allows data to become more meaningful, as a specific event can be associated with a specific cause or outcome. Data Governance defines terminologies and can expose additional data relationships that help enterprises increase the contextualization of their core data. When one data point can be tied to many unique data points to understand the full picture, the power of contextual data is found. 

5. Data Security and Regulatory Compliance 

Enterprises with strong Data Governance programs also get to enjoy the benefit of increased levels of data security and auditability. A good Data Governance framework will give an enterprise the right tools to catalog their key enterprise data, trace changes and movement of data, record usage history, and restrict unauthorized access to sensitive data. By placing controls on data, Data Governance reduces the risk of security breaches and protects your data and reputation. This becomes increasingly important in a world where regulatory compliance is increasingly complex, and the financial risk of compliance is correspondingly escalating. A good Data Governance program allows an enterprise to meet these ever-changing security and regulatory requirements and protects a company’s valuable data assets.

Data Governance is required to efficiently collect, store, and use enterprise data. Without it, data will be inconsistent, results will vary, and companies will leave themselves open to potential legal exposure. A Data Governance strategy designed and supported at the enterprise level, combined with a well-executed program to implement proper processes and tools, is a proven way to protect data and extract enriched business value from it. Implementing components such as data glossaries, catalogs, tracking data lineage, and applying privacy controls are just some initiatives that make up a good Data Governance framework. Beyond that, early organizational change is imperative to manage and govern data effectively. Business leadership must participate and steer successful Data Governance programs, ensuring that individuals within the organization understand the need for best practice adoption in Data Governance.