This first post in a two-part series provides an overview of the practice of data governance. In part two, we’ll review a list of top data governance vendors and tools.
Best Practices to Support an Enterprise Data Governance (aka Metadata Management) Program
Data is the lifeblood of every enterprise organization. Therefore, companies must ensure that the data used in their business processes is consistent and trustworthy. This is critical as more organizations rely on data to make business decisions, optimize operations, create new products and services, and improve profitability. The formalized process of caring for data is known as data governance (DG).
What is data governance?
The Data Governance Institute says data governance is “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” Here’s a more straightforward definition: “Data governance is the overall management of the availability, usability, integrity and security of data used in an enterprise.”
Data governance includes the people, processes and technologies needed to manage and protect the company’s data assets in order to guarantee generally understandable, correct, complete, trustworthy, secure and discoverable corporate data. Typically, DG includes a governing body, well-defined procedures and a plan for implementing those procedures.
What does a data governance program include?
A solid DG program establishes internal policies for data use in order to minimize risk and to better position the organization to implement and meet compliance requirements, such as for HIPAA, CCPA or GDPR. A good program can increase the value of the data by defining how and when it can be used for different business purposes, and by making it available to the appropriate users. For example, digital transformation and data readiness are top priorities for large enterprises striving to deliver more agile business models based on data transparency, data standardization, predictive analytics and high-quality data sets.
What is data stewardship and what are data stewards accountable for?
A major objective of data governance is to assure data quality in terms of accuracy, accessibility, consistency, completeness and updating. Thus, a DG program necessitates the appointment of one or more stewards who are accountable for various portions of the data. Large enterprises often appoint teams of data stewards to guide the data governance implementation. Data stewards work with individuals throughout the organization to help ensure data use conforms to a company’s data governance policies.
What are data governance goals and best practices?
One of the goals of data governance is to ensure that data meets the needs of the organization. Other goals include resolving issues related to data, reducing the costs of managing it, and positioning data as a highly valued asset within the organization. There is much work to be done by everyone involved. While each company may take its own approach to data governance, here are a few best practices from the consulting firm, Consolidated Technologies that have helped many organizations through the process over the years.
- Identify Benefits and Opportunities – Focusing on the benefits that data governance provides can help you in creating your data governance strategy and help motivate people within the organization to improve how they manage data. When beginning to develop your data policies, take a look at your current practices and opportunities that improving them could provide. You can then develop your strategy around taking advantage of those opportunities. Implementing a significant change within an organization is challenging, and having buy-in from others in the company is critical for success. Identifying the potential benefits of data governance can help get buy-in from upper-level management, which is necessary for launching such an initiative. You also need buy-in from others who handle data at all levels of the organization. When people understand the reason for implementing a change, they may be more motivated to do the work needed to make it. Some of the benefits of data governance include improved data quality, better decision-making, enhanced operational efficiency, regulatory compliance and increased revenue.
- Start Small – Data governance requires participation across your entire organization and can involve complex systems, numerous groups of people or large amounts of information. Getting started with data governance can be intimidating. Starting small can help and may, in the end, lead to better results. Although your overall goal in your data governance is large, it’s advisable to start with just one business area or data issue and expand from there. Break your larger overall program down into smaller steps for a better chance at success. Starting with one area makes the organizational change more manageable. It allows you to test out ideas and processes to determine what works best. When you move to the next area after your initial roll-out, your process will be more refined and therefore more efficient and cost-effective.
- Measure Progress – Measuring the success of your data governance framework through the use of metrics is critical for meeting your data goals. It helps you to ensure that you’re on the right path with your data management and helps you determine what parts of your strategy are working well and what parts you should change. Metrics are also essential for demonstrating the benefits that a data governance framework has for a company. The kinds of metrics you should measure depends on your goals. Choose metrics that help you determine if your framework is fulfilling its objectives.
|What Data Governance Goals Should You Measure?|
|Data Governance Metric||Definition|
|Data quality scores||You can measure the quality of your data according to its completeness, accuracy and timeliness. Measuring data quality in the same way across the organization will make your data quality metrics more useful.|
|Adoption rates||For a data management strategy to be successful, you need people to implement it. The rates at which people within your organization are complying with your standards and procedures can help you determine if your system is working.|
|Number of risk events||Bad data management can result in inaccurate decisions, lost clients and fines from regulators. Data loss and cybersecurity incidents can be especially costly. In fact, downtime caused by data losses can cost many thousands of dollars each day. Data governance aims to reduce the frequency and severity of these events. Analyzing these events over time will tell you if your system is succeeding in this.|
|Data rectification costs||Data governance aims to fix bad data as early in the process as possible or prevent it altogether. Fixing bad data comes with costs, especially when the problem has existed for longer. Data governance should reduce data rectification costs over time.|
- Communicate – Data governance is about data, but it’s also about people. You need strong internal communication for a data governance plan to work. Communication plays a role in every stage of creating and implementing a data governance strategy. As part of creating your data governance framework, you should also establish a strategy for communicating about it. Early in the process, you need to convey the benefits of data governance to get buy-in. Communicating the successes of the strategy through the use of metrics can help cement buy-in and keep people motivated to participate. It’s also essential that the group in charge of the implementation clearly communicates what the roles of each participant in the data management strategy will be. Each participant should have a clear understanding of what their goal is and the guidelines they should follow in accomplishing their goal. As you assess your strategy, you’ll also need to communicate about any changes you have to make to it. Those affected by the changes should understand why they’re making them and how to do so.Without proper communication, misunderstandings and lack of buy-in can cause problems in implementing a data strategy. With strong communication, however, you have a much higher chance of success.
- Make It Continual – An essential aspect of data governance is that it’s a practice, not a project that you set aside once it’s finished. Data governance doesn’t have an end date like a typical project does. Instead, it requires fundamental changes to the way a business operates. People within the organization will need to incorporate the standards and procedures into the way they do their jobs for data governance to be successful. You’ll also need to make decisions about how to handle data as needs change, data volume increases or you start gathering new types of data. Your standards and policies can guide these decisions, but you’ll need to make them in real time. It’s also critical to periodically review your data management policies and strategies, evaluate their effectiveness and make any changes needed to improve them. This requires keeping track of metrics to determine what works well and what does not for your organization.
Source: Consolidated Technologies, Inc.
In part two of this blog post, we’ll dive into the data governance market and provide a list of top data governance vendors and tools.