Data Governance Best Practices
With the rise of big data, more companies are recognizing the benefits that well-managed data can provide them with, as well as how much data is potentially available to them. Collecting that data and figuring out how to use it is not always easy though, especially as the volume of data to which a company has access grows.
That’s where data governance comes in. Following best practices for data governance can help businesses fully take advantage of the benefits data can provide.
Data Governance: An Overview
What is data governance? Essentially, it’s the management of data in an organization. It’s a system of decision rights, accountabilities and processes aimed at improving the quality, availability, usability and security of an organization’s data. Typically, it includes a governing body, well-defined procedures and a plan for implementing those procedures. The specifics will differ based on the needs of the organization.
A primary goal of data governance is improving the quality of an organization’s data. As data becomes more integral to a company’s success, data quality is becoming increasingly important. Today’s organizations can use data to make business decisions, improve profitability, optimize operations and create new products. If that data isn’t high-quality, these efforts will not be successful.
Any organization that handles data can benefit from having a data governance framework in place. The need for such a system increases as the volume of the organization’s data and the complexity of its data management rises.
In the early days of data governance, most organizations employed manual documentation using spreadsheets and other such methods. As the volume of data increased and technologies improved, businesses began using more advanced data governance tools available from vendors. Many of today’s tools use machine learning, automation, recommendation engines, cloud computing and other technologies.
Steps of Creating a Data Governance Framework
There are three main steps involved in creating a data governance framework:
1. Designating data stewards:
To create a data governance framework, an organization must define the people who will be responsible for the data — the owners and custodians of its data assets. The organization must decide which teams will handle which data. These teams or individuals are called data stewards.
2. Define standards and procedures:
The organization will then establish standards and procedures for how the organization will manage its data, including storage, archiving, backup schedules and security. These procedures will include rules for how authorized personnel should use data. They’ll also include controls and audit procedures to ensure compliance with the organization’s data policies as well as applicable government regulations.
3. Implement policies and procedures:
Once the organization has defined its data stewards and created an overall strategy for managing its data, the various governance teams will decide how to implement the policies and procedures. This includes choosing the technologies that will be used to manage the data. The company should regularly review, update and improve its data governance framework.
Data Governance and SMBs
A principal goal of data governance is ensuring that data meets the needs of the organization. It also aims to resolve issues related to data, reduce the costs of managing it and position data as a highly valued asset within the organization. To meet the needs of an organization, the framework must be tailored to its capabilities, available resources and needs.
For this reason, the frameworks used may look different across sectors and various sizes and types of businesses. As compared to large corporations, small and medium-sized businesses (SMBs) will likely assign roles to fewer individuals and combine more positions. Their overall system will also likely be less complex than that of a larger organization which handles more data from more varied sources.
Best Practices for Data Governance
The way you approach data governance will vary from organization to organization, but there are some best practices. Here are five things to focus on when getting started with data governance:
1. 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.
2. 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.
3. 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 you’re framework is fulfilling its objectives. Some commonly used metrics include:
- 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 an SMB 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.
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.
5. 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.
What’s the Future of Data Governance?
Data governance has evolved considerably over the years, as the volume and value of data have increased. Management practices are becoming increasingly automated and tool-based as opposed to manual. Data is also becoming more and more integral in business’ operations. Data governance is continuing to evolve. What might the future of data look like?
1. Increased Volume
The volume of data has increased substantially in recent years, and it’s expected to continue to grow. The International Data Corporation (IDC) forecasted in a recent report that global data would expand from 33 Zettabytes (ZB) in 2018 to reach 175 ZB by 2025.
As the amount of available data increases, it will become more important for businesses to adopt systems for managing it. They’ll also need to put more effort into deciding what data will help them meet their goals and what information they should discard. The methods used for organizing data will also become increasingly important as it will enable companies to make sense of their data.
2. Increased Alignment With Business Goals
As data management continues to evolve, it will become more integral to business’ operations. Data governance will continue you to move away from being an IT-controlled area to become better aligned with business goals. Companies will continue to recognize the value data can have for them and incorporate it more seamlessly into their daily activities.
As data governance moves to the forefront, IT and the business will have the same goals, and IT will cease to be the data gatekeeper. This will also enable data governance to become a more consumer-focused initiative.
The recent advent of the General Data Protection Regulation (GDPR) has significantly changed how companies handle data. GDPR went into force in May 2018 and applies to all companies that deal with the personal data of EU citizens. It aims to give consumers more control over their personal information.
Data governance policies and data management systems can help companies comply with GDPR. As more companies seek compliance with the regulation, they’ll turn to data governance. It’s likely that other similar laws will start to emerge in other countries around the world too.
4. Consumer Focus
In the future, consumers will likely take a more active role in managing their personal data, in part because of regulations like GDPR. They’ll have more control over the data they share. This could improve the quality of data but will change data management from a top-down approach to one that’s more focused on the consumer.
Over the years, data governance technologies have become more advanced. Today, many parts of the process are automated and data management tools from vendors help to collect, organize, analyze and activate data. In the future, we will continue to see the role of artificial intelligence, machine learning and other such technologies in data governance expand. Further into the future, we may see new uses for these technologies emerge as well as entirely new technologies.
It’s essential for businesses to plan for the future as part of their data governance strategy. Companies should consider their potential future needs, as well as changes in regulations, technologies and the way companies manage data.
Contact Consolidated Technologies, Inc., Today
As data becomes both more plentiful and more valuable, the need for data governance increases. It allows businesses to improve the quality of their data, keep it secure and leverage it to help them achieve their business goals. With the proper planning, communication and technology, businesses can improve the way they manage their data and take full advantage of all the opportunities it presents.
To learn more about data governance and discover how we can help your business leverage your data, contact Consolidated Technologies, Inc. today. From our comprehensive managed IT services to data networking to cloud computing, we have the solutions you need to help your business succeed.