The volume of data that the world produces has risen exponentially in recent years. In 2020, global data production was 64.2 zettabytes. By 2025, it is expected to more than double, reaching 181 zettabytes, according to Statista. Despite growing volumes of Big Data, most data that organizations produce is not leveraged and is often siloed. Leading organizations are now starting to step up their data governance efforts in order to better utilize and manage their expanding data sets.
What is data governance?
Data governance is defined as a data management discipline that focuses on implementing policies and procedures to ensure an organization’s data is accurate throughout its lifecycle — from input to storage, manipulation, access and deletion. This includes establishing the infrastructure and technology, setting up and maintaining the processes and policies, and identifying the positions responsible for handling and safeguarding data types.
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As cloud migration and digital transformation are embraced by all sectors, data governance becomes a critical component for every organization. Leaders, managers and data teams that work with good data governance practices gain a competitive advantage and excel in performance.
Benefits of strong data governance best practices
The benefits of good data governance policies are vast and include:
- Better workforce performance
- Marketing and sales enhancement
- Demand, supply and inventory management
- Supply chain management
- Insight and visibility with single-source-of-truth data analytics
- Better risk management and opportunity assessment
- Automation and cost reductions
- Transparency and compliance with laws such as GDPR, CCPA, PCI DSS and HIPAA
Data governance best practices
Data governance practices are principles that any organization can use to blueprint its data management strategy. Whether your company is new to data governance or very advanced in the game, these best practices ensure you stay up to date, providing you with a safe and efficient process to manage your data and get the most out of it.
1. Think big picture, but start small
Many leading technology companies agree with the principle of thinking big while taking small steps into data governance. Microsoft says you should document your high-level goals — draw up your big picture — but always keep in mind your short, mid, and long-term objectives and milestones.
Data governance works with the right people, processes and technology. Start your data governance program by working with your existing employees, hiring the right people, and ensuring all roles and responsibilities are clearly defined. The right people can define processes and needed technology investments for a successful data governance program.
After the right people are in place, move on to building out your processes. Later, you can bring in the technology you need. A good data governance practice and toolset will not work until a clear vision of the data governance program’s goals and objectives is in place.
2. Build a solid business case
Building a solid business case for data governance goes beyond getting buy-in from top management and C-level executives. This business case also allows the rest of the workforce to understand what they are working on, visualize the commitment and fulfill the work to reach company goals more accurately and efficiently. A good data governance strategy outlines benefits and exposes the consequences of poor data governance, from lawsuits to impacts on the bottom line.
All businesses should build a comprehensive business case for their data governance programs. This case must include a project description, benefits, impacts, goals and milestones to measure progress and success. The case should also include an agile version of the goals and program that can adapt over time.
3. Select and focus on the right metrics
Too many or too few metrics will hinder your ability to understand performance and whether your organization is reaching its data-related goals.
SAP says that more is not always better. Even when metrics are automated thanks to machine learning or artificial intelligence, these setups will take time to build and analyze, and models can drift and need to be corrected. To keep your organization productive with effective data sets, it’s important to identify the right metrics and tools to measure your data in a focused, more granular way.
4. Engage in transparent communication about data roles and responsibilities
Data governance frameworks operate throughout different levels of an organization, with workers responsible for different parts of the process. Open multi-level communication between all workers is essential. Workers must also be fully briefed on the program, steps, objectives and processes. Transparency and adapting when things are not working are important to the long-term efficacy of a data governance program.
How to implement data governance best practices in your organization
Evaluating past data management practices
When implementing data governance programs and best practices, you should start by assessing the current data management process that your organization uses. Even if there is no comprehensive data management approach, you should still be able to evaluate how data is gathered, stored, managed and deleted. Assessing the previous data management system also serves to identify deficits and new areas of opportunity.
One question leaders need to ask themselves at this stage is where data comes from and where data is managed in different business use cases. For example, if you are working with European data, your company must meet the standards of the General Data Protection Regulation. On the other hand, if you are working with U.S. customer data, you must meet federal regulations or even state-by-state regulations.
There is also the need to consider industry-specific data regulations. For example, if your company works with healthcare data in California, you need to comply with both the Health Insurance Portability and Accountability Act as well as the California Consumer Privacy Act.
In these data privacy and compliance scenarios, it’s important to examine what procedures are already in place to manage specialized data and how those procedures will fit into a new data governance program. Especially for industries in which data governance compliance is complicated, it’s a good idea to consult a data or privacy lawyer who can help to resolve any data governance compliance issues.
Finding the right data professionals and tools for success
Once you have reviewed past practices and determined a data governance strategy for moving forward, you can turn your focus to people, processes and technology. Remember, the people working in your data governance framework will drive the process forward, so make sure they are the right people for the job and have the necessary tools and resources.
Investment in data governance technology, solutions, tools and resources should be made based on the needs of the program. These can include solutions to address data storage, cloud migration, cloud management, on-premises IT infrastructure, advanced security tools, automated analytics, machine learning, AI and smart dashboards.
Finally, it’s crucial to continually assess the efficacy of your data governance program as internal needs and external factors impact company data. New technologies, approaches, skilling, cyber attacks and data laws are constantly in flux. Be ready to adapt and evolve to redirect your efforts and drive a successful data governance program that stands the test of time.