The amount of data the world produces has grown exponentially in recent years. In 2020, global data production was 64.2 zettabytes. By 2025, it is expected to more than double to 181 zettabytes, according to Statesman. Despite the growing amounts of Big Data, most of the data that organizations produce is underutilized and often stored in silos. Leading organizations are now beginning to ramp up their data governance efforts to better utilize and manage their growing data sets.
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What is data management?
Data governance is defined as a data management discipline which 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 setting up the infrastructure and technology, establishing and maintaining the processes and policies, and identifying the functions responsible for handling and securing data types.
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As cloud migration and digital transformation are embraced by all industries, data governance is becoming a critical component for any organization. Leaders, managers and data teams who work with good data governance practices gain a competitive advantage and excel in performance.
Benefits of powerful data management best practices
The benefits of good data management policies are enormous and include:
- Better staff performance
- Marketing and Sales Improvement
- Demand, supply and stock management
- Supply Chain Management
- Insight and visibility with single-source-of-truth data analytics
- Better risk management and opportunity assessment
- Automation and cost savings
- Transparency and compliance with laws such as: GDPR, CCPA, PCI DSS and HIPAA
Data Management Best Practices
Data management practices are principles that any organization can use to blueprint its data management strategy. Whether your business is new to data management or very advanced, these best practices will keep you up to date, giving you a secure and efficient process to manage your data and get the most out of it .
1. Think big, but start small
Many leading technology companies agree with the principle of thinking big while taking small steps in data governance. Microsoft says you need to document your goals at a high level—create your big picture—but always keep your short, medium, and long-term goals and milestones in mind.
Data governance works with the right people, processes and technology. Start your data management program by working with your existing employees, hiring the right people, and making sure all roles and responsibilities are clearly defined. The right people can define processes and necessary technology investments for a successful data governance program.
After the right people are present, you can continue building your processes. Later, you can bring in the technology you need. A good practice and toolset for data governance only works if there is a clear vision of the goals and objectives of the data governance program.
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 empowers the rest of the workforce to understand what they are working on, visualize the commitment and fulfill the work to achieve the business goals more accurately and efficiently. A good data governance strategy outlines the benefits and exposes the consequences of poor data governance, from lawsuits to profit impact.
All companies must prepare a comprehensive business case for their data management programs. This case should 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 stats
Too many or too few statistics will hinder your ability to understand performance and whether your organization is achieving its data-related goals.
JUICE says that more is not always better. Even when statistics are automated thanks to machine learning or artificial intelligence, it takes time to build and analyze these settings, 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 targeted, more granular way.
4. Ensure transparent communication about data roles and responsibilities
Data management frameworks operate at different levels of an organization, with employees being responsible for different parts of the process. Open, multi-level communication between all employees is essential. Employees must also be fully informed about the program, steps, objectives and processes. Transparency and adapting when things don’t work are important to the long-term effectiveness of a data governance program.
How to implement data governance best practices in your organization
Evaluation of past data management practices
When implementing data management programs and best practices, you should start by assessing the current data management process your organization uses. Even if there isn’t a comprehensive approach to data management, you still need to be able to evaluate how data is collected, stored, managed, and deleted. Assessing the previous data management system also serves to identify shortcomings and new opportunities.
A question leaders should ask themselves at this stage is where data comes from and where data is managed in different business use cases. For example, if you work with European data, your company must comply with the standards of the General Data Protection Regulation. On the other hand, if you work with US customer data, you must comply with federal or even state-to-state regulations.
Industry-specific data regulations must also be taken into account. For example, if your company handles healthcare data in California, you must comply with both the Health Insurance Portability and Accountability Act and the California Consumer Privacy Act.
In these data privacy and compliance scenarios, it is important to examine what procedures already exist to manage specialized data and how those procedures fit into a new data management program. Especially for industries where data governance compliance is complicated, it’s a good idea to consult a data or privacy attorney who can help troubleshoot data governance compliance issues.
Finding the right data professionals and tools for success
After you review past practices and define a data management strategy for moving forward, you can turn your attention to people, processes, and technology. Remember that the people working in your data management framework drive the process forward, so make sure they are the right people for the job and have the necessary tools and resources.
Investments in data governance technology, solutions, tools and resources should be made based on the needs of the program. These can be solutions for data storage, cloud migration, cloud management, on-premises IT infrastructure, advanced security tools, automated analytics, machine learning, AI, and smart dashboards.
Finally, it is critical to continuously assess the effectiveness of your data management program, as internal needs and external factors affect business data. New technologies, approaches, skills, cyber attacks and data laws are constantly evolving. Be ready to adapt and evolve to redirect your efforts and develop a successful data management program that will stand the test of time.
