10 data governance challenges that can sink data operations
No organization can successfully use data without data governance. Address 10 data governance challenges to avoid financial loss, derailed operations and reputation damage.
Data governance isn't a luxury -- it's a core requirement to conduct business effectively.
Data governance provides a framework that specifies how to collect, transform, store and use data. In today's data-driven world, it makes or breaks an organization, especially in its attempts to use AI and analytics in business operations. Organizations must maximize data use and compete in the marketplace, but can't risk having compromised data. Nor can they afford to make critical business decisions based on inconsistent, inaccurate or incomplete data.
Implementing a governance strategy is difficult. Data control and protection are constantly evolving as data volume and complexity grow.The following 10 challenges provide a foundation for companies to understand what they're up against when trying to protect their data assets.
1. Poor data quality and consistency
Data quality is one of the biggest challenges for every data-driven operation. When an organization fails to maintain data quality, users can't trust, locate or use the data. Substandard data can be inaccurate, inconsistent, irrelevant or redundant. Ultimately, unreliable data leads to poor insights and decision-making, operational inefficiencies and unnecessary expenditures.
Improving data quality can be a significant undertaking, depending on the following factors:
Amount and type of data.
Data location.
Resource availability for data management.
Existing efforts and tools to use and manage the data.
The current management strategy.
If the data is inventoried or catalogued.
Implement a data management strategy to improve data quality. This strategy should include data profiling and classification, and require tools and technologies that provide full lifecycle management. Automate and improve operations wherever possible. Always monitor data systems and perform regular data audits with quality as a key strategic goal.
2. Lack of personnel understanding
Organizations are often overwhelmed by the amount of data they collect and generate. Many people assume that IT owns and governs the data. However, data governance is an organization-wide effort that relies on many players, each with specific roles and responsibilities.
Factors that contribute to the lack of ownership and sense of shared responsibility include:
Absence of assigned data stewards accountable for upkeep.
Lack of clear communication and documentation that specifies how to work with the data.
Lack of cross-functional collaboration across departments and teams.
Little to no training and education on how to work with data.
Many of these issues occur because the organization has no governance program or has failed to establish a culture of governance. As a result, data is often mismanaged or misunderstood, furthering the confusion, miscommunication and overall mistrust of the data.
Everyone who works with data, from IT teams to anyone creating the occasional Excel spreadsheet, must understand governance's importance.
To address these issues, organizations must create and emphasize a culture of governance. Make it clear that governance is a shared responsibility, not solely an IT operation. Clearly define roles and responsibilities, and have appropriate business groups must take ownership of the data that they generate, collect and use. Assign data stewards, and provide employees with necessary governance education and training.
3. Lack of leadership support
Data governance strategies will fail if they are not backed by strong support and leadership. Executive support is crucial, but leadership often fails to recognize data governance's importance. This can result in inadequate resources and funding, making it difficult to move forward with an effective strategy.
A successful data governance strategy needs to follow six key steps to reduce risk.
Data governance ensures data's integrity, availability, security and usability. The lack of a carefully defined, comprehensive governance strategy can cost an organization more than implementing and maintaining one.
Without this understanding, organizations risk:
Substandard data, including data errors.
Missed financial opportunities.
Integration issues.
Operational overhead.
Compromised data.
Compliance violations.
4. Unclear policies and procedures
An effective data governance strategy relies on clearly defined policies and procedures. They require careful planning, documentation and distribution to those who need to see and understand them. However, the people who own and work with data might find the policies confusing, incomplete or contradictory -- assuming they see the policies at all.
Defining clear, concise policies and procedures is daunting because of data's innate complexity. It's distributed, managed and used in multiple ways across the organization. Policies and procedures must take this into account to integrate smoothly into existing business processes. Even if stakeholders accept the policies, it can be challenging to get everyone to adhere to them.
Properly communicating the policies and procedures is essential to the success of a governance program. Key stakeholders should help create a program that everyone understands. Know that policy integration into existing operations takes time. However, it's worth it to avoid poor data quality and gain meaningful insights to make informed business decisions.
After implementation, regularly review policies and procedures. Regular updates are critical to meeting changing business requirements.
5. Siloed data operations
Siloed data is a common problem in many organizations, especially larger ones. While data governance is an organization-wide effort, individual departments might take a different approach to storing and maintaining it. This can lead to individual governance strategies, which are seldom consistent with each other.
Any organization hoping to implement a comprehensive governance strategy must tackle siloed data. First, they must understand why their data is in silos.
Multiple factors can contribute to data silos, including the following:
Difficultly tracking and maintaining massive amounts of data.
Unstructured data from a wide range of sources.
Data ecosystems relying on legacy technologies.
Improperly maintained data due to a lack of necessary tools.
Lack of effective communication and cross-functional collaboration between teams.
Governance teams should develop a detailed plan specifying how they'll roll out the strategy and measure each stage's success. At the same time, leadership should foster a culture encouraging communication, collaboration and education.
6. Budget and resource constraints
Some organizations are reluctant to invest resources in governance, failing to see the long-term advantages and risks of not implementing a strategy. The total cost of such an endeavor is difficult to forecast, given the dynamic nature of data. It requires a significant investment in time, tools, technologies and people to work.
Budget and resource constraints can lead to a half-hearted attempt at governance, or worse, no attempt at all. However, leadership must prioritize governance when allocating resources. A data governance initiative might seem out of reach for an organization with limited resources, but it is possible. In this case, organizations should take small steps as part of a larger, carefully orchestrated effort.
The greatest challenge is acquiring personnel and tools. Many organizations also face a skill shortage and struggle to find talent. Employees are already overburdened and have little time to learn new skills or take on additional responsibilities.
Cross-training personnel where practical and implementing self-service capabilities to share responsibilities can maximize efficiency. Automate operations whenever possible, though some cases warrant hiring temporary consultants. Track and analyze resource use and regularly create detailed reports.
7. Slow adjustment periods
Organizations face many challenges in implementing and maintaining a long-term governance program. Difficult adjustments are inevitable due to new technologies and market fluctuations affecting operational environments. Governance teams must adapt to changes quickly and efficiently. Otherwise, it's difficult to sustain meaningful governance.
Untimely responses occur for several reasons, including the following.
Data and user increases. The types and amounts of data or the number of users might increase at unprecedented rates. Incorporate ongoing monitoring and reporting capabilities that enable the organization to address new circumstances as they arise.
No comprehensive change management plan. A flexible change management strategy is crucial. This plan will enable teams to address unusual circumstances and accommodate new technologies or changing operations.
Improper infrastructure. An organization might not have the infrastructure and systems in place to adequately scale to meet shifting demands. Address scalability limitations by adopting modern technologies that withstand operational shifts.
8. Changing regulatory requirements
Organizations must comply with multiple data regulations in the regions where they do business. However, constant updates to old regulations or the creation of new ones can be difficult to apply to a large and diverse data ecosystem. As such, it's not uncommon for organizations to fall out of compliance due to the regulations' intricacy.
Failure to comply with applicable regulations can result in hefty penalties and legal consequences. If sensitive data is compromised, an organization could face lawsuits, exorbitant costs, a hit to the bottom line and a tarnished reputation from which it might never recover. Some regulations to be aware of include the EU's GDPR and the US's CCPA, among others.
Implementing regulation changes into an organization's systems can be a considerable undertaking. Sweeping changes are often announced far in advance, giving organizations time to gather the resources to handle them. This includes ensuring data governance programs are adaptable enough to adhere to current and new regulations.
9. Privacy and security concerns
Data protection is a fundamental pillar of a governance strategy. Safeguarding sensitive and private data from internal and external threats is critical.
To know which data assets to protect and how to protect them, understand the following reasons why compromised data occurs:
Not implementing proper access controls, exposing sensitive data.
Users lack the training and education to exercise caution and maintain awareness.
Working with outside vendors.
Improper system monitoring.
Complex operations and workflows make data protection challenging for IT and security teams. However, organizations should deploy governance plans that cover all aspects of security and privacy.
Complete the following steps to enhance data security:
Implement proper access controls. Make sure that only authorized users can access the data they need. There are many types of access controls, including zero-trust and multi-factor authentication.
Implement basic security measures. Basic security processes include inventorying their data assets, encrypting the data and regularly patching and updating their systems.
Employ continuous monitoring. This will help organizations of what data they have and where it is. Perform regular audits as part of this process.
10. Failing to understand the importance of governance
Everyone who works with data, from IT teams to anyone creating the occasional Excel spreadsheet, must understand governance's importance. Convincing skeptics has never been more important.
Many simply don't understand the long-term business value of a comprehensive data governance strategy. Most times, this is due to an overall lack of strategic vision recognizing data as an asset. Proponents might also face resistance to change that permeates every level of the organization.
Building a data governance team requires representatives from all levels of an organization.
Establishing a data governance team empowers a specific group to educate decision-makers and those who work with the data on the importance of governance. The team must provide realistic goals and timelines so stakeholders know what to expect. This will help create the cooperative and willing environment a successful governance program requires.
Robert Sheldon is a freelance technology writer. He has written numerous books, articles and training materials on a wide range of topics, including big data, generative AI, 5D memory crystals, the dark web and the 11th dimension.
