In today’s digital age, every interaction, transaction, and customer touchpoint generates valuable information. Businesses are collecting information at an unprecedented rate – a trend with no signs of slowing down. IDC forecasts that by 2025, the global datasphere will reach a staggering 163 zettabytes, a tenfold increase from 2016. 

But here’s the catch: the sheer volume of data doesn’t guarantee success. It’s the quality of that data that truly matters.

Research by Gartner indicates that organizations lose an average of $15 million per year due to poor data quality. This translates to billions of dollars wasted on misleading insights borne out of bad data leading to missed opportunities. Beyond day-to-day operations, high-quality data becomes even more critical as companies embrace AI and automation. These powerful tools are only as good as the data they learn from. Feeding an AI algorithm with inaccurate or incomplete data will inevitably lead to unreliable results. 

Challenges With Data Quality 

While organizations are keen to enhance their data quality, they are usually hurdled with the following challenges:

• Increased regulations: The growing number of regulations like GDPR and CCPA pose a significant data quality challenge. These regulations dictate how personal data is handled, requiring organizations to be accountable for its proper management. The challenge lies in incorporating these regulatory requirements into existing data architecture to ensure compliance. This adds another layer of complexity to data governance and ultimately data quality.
  
• Lack of resources: It is often difficult to scale data quality programs running in one business function across the entire organization due to skill gaps. Implementing a robust data quality program requires a skilled team with expertise in data governance, data cleansing, and data analysis. However, these data professionals are often in high demand and can be expensive to hire.
• Inconsistent data: Data gets compartmentalized within different departments, leading to inconsistencies and duplication across the organization. When data isn’t standardized and connected, achieving consistent quality becomes a major hurdle. For example, customer addresses might be recorded in one format by marketing and another by sales. This inconsistency creates confusion and hinders effective data analysis.
• Lack of ownership: Data quality suffers when ownership is unclear. Business users might see data as something IT provides on demand, developing a “not my job” attitude. However, data quality is a shared responsibility. How business users enter, utilize, and manage data directly impacts its quality too.
• Resistance to change: Shifting established data practices can face internal pushback from those accustomed to the status quo. The perceived complexity and lack of immediate ROI associated with data quality initiatives can further compound this resistance.

These data-related or data management challenges highlight the need for a proactive approach to data quality. The good news is that there are concrete steps businesses can take to ensure their data is clean, consistent, and trustworthy. But focusing on just data quality is only half the battle won. Without standardized data governance in place, your data quality can quickly go haywire.

In the following sections, we’ll explore actionable steps to overcome these common hurdles and get the best out of your data.

Actionable Steps to Improve Data Quality

Step 1: Define Goals and Objectives

Before diving into data governance, it’s crucial to understand your organization’s goals – both the big picture strategic vision and the day-to-day tactical objectives. Regulations and market shifts can also influence these goals. A well-defined data strategy considers these factors and identifies the specific data your organization needs to succeed. This strategy goes beyond data itself, outlining key performance indicators (KPIs) and stakeholders, and pinpointing the technology and processes needed to manage your data effectively. Your data strategy is a living document that should be reviewed and updated as your business evolves.

Once you have a clear understanding of your goals and data needs, you can set data governance goals that directly support them. These goals might include improving data quality for better decision-making, or enhancing data security to comply with regulations. This will help you focus on the data that has the biggest impact on your business outcomes, like customer data for improving customer satisfaction.

Step 2: Get Stakeholders Support

Building a successful data governance program requires a team effort. The first key player is an executive sponsor – a champion who understands the value of data governance and is willing to invest in its implementation. This sponsor rallies support and secures resources for the program.

Next comes assembling the data governance team. This cross-functional group should represent different parts of the organization, including IT staff, data architects, line-of-business owners, data custodians (those responsible for data accuracy), and even stakeholders outside your organization, if relevant. Each member brings their unique expertise to the table.

Effective communication is essential for this team’s success. Everyone involved needs to understand the program’s goals and the business value it delivers. This includes establishing a common language around data quality, ensuring everyone agrees on what “good data” looks like. By working together, the data governance team can build a program with clear objectives and broad stakeholder buy-in, ultimately creating a strategic advantage for the organization.

Step 3: Assign Roles and Responsibilities

Assigning clear roles and responsibilities to everyone involved, from data collection to usage is vital. This ensures everyone understands their part and contributes effectively. The structure can vary depending on your organization’s size. Large companies might have a dedicated data governance team (as discussed in Step 2), while smaller businesses might integrate these tasks into existing roles. 

Regardless of the structure, a key element is the data steward. These individuals collaborate with central data and analytics (D&A) teams to develop best practices, establish collaboration frameworks, and define data-related rules and standards. Data stewards are the go-to people for data quality issues, monitoring data assets, investigating problems, and resolving policy violations. Their core responsibilities include monitoring data assets, investigating quality issues, resolving policy violations, and collaborating with stakeholders to define business rules and monitor compliance. They also play a key role in setting data quality best practices within their teams and helping collect and manage metadata, which improves overall data understanding.

Step 4: Assess Current State and Set Target State

Now that you’ve identified your business goals and assembled your data governance team, it’s time to assess your current capabilities. Frameworks like DCAM and CDMA can help you benchmark your data management practices against industry standards. This evaluation will reveal your strengths and weaknesses, highlighting areas that need improvement to achieve your objectives. Remember, you don’t have to tackle everything at once. Focus on the most impactful areas to deliver quick wins and demonstrate the value of data governance.

An essential step is establishing a baseline for data quality. This initial “benchmark” will help you measure progress and justify the investment spent thus far. Remember, data quality requires ongoing effort. Without a sustainable approach, data quality will quickly deteriorate. Focus on how improved data quality can positively impact key metrics like financial performance.

Step 5: Implement your Framework and Processes

After establishing a data governance framework, the real work begins – putting it into action. This involves implementing processes and controls to enforce the framework, like data quality checks or user access restrictions. Setting up data governance processes early on prevents confusion and issues later. It is good to integrate data quality processes directly into your business workflows using data quality tools like DQLabs. Identify frequently occurring data quality issues and embed solutions into workflows. This might involve using new tools, changing existing processes, or even adopting new technologies.

Modern data quality tools are essential for scaling these efforts. Dedicated data quality platforms offer features like automated data assessments, rule management, data monitoring, and data cleansing. They also leverage AI and machine learning to provide valuable insights and automate tasks.

Read: You can also read this blog to know how to leverage AI/ML to improve data quality.

Step 6: Evaluate and Improve Your Approach

Track your program’s effectiveness and adjust your approach as needed to ensure it meets your evolving needs. A successful data governance program is flexible and adaptable. For this, consider a trust-based approach to data governance. Instead of striving for a single “truth,” define different trust levels for your data based on its criticality as suggested by Gartner. This allows for more flexibility and efficient management of your data assets.

An iterative approach is also crucial for success. Focus on addressing the most pressing data quality issues first, even if solutions aren’t perfect. Finally, think of data governance as a continuous cycle of improvement. Engage stakeholders to develop improvement plans, analyze root causes of data issues, and create solutions as regular evaluation helps identify areas for enhancement.

Step 7: Create Data Advocacy Groups

Now that you have successfully created and implemented your data governance program, develop a data savvy workforce to sustain this momentum. 

Don’t let data quality become a finger-pointing game! Everyone plays a role. Break down silos by forming data advocacy groups across departments and IT. These groups, formal or informal, can be a starting point for anyone facing data quality issues. Think of them as data champions, working together to share best practices and identify areas for improvement. Having regular meetings with them can encourage collaboration and knowledge exchange between teams.

Step 8: Provide Data Quality Training Programs

Building a culture of data enthusiasm is crucial for successful data governance. When employees are invested in the idea of leveraging data for better decision-making, they’re more likely to ensure data quality and share it openly. However, fostering this excitement can be challenging. Leading organizations implement change management strategies to cultivate data advocates and address any skepticism.

This might involve senior leaders acting as role models, recognizing high-quality data contributions, and showcasing the value of data through real-world use cases. Additionally, organizations can offer training and career development opportunities in data science, or even host data-centric events to generate excitement. Ultimately, the most effective approach depends on the unique culture of each organization.

Conclusion

While the sheer volume of data offers incredible potential, its true value lies in its quality. Poor data quality can lead to skewed insights, hindering decision-making and hindering business performance. By taking a proactive approach to data governance, organizations can obtain the true value of their data assets.

This article has outlined a comprehensive framework for building a data quality governance program. From defining goals to implementing tools and processes, each step plays a crucial role in ensuring data accuracy, consistency, and accessibility. Also, by fostering a culture of data quality and continuously refining your approach, you can transform your data into a powerful asset that drives informed decision-making and propels your organization towards success.