Data is an invaluable asset to any organization. As various industries undergo digital transformation, businesses have relied heavily on data to drive decision-making. However, not all data is created equally.
According to Forbes, 80% of data analytics adoptions will affect a business's capabilities. Operating with poor data quality can bring disastrous consequences, such as low revenue and faulty business decisions due to overly complex data ecosystems.
Fortunately, obtaining good data quality doesn't have to be a daunting task. By implementing effective data quality practices, businesses can achieve accurate insights, reduce operational costs, and increase overall efficiency.
In this piece, we will discuss the top 10 ways to improve data quality in your organization.
1. Understand How Data Quality Impacts Business Decisions
The first step to enhancing data quality is to look for connections between data assets, business processes, and key performance indicators (KPIs). This involves closely examining existing data quality issues and how they may affect revenue and KPIs.
Once the linkage has been established, you can build a targeted data quality improvement program that outlines the scope, identifies stakeholders, and defines a high-level investment plan. By investing in a strategic improvement program, businesses can effectively prioritize their resources and maximize the value of their data assets.
2. Determine Appropriate Data Quality Standards
Defining an appropriate standard for data quality is key to improving data quality across an organization. It's important to understand what works best for a specific business, as different sectors using the same data may have varying expectations and standards.
Having periodic discussions with your stakeholders can help capture these expectations. This ensures your data quality improvement program aligns with the organization's unique needs.
3. Implement Data Quality Standards Across the Organization
Establishing a data quality standard helps businesses maintain consistent data quality across all business units. As stakeholders within a company may have different levels of business sensitivity, maturity, and culture, the rate at which these requirements are met may vary.
For this reason, taking a holistic approach to standardization that considers all those involved in the enterprise's data ecosystem can account for these differences.
By implementing an enterprise-wide standard, involved parties can execute their operations according to the defined and agreed-upon requirements. This helps streamline adoption as stakeholders can be educated on these standards and how they apply to their roles.
4. Utilize Data Profiling Early and Often
Data profiling is the process of investigating and summarizing data from an existing source. This step helps you identify corrective data quality actions and provides valuable insights to inform your improvement program.
Profiling lets you pinpoint data quality issues that must be addressed at the source and which can be fixed later. For maximum efficiency, it should be performed regularly depending on the availability of resources and the occurrence of errors. This enables businesses to stay on top of their data quality issues and continuously improve their database.
Data profiling may reveal that a large amount of sales data is missing key information on customer demographics and purchase history – which could directly impact sales forecasting and analysis. By addressing this issue and filling in the missing information, sales teams can accurately target customers with the right products or services.
5. Monitor Key Data Assets with Quality Dashboards
Data quality dashboards provide organizations with a comprehensive view of key data assets. These dashboards can help you spot data quality trends and patterns to inform future operational improvements.
Furthermore, these could be used to monitor and compare data performance over time for different business processes, helping stakeholders gauge the trustworthiness of their data. Overall, data quality dashboards can be tailored to meet a company's specific needs so it can better understand how to improve data quality.
6. Shift from a Truth-Based to Trust-Based Model
When conducting research, it’s important to recognize that data assets are often acquired from external sources where the rules, authorship, and governance are unknown – which is why a "trust model" is preferred over a "truth model."
This means organizations should consider their data's origin, jurisdiction, and governance rather than assuming it's always absolute. A trust-based model can improve data quality and ensure that decision-making is based on accurate and reliable information.
7. Incorporate Data Quality in Governance Board Meetings
To make your data quality initiatives successful, aligning them with business outcomes is crucial. Doing so allows you to easily monitor data quality investments against the company's objectives.
However, it's not enough to implement these initiatives. The board also needs to be informed of the impact data quality improvements are making in terms of business and revenue. Only with clear and frequent updates can the board make informed decisions and support the organization's data quality improvement efforts.
8. Define Responsibilities and Procedures for Data Stewards
A data steward plays an essential role in maintaining the quality and fitness-for-purpose of an organization's data assets, including the metadata. They are responsible for monitoring, controlling, and escalating data quality issues as they occur while also championing good data management practices.
Incorporating this role into your data and analytics strategy ensures data quality is measured and maintained systematically. This includes creating a governance scope and stakeholder map that explains how data quality issues are managed and how the steward interacts with stakeholders to address these issues.
9. Form Special Interest Groups
Establishing a data quality interest group comprised of representatives from different business units, IT, and the office of the chief data officer can significantly improve data quality across the organization. This collaborative effort can advance risk management and create more opportunities to reduce operational costs.
By sharing and implementing best practices, the group can encourage growth and innovation. With a dedicated team working towards data quality improvement, organizations can effectively prioritize initiatives and streamline their efforts for maximum impact.
10. Introduce Data Quality Review
Integrating a data quality review as a release management "stage-gate" is crucial for any organization that wants to gather accurate and reliable data. Regularly reviewing and updating progress lets you conduct timely corrections and checks before moving to the next stage of the improvement process.
Additionally, identifying and circulating the most impactful practices ensures that everyone in the organization has the necessary knowledge and tools to maintain high data quality standards. Ultimately, a data quality review can enhance the overall efficiency and effectiveness of data management processes.
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