Data is the lifeblood of modern organizations, and as such, it must be carefully managed and protected.

Whether it's financial data, personal health information, or customer data, organizations that generate and manage data must implement a comprehensive data governance strategy. A robust data governance policy ensures compliance and security and improves the quality of Business Intelligence (BI).

This blog post will explore how a solid data governance strategy can improve data quality, leading to better decision-making, operational efficiency, and cost savings.

Data governance for better insights

A comprehensive data governance strategy is essential for any organization that generates or manages data through its operations. This is especially true for:

• Financial institutions handling sensitive financial and personal data;
• Healthcare organizations that collect and store personal health information ((PHI);
• Government agencies managing large amounts of administrative and regulatory data;
• Retail and e-commerce companies that process considerable amounts of customer data;
• Technology and research organizations that produce proprietary consumer, product, and operational data.

Data governance refers to the policies, procedures, and standards for managing data. An effective governance policy also clearly defines data management roles and responsibilities. It establishes data quality controls and validation processes and ensures that data is protected and secure.

Protecting and securing data is crucial for maintaining trust with customers and stakeholders and preventing costly data breaches. However, compliance and security are not the only benefits of solid controls and practices. A robust data governance strategy can markedly improve the quality of business intelligence (BI).

Business intelligence relies on quality data

Business Intelligence relies on quality data to collect, analyze, and present information to support superior decision-making. BI aims to identify trends, patterns, and opportunities within an organization's data to provide insights that can improve operations, increase profits, and identify other essential or profitable initiatives.

To achieve this, business intelligence employs software and other tools to analyze and visualize data from various sources, such as financial and operational systems, customer data, market research, and social media. Reports, dashboards, and other data-driven presentations are then shared with stakeholders, such as executives, managers, and board members.

Since business intelligence relies on data to generate deductions, the quality and reliability of the information used are crucial. If the data used by business intelligence teams are accurate, it is far more likely that the findings and conclusions they provide will be as well. Therefore, data governance must constitute more than security and compliance measures. A comprehensive approach to data governance includes data organization, standardization, and structuring.

Clean data

Leveraging a layered data governance strategy to cleanse and organize data provides powerful benefits. Executing data cleansing, profiling, and standardization techniques at the entry point for all data removes errors and reduces complications before processing begins.

Data cleansing fixes or removes inaccurate, corrupted, and poorly formatted data and removes duplicate and incomplete data from datasets. This step is crucial when combining multiple data sources, a process known as data blending, as the opportunity for errors increases exponentially during this process.

Profiled data

Data profiling analyzes and creates summaries of data, producing a high-level overview of data and uncovering quality issues early that may otherwise go unnoticed. Providing BI teams with clean data reduces costly delays in creating and delivering insights. Cleaning and profiling data as acquired eliminates multiple groups encountering the same issues with accuracy and quality when accessing the same data.

Standardized data

Organizations should also establish common standards to ensure data is consistently formatted and structured across diverse systems and departments. This formatting can include means for data naming conventions, data types, and data definitions, which can reduce inconsistencies and errors.

Data standardization creates norms for data that may derive from diverse sources. It dictates a consistent format for data, including capitalization, punctuation, non-alphanumeric characters, and more.

Additionally, implementing a data governance strategy can help organizations better manage their data assets by ensuring that the data is accurate, complete, and up-to-date. This process improves data quality, leading to better decision-making, operational efficiency, and cost savings.

Well-organized, easy-to-access, and understandable data is critical for business intelligence. By organizing and structuring information logically and intuitively, organizations make it easier and faster to analyze and draw insights from their data.

Data literacy training & development

Data literacy training and development is a critical component of a comprehensive data governance strategy. Organizations can enhance their data literacy skills by providing regular training programs to all employees and underscore the importance of maintaining high-quality data. This approach fosters a data-driven environment that aligns each stakeholder group interacting with data, from producer to user.

In this context, a dynamic governance team plays a vital role in leading the education and development of new and existing team members. They can create training programs that improve data literacy, communication, and critical thinking skills. The training programs can also help employees understand how to use data to solve business problems, which leads to better decision-making, increased operational efficiency, and growth opportunities.

Moreover, data governance teams can promote bottom-up fluency and data quality, which supports BI's endeavors to construct meaningful analysis. This approach ensures that employees have a deep understanding of the data they work with and can use it effectively to drive business growth

Conclusion

Implementing a comprehensive data governance strategy is essential for improving the quality of business intelligence. BI relies on high-quality data to generate accurate insights, identify trends and opportunities, and provide valuable insights for decision-making. However, if the data used by BI teams are inaccurate, incomplete, or poorly formatted, it is more likely that the findings and conclusions they provide will be as well.

Using clean, organized, and structured data, businesses can produce meaningful analysis supporting superior decision-making. To achieve this, organizations must implement policies, procedures, and standards for handling data, and establish clear roles and responsibilities for data management.

Data governance policies should also incorporate data quality controls and validation processes to ensure data accuracy, completeness, and consistency. Without proper data governance, the accuracy and reliability of BI reports could be compromised, leading to faulty conclusions and ineffective decision-making.