Untapped Business Value: Why Significant Portion of Data Remains Unused? 

The exponential growth of global data generation is undeniable. Consider this: every single day, we exchange 350 billion emails¹ and conduct 8.5 billion searches² on Google alone. For businesses, data is fuel for innovation, operational efficiency, and exceptional customer service. Insights-driven companies, as noted by Forrester, are not only 23 times more likely to acquire customers but also 19 times more likely to achieve profitability.³

Despite the recognition of data’s value, a significant portion remains untapped within organizations. This underutilization limits the full potential of data infrastructure investments, as many employees struggle to understand how to access and use existing data.

In this article, we explore the challenges companies face in data utilization and how organizations can improve the current situation to empower more employees to use data insights for informed decision-making.

1. Leadership Commitment

To fully capitalize on data, leadership must be committed to investing in data governance and quality. Establishing a solid data foundation is critical, as poor-quality data and fragmented sources can severely slow down data utilization efforts. Leaders must not only invest in robust infrastructure but also model and promote data-driven decision-making throughout the organization.

Steps to Increase Leadership Commitment:

• Showcase Success: Present case studies where data has directly improved outcomes.
• Strategic Alignment: Ensure that data initiatives are linked to strategic business goals and prioritize them accordingly.
• Model Data-Driven Behavior: Encourage leaders to integrate data into their decision-making processes and highlight its importance in discussions.

2. Access to Data

A common difficulty employees face is accessing the data they need. Often, they are unaware of where to find data, how to access it, or even what data is available. Conflicting priorities between IT security and operations further complicate this issue. While operational teams seek easy data access to analyze and optimize their processes, IT departments are focused on ensuring data access is secure and following proper data modeling guidelines. Strict access controls and security measures, though necessary, can discourage widespread use of enterprise data.

According to a Deloitte study, 67% of managers and executives stated they were not comfortable accessing or using data from their analytics tools. Whether it’s a matter of technology fit or insufficient training, the use of these tools shouldn’t be limited to analysts and data scientists alone.⁴

Strategies to Improve Data Accessibility:

• User-Friendly Tools: Implement self-service tools that simplify data access for non-technical users, boosting decision-making efficiency.
• Centralized Data Catalog: Create a comprehensive data catalog with clear descriptions and access instructions.
• Automated Access Control: Use automated workflows for role-based data access to streamline the process without compromising security.

3. Trust in Data

Access alone isn’t enough—ensuring data quality is equally crucial. According to a Gartner report, poor data quality costs organizations an average of USD 12.9 million each year.⁵ Only 20% of business executives completely trust the data they get.⁶ Raw data often contains errors or inconsistencies, requiring thorough processing and cleaning before it can be utilized effectively. Identifying areas where data quality needs improvement is essential, allowing targeted actions to enhance the data in those specific locations.

Actions to Increase Trust in Data:

• Centralized Clean Data Storage: Provide access to pre-processed, clean data for self-service analytics.
• Data Profiling: Regularly profile data to identify and address quality issues such as anomalies, outliers, or inconsistent formats.
• Prioritize Quality Improvements: Focus on improving the quality of critical data elements that have the most significant impact on business decisions.

4. Competence Development

Extracting value from data often requires specialized skills in data engineering and visualization, leading companies to rely on external consultants. Organizations that implement self-service tools achieve 30% faster decision making compared to those relying on traditional BI tools, as noted by Gartner.⁷

Ways to Develop Competence:

• In-House Expertise: Offer training programs for employees to build data-related skills internally.
• Leverage Automation Tools: Utilize automated tools that simplify data tasks, reducing the need for specialized skills and speeding up the process of extracting insights from data.
• Build Self-Service Processes: Develop processes that allow non-technical users to independently access and analyze data without IT intervention. The process of gathering data, analyzing it, and generating reports was complex and time-consuming.

One of the most notable examples of leveraging self-service analytics on a large scale is Coca-Cola Bottling. Prior to implementing self-service analytics, the company faced significant challenges with data silos, manual updates, scattered systems, and a heavy reliance on IT for generating reports. These issues led to delays and inefficiencies in decision-making. By adopting self-service analytics, business users gained access to automated tools and actionable insights, enabling faster, more informed decisions, reducing dependency on IT, and achieving cost savings. This transformation also improved performance tracking across sales, supply chain, and operations, enhancing overall efficiency and expanding data analysis capabilities throughout the company. The adoption began as a pilot in a single department and gradually expanded to more departments, eventually reaching the entire company. Today, sales representatives can access real-time data anytime, anywhere, which helps them better serve customers and increase sales.⁸

This case highlights how self-service analytics can democratize data access, empower employees, and drive significant business improvements.

In Conclusion

With the accelerating pace of digitalization, organizations are generating more data than ever before. To fully leverage this data, companies must focus on extracting value through cost reduction or innovation.

Enhancing data utilization involves securing leadership support, improving access, and ensuring data quality. This approach empowers non-technical employees to make informed decisions quickly and cost-effectively.

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7. Gartner Magic Quadrant for Analytics and BI Platforms 
8. Coca-Cola Overcomes Challenges to Seize BI Opportunities

About the Author

Anastasiia Glebova is an analytics professional with over a decade of expertise in analytics and process excellence in global industrial companies such as KONE, Konecranes, and Wärtsiä. She specializes in transforming data into actionable insights. A pioneer in establishing process mining practices from the ground up, she has been successfully driving initiatives to optimize processes and enhance operational efficiency.

In her free time, Anastasiia is a sports and outdoor enthusiast, often found running and exploring new natural landscapes.

Tune into her presentation at this year’s Data Innovation Summit for more in-depth insights! In her talk, Anastasiia sheds light on process mining and the path to operational excellence.