Finding value in data means extracting meaningful insights from a dataset that can be used to make informed decisions, improve processes, or identify new opportunities. You’re looking for ways of turning your raw data into actionable information that drives business value. 

Most business owners and executives know they need better access to their data to unlock the hidden value in their data. Data accessibility and the proper technical infrastructure to support it are crucial for businesses to fully realize the hidden value within their data. This “data gold” can increase business value in various ways.

Identifying new opportunities for leveraging your data can generate excitement around its pursuit. Once you recognize this value, you can share the data with those who need it, tailored to their preferred formats. Here are some typical examples of high-payoff activities that arise from effective data utilization:

• Data-driven decision making 
  • Where high-quality data is automatically available to anyone who needs it.
  • This supports sound decisions and allows you to build confidence in effectively delegating responsibility and authority for a given business outcome. 
• Improve the customer experience.
  • Customer service and salespeople can have the information they need to provide outstanding customer service and keep customers happy.
• Drive Operation Efficiencies.
  • Data-driven insights can be used to optimize resource allocation and improve business processes.
• Foster an Activity-Based Culture of Excellence.
  • Data can help align your organization with corporate goals and objectives, and it is displayed using dashboards for each department, clearly showing the measurements of success.
  • Managers can then focus on the activities performed by each employee and build incentives for excellent performance.

My intention with this article is to get you thinking about the value of your data. I have yet to see a team that is not excited about better data access, especially once they realize it can now be formatted to meet their needs. Use the examples below as part of a brainstorming session where you gather your managers and ask them what they think would be valuable. Maybe have fun with this, like a prize for the most valuable suggestion since at this point, in using your hidden data,  you may “strike gold”! 

 If you want help with this process or general data guidance, please get in touch with me for details.

Examples of Valuable Data:

Some General Examples:

• Using customer purchase history to personalize marketing campaigns.
• Analyzing website traffic patterns to optimize user experience.
• Identifying trends in sales data to predict future demand. or
• Leveraging sensor data in manufacturing to detect potential equipment failures. , 

Specific Examples Across Industries:

• Retail:
• Targeted promotions: Analyzing customer purchase data to identify which products to promote to specific customer segments based on their buying habits.
• Inventory optimization: Using historical sales data to predict future demand and optimize inventory levels, reducing stockouts and overstocking.
• Healthcare:
• Patient risk stratification: Identifying high-risk patients by analyzing medical records to proactively manage their care.
• Clinical trial analysis: Analyzing patient data from clinical trials to evaluate the efficacy of new treatments.
• Finance:
• Fraud detection: Using transaction data to identify suspicious patterns that may indicate fraudulent activity.
• Investment analysis: Analyzing market trends and company performance data to make informed investment decisions.
• Manufacturing:
• Predictive maintenance: Monitoring sensor data from machinery to predict potential equipment failures before they occur, preventing downtime.
• Process optimization: Analyzing production data to identify areas for improvement and optimize manufacturing processes.
• Marketing:
• Customer segmentation: Grouping customers based on demographics and behaviors to tailor marketing campaigns to specific segments.
• A/B testing: Analyzing data from different versions of marketing materials to determine which performs best.