How to Get the Most Out of Historical Business Data

As businesses expand, new challenges arise that come with that growth. One of the biggest challenges that growing companies face is how to handle historical data. What data should you keep, and where should you store it? How can you use it to benefit your company? What do you do with it when you migrate to a new software system? Most company leaders have faced these and other questions.

overlay of data showing review of historical business data

In this blog, we’re going to cover how you can get the most out of your historical business data. We’ll cover types of historical data, ways it can be used, benefits of using it, best practices, and migration tips. Let’s explore how to harness the power of historical data!

Types of Historical Data 

Historical business data is a treasure trove of information, often holding the secrets to understanding past performance, recognizing trends, and predicting future success. Here are some types of historical business data and their potential uses:

  1. Compliance and Legal Records: Historical data on regulatory compliance, legal disputes, and intellectual property records inform risk management and ensure legal compliance.
  2. Technology and IT Records: Historical IT infrastructure data, software usage, and system performance logs help manage technology investments and upgrades.
  3. Operational Data: Records of business processes, production schedules, and maintenance logs help optimize operations and improve efficiency.
  4. Financial Records: Income statements, balance sheets, cash flow statements, and tax records can help you understand the financial health of your business.
  5. Sales Data: Historical sales reports, revenue figures, and customer transaction records can help identify trends, seasonality, and customer preferences.
  6. Inventory Records: Data on stock levels, inventory turnover rates, and product movement aids in managing supply chain efficiency and demand forecasting.
  7. Employee Records: Information on hiring, retention, payroll, and performance evaluations helps in workforce planning and management.
  8. Customer Data: Historical customer databases including demographics, purchase history, and customer feedback are crucial for marketing and customer relationship management (CRM).
  9. Market Research: Historical market analysis reports, competitive analysis, and consumer behavior studies can guide strategic planning and market positioning.
  10. Marketing Data: Past marketing campaigns, advertising spend, and ROI analysis help assess the effectiveness of marketing strategies.

Understanding and analyzing this data can provide valuable insights that drive informed decision-making and future business strategies.

Benefits of Using Historical Data

We often focus on the importance of real-time data when it comes to business insights and reporting. While real-time information is crucial to make data-driven decisions, to make the best decisions possible, you also need historical data. For example, if you receive inventory and sales numbers for a given month, that’s good information, but it would be even better to be able to compare those numbers to the numbers from the same time period last year.

Historical data could also help your sales team or your AI-powered website suggest items to customers based on past search and purchase history. Information about past billing and payment cycles could help you predict whether a customer is likely to pay on time.

Ultimately, historical data is about seeing trends and patterns, not just snapshots in time. Trends and patterns are what allow us—or AI tools—to predict future behavior. This matters because predictive analytics are one of the most valuable capabilities a business can have when it’s time to make important decisions.

Best Practices for Historical Data Management 

Of course, every industry and even every business will have different historical data management requirements and constraints. But there are some principles that cross many industries when it comes to historical data management:

  • Let specific use cases drive your choices: “We should save historical data” doesn’t really work as a strategy. Before you choose a method for storing historical data, ask yourself: How much data do we need to store? Who will access this data, how, and how often? What types of analysis would be most helpful in this situation?
  • Use a tiered storage strategy: Invest your storage resources in the information that truly matters. What information will be accessed the most? What information is most useful to you when it comes to data analysis and predictive analytics? Invest your resources and time accordingly.
  • Remain aware of compliance and regulations: Certain businesses are required to store certain types of historical data for a particular period of time. And some historical data may be sensitive information that needs extra layers of security.
  • Automate where possible: If it fits in your budget, automated archival of certain documents can be a huge time saver. Automated processes reduce human error and save employees hours of time.
  • Save to the cloud where possible: Unfortunately, historical data can take up a huge amount of space in your company’s software and servers. Save your historical data to the cloud whenever possible in order to alleviate some of the strain on your software and hardware.

If you’re not sure where to start, try consulting an expert who has experience navigating historical data management and the software that goes along with it.

Migrating Historical Data Between Systems 

Believe it or not, storage isn’t actually the biggest challenge that historical data presents to a business. Migrating historical data between systems can be even more difficult. Whether you’re moving to a new ERP, implementing a new document management system, or something else, migrating historical data is a complex process that can fail in countless ways.

While there’s no way to prevent every error in data migration, a good migration strategy can mitigate some of the problems that may arise. Here are four phases of the migration process that you’ll need to consider as you plan out your migration project:

  1. Analysis: Before you get into the details of your new system, pause to analyze your existing historical data. Go back to the questions listed above: what data do you need, and who needs it when? Which data should be prioritized? Create overviews of business rules, workflows, and more to understand exactly what currently happens to your historical data and any changes you need to make.
  2. Mapping: Next, your data analysts (or your project partner’s data analysts) need to create a systematic correspondence between data fields and elements of the old system and those of the new system. Make sure that this process is done by experienced data analysts who know the systems well.
  3. Transformation: Data must be taken out of the old system and adjusted in ways that make it compatible with the new system. This may involve cleaning out the data, removing duplicates, enriching the data, validating the data, or adding metadata.
  4. Loading: Lastly, the new data must be loaded into the new system and extensively tested. Be sure that every use case is accounted for and verified as working.

Looking for a Data Migration Partner?

Because data storage and migration is such a complex process, it’s worth it to invest in an experienced, reliable project partner—like Laminin! We specialize in working with Epicor Enterprise historical data and connecting it to our clients’ current systems.

Check out our recent customer success story to find out more about what a typical migration project looks like with us. If you want to learn more about how we can help you with your unique project, contact us today! We’re excited to speak with you.