Morning in the Lab of the Future—A Company-wide Data Management Strategy

Morning in the lab of the future: A company-wide data management strategy

It’s early spring, 2023. The company-wide informatics upgrades you committed to in 2022 after reading this blog post are now implemented and validated. This morning, the technicians in your lab will produce reports on a larger volume of samples and more complex tests than ever before. Management will get those reports in a more visually appealing format. Your inventory and workflows are planned into the next fiscal year. Your lab is more efficient than you dreamed it could be, as are all the other labs across your company. Decision-makers up and down the value chain have actionable insights from the full range of data available to them; data pulled from every relevant source throughout the company.

If that introduction resonates with you, it’s because it’s designed to. It’s an example of a powerful persuasive tactic. If you’d like to get your company to the potential future imagined in that introduction, this blog post will help you understand the path in front of you.

A new year always brings on the desire for improvements and more comprehensive approaches to lab data management, but we want you to think beyond the lab to the entire organization. If you plan to implement new systems that will provide new ways to access, use, and share data, it is worth considering what the impact of that data will be on up-and downstream efforts within or across the organization. Think about how this data will support broader questions that might lead to cost savings, new or improved products, and streamlined standard operating procedures. Integrating all of your data streams enables faster responses to product issues, deeper analyses of customer activities, and even improved procurement and billing efforts.

A Data First Approach

It is possible to achieve seamless data acquisition and meaningful reporting from all of the systems in your company’s operating environment, not only in the lab but also companywide. This is the future that CSols Inc. is envisioning for our clients.

How Data Management Contributes to the Bottom Line

PCS STARLIMS Empower 3 Integration Data Sharing

The information that comes out of your lab and the company as a whole is just as important as the data that comes in. It should be recognized that there is an opportunity for continuous value to be gained from the data, throughout the organization. In a recent project at a polymer manufacturer, integrating the in-process sampling via a custom C# interface between the distributed control system, the STARLIMS SDMS, and the Empower CDS increased manufacturing efficiency and decreased the number of support tickets. The client can now test samples at different points in the manufacturing process, depending on the batch being produced. With this kind of integration, any organization could experience improvements that would be most meaningful to their unique setting. A contract manufacturing organization, for example, could tie their customer management system with the procurement system, the LIMS/ELN, the enterprise resource planning tool, and the business intelligence reporting system. With a holistic view of your company’s data, there would be many opportunities to make improvements to the bottom line. Tighter integration between data streams also ensures regulatory compliance and improves data integrity.

Pooling Your Lab Data in a Company-wide Data Management Strategy

Combining your lab data with data available in other areas of your company provides several competitive advantages. Data pulled from all areas of your company enables opportunities for all of the following:

  • Trend spotting
  • Gaining efficiencies or removing redundancies
  • Improving transparency of your data for your customers
  • Sourcing future product ideas
  • Preserving intellectual capital

It’s important to recognize, though, that before you can pool your data you need a solid foundation in the form of data flow. In order to access your data, you need to properly prepare it. You will need to work with various data owners who have probably developed varying rules for their data structures.

Preparing the data to go into a single pool will take significant time to develop a common structure and labeling scheme. Depending on the industry in which your company works, there may be privacy and security requirements around who can access certain data that will need to be met (think HIPAA or GDPR). Creating an organization data flow requires upfront work and will consume a majority of the total project time. Putting in this time will help ensure that your data lake doesn’t turn into a data swamp!

Google and Amazon Web Services, among others, have data labeling and structuring services to help with preparation. The Pistoia Alliance, GO FAIR, and Analytical Information Markup Language (AniML) have tools for data standardization that can be adopted for your purposes. Biopharmaceutical companies were among the first to leverage data lakes to drive innovation and drug development, but today the majority of companies recognize the business intelligence value of data lakes and data warehouses.

What Robust Data Management Can Tell Your Company

Deploying your company’s data in a data lake or warehouse will foster better business decisions and actions. The ability to easily query and present your data not only provides insights about the known unknowns but also potentially uncovers the unknown unknowns. For example, you might not know how or if seasonal changes affect processes in your batch reactors. You might not realize that the environmental health staff aren’t checking for mouse nests in the insulation around your spray dryers. If your contract research organization is using a project-focused data stream you might find it difficult to gather the full history and scope of a client’s work, which you would have if you were using a client-focused data stream.

Taking an Agile approach to data management promotes quick wins to keep stakeholders engaged as you work on the known unknowns and will let users test and refine processes to arrive at an optimal strategy for addressing all of your unknowns. An Agile approach also helps with staying on top of regulatory and compliance requirements because you can make incremental changes as you work.

What your company gains from a data management strategy clearly depends on what is put in as the data. Taking the time to identify critical data and prepare that data carefully up front, with useful structure and relevant labels, will address your known unknowns, possibly uncover some unknown unknowns, and make your data much more useful in the long run.

Are you ready to have the lab of the future envisioned at the beginning of this blog post and to attain an organization-wide streamlined data management system? That goal is attainable, and it’s one that CSols has the expertise to help you achieve.

If you still have questions about what could be achieved by pooling your lab data with all of the other data available in your company, comment below and let us know what you’re wondering about.

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