Reasons for M&As are numerous and diverse and include:

• Improve profitability
• Increase efficiency
• Automate manual processes
• Shorten time to value
• Enhance business intelligence
• Acquire R&D data
• Expand product portfolio
• Maximize growth
• Innovate core business models
• Mitigate technology disruption
• Implement game-changing strategic moves

While the potential benefits of an M&A are compelling, there are also potential pitfalls lurking that can swallow large amounts of both money and resources. One of these potential pitfalls involves the problems that can occur when attempting to merge two different company’s data infrastructures.

Integrating and consolidating the master data in disparate enterprise systems is one of the most critical, yet costly and time-consuming, challenges that need to be met in an M&A. In part 5 of our LIMS master data best practices series, we will discuss best practices that can help guide the strategy for consolidating and managing LIMS master data in mergers and acquisitions.

Master Data Best Practices for M&As

Any organization undergoing an M&A will be significantly increasing IT infrastructure and the amount of master data that needs to be managed and maintained, as well as the cost of doing so. As such, each application should be analyzed to determine how it aligns with the company’s future-state vision and brings value to the organization over its lifetime in a process known as application portfolio rationalization.

There is also a strong need to aggregate and consolidate data to provide for post-merger operational efficiency and also quick wins for the short term. Merging and harmonizing disparate LIMS and their data into a single functional operating environment is not a simple task and can put enormous strain on a company’s IT department if not planned and executed effectively. Even if you don’t need to merge two separate LIMS into one, master data in your LIMS will need to be adjusted to accommodate new and/or altered workflows. Having a scalable master data plan in place, as we discussed in part 3 of our LIMS Master Data Best Practices series, can help to facilitate this process.

Effective master data management (MDM) during an M&A is an important enabler of everything from business continuity to post-merger innovation. Some of the key aspects of a successful master data management (MDM) strategy for LIMS master data include:

Conduct an Audit of Systems and Data. When a company integrates an acquisition or engages in a merger, the sooner the data integration team is involved, the smoother the integration is likely to be. The first thing that should be done by IT during an M&A is to conduct a full system and data inventory in order to understand and document the current data landscape. Some data challenges to consider and document include:

• Data may be captured, managed and maintained differently
• Data standards may be different
• Data processes, procedures and methods may be different
• Data quality may be different
• Data strategies may be different

An inventory of this nature can be difficult to accomplish if either organization lacks good IT and data governance/documentation This is why it is important to have change control procedures in place A good place to start is to identify business, subject matter and data experts across both organizations and form a data team to research and determine what documentation is available for this initial phase. A few key questions to ask when trying to document the current data landscape include:

• Where is the master data currently located (systems, apps and files)?
• How does data traverse these different systems?
• Who owns data?
• Who manages data?

If a change control procedure (see part 4 of our LIMS Master Data Best Practices series) is in place that includes a data migration plan, the master data plan, and how to conduct reviews and audits, these questions will be easy to answer.

Do Strategic Planning. Once the current data landscape is documented, the next step is mapping out the future state workflows that will determine your master data configuration. Laboratory workflows utilize LIMS master data, and an M&A means workflows will likely need to be altered and new workflows added into the system. The first step in the process is for business analysts to conduct a series of interviews with business stakeholders to document the current state of laboratory business processes, technology and IT architecture in both organizations. In addition, analysts should discuss the merger at a high level with the organization’s management team to understand the goals, aspirations and objectives of the desired future state of the laboratory.

Once the current state is fully documented, the project team will work to create a future state model by defining the goals, workflows and requirements of the desired future-state. If you choose to harmonize and standardize laboratory operations on a new LIMS or a legacy system, the technical, business and user requirements from the strategic planning phase are utilized to guide the technology selection, implementation and integration process.

Map Out the Data Structure. With future state workflows established, the next steps are determining the data fields to be entered into the LIMS, establish naming conventions, and mapping out the data structure. We covered this in detail in part 2 of our LIMS Master Data Best Practices series. In addition, if the Master Data Plan we discussed in part 1 of our series exists for the LIMS that will be used in the new operating environment, the process of mapping out the new data structure will be much easier. Of course, this Master Data Plan will need to be updated to reflect the new operating environment. Once the data structure is mapped, configuration of the LIMS can begin.

Standardize and Migrate Master Data. Master data from the acquired company may need to be standardized before being migrated into the LIMS that will be used for the new unified operating environment. Data migration for a project of this nature can be a significant challenge The Data Migration Plan we discussed in part 4 of our series should guide this process. Master data from the acquired company will likely need to be extracted, translated and loaded into a new location. Questions to ask that help to determine your data migration/management strategy include: How are we going to get master data out of the acquired company’s systems and into the LIMS? How are we going to harmonize data across multiple sites?

Data management/migration for an M&A is typically a big job. It is therefore important to formulate a Master Data Management/Migration Strategy before any migration or LIMS configuration has happened in order to avoid significant time and cost overruns as the project proceeds, as well as minimal disruption to your lab operations during migration activities.

Once the data has been migrated, regulatory requirements mandate that it must be validated to make sure it is accurate and has been transferred properly. This validation should be guided by your Change Control procedures. Even if your company is not in a regulated industry, validating migrated data is important to make sure your data is sound.

Conclusion

Mergers and acquisitions can help drive your competitive advantage, but they can also paralyze a business and generate significant unexpected costs that can serve to minimize the value proposition of the merger. Following the best practice recommendations described in the blog help to establish a single version of the truth in your LIMS, something which is critical to ensuring your laboratory maintains operational efficiency, data integrity, and regulatory compliance. Effective LIMS master data management in an M&A is also important to ensure your laboratory continues to produce the valuable business intelligence that drives innovation and competitive advantage for your organization.

Astrix is a laboratory informatics consulting firm that has been serving the scientific community since 1995. Our experienced professionals help implement innovative solutions that allow organizations to turn data into knowledge, increase organizational efficiency, improve quality and facilitate regulatory compliance. If you have any questions about our service offerings, or if you would like have an initial, no obligations consultation with an Astrix informatics expert to discuss your master data strategy or LIMS implementation project, don’t hesitate to contact us.

About Astrix Technology Group

Scientific resources and technology solutions delivered on demand

Astrix Technology Group is an informatics consulting, professional services and staffing company dedicated to servicing the scientific community for over 20 years.  We shape our clients’ future, combining deep scientific insight with the understanding of how technology and people will impact the scientific industries. Our focus on issues related to value engineered solutions, on demand resource and domain requirements, flexible and scalable operating and business models helps our clients find future value and growth in scientific domains. Whether focused on strategies for Laboratories, IT or Staffing, Astrix has the people, skills and experience to effectively shape client value. We offer highly objective points of view on Enterprise Informatics, Laboratory Operations, Healthcare IT and Scientific Staffing with an emphasis on business and technology, leveraging our deep industry experience.

For More Information

For more information, contact Michael Zachowski, Vice President at Astrix Technology Group, at mzachowski@astrixinc.com.

The post LIMS Master Data Best Practices Part 5 – Mergers & Acquisitions appeared first on Astrix.

LIMS implementations usually demand a substantial investment of time, money and resources, typically costing hundreds of thousands to millions of dollars and requiring hundreds of person days to accomplish. Failure of a LIMS project can be a huge waste of time and resources, and a financial disaster for the organization involved. As such, it is critical to get a LIMS implementation right the first time in order to preserve your return on investment.

One important facet of any successful LIMS implementation and/or migration is the design and configuration of master data. In our experience, many companies involved in LIMS implementations tend to focus on software testing and configuration and put off dealing with master data until the end of the project. This is a huge mistake. Master data design and configuration is typically a much bigger job than anticipated and has multimillion-dollar impacts down the road on things like operational efficiency, time to market and LIMS return on investment (ROI).

In an effort to help organizations understand the importance and implications of master data and avoid project delays and cost overruns, we’ve put together a series of articles to highlight LIMS master data best practices. Some of the topics that will be covered in future articles in this series include:

• Master data configuration pitfalls
• Extrapolation of master data from your current paper records
• Master data naming conventions
• Strategies for handling master data in mergers and acquisitions (M&As)
• Designing your master data for maintainability and scalability
• Evolution of master data and change management
• Master data quality control
• Master data harmonization

In this part 1 article of our LIMS master data series, we’ll define master data and discuss the importance of developing a master data plan for your LIMS implementation. Without further ado, let’s dive into our LIMS master data series!

What is Master Data?

Master data can be thought of as the information that needs to be in place in the LIMS for users to be able to use the system as intended. Master data is core, top-level, non-transactional, static data that will be stored in disparate systems and shared across the enterprise, and possibly even beyond to external partners. As master data establishes a standard definition for business-critical data, its accuracy is very important, because it collectively represents a common point of reference and “single source of truth” for your organization. As such, everyone across the organization must agree on master data definitions, standards, accuracy, and authority. ​

Within most LIMS applications, there are two types of data that come into play – static (defined) and dynamic (transactional data). Dynamic data is the data users enter into the system as part of their daily activities such as test results, samples, batches or lots of a product. The master data is typically the static data that defines the structure of the system.

Master data and dynamic data are connected in the sense that the only way that dynamic data can be created is if master data already exists in the system. For example, in order to record a sample of a product for testing (transactional data) in a LIMS, the product name (master data) must exist in the LIMS so that the sample can be associated with a particular product in the system.

In most LIMS applications, various templates provide the ability to house the master data as lists/tables of values that will be used throughout the system. Master data typically includes core data entities like products, materials, specifications, sample types, analyses, lists, locations, reagents, instruments, environmental monitoring schedules, stability protocol templates and users. That said, universal specifications of master data items are not possible, as different laboratory types and/or LIMS will typically have different objects/entities identified as the master data.

Master data is foundational to business success. Even minor issues with master data can cause significant operational problems, and these problems will only be magnified as the organization scales, or reintroduced anytime new products or facilities are implemented. In order to avoid project delays and cost overruns for a LIMS implementation, it is critical to design and configure the master data properly. Towards this end, every LIMS implementation project should include a comprehensive Master Data Plan to ensure success.

Creating a Master Data Plan

In order to ensure a successful LIMS implementation, it is important to create a well thought out Master Data Plan that includes collecting all the master data that needs to be entered, deciding on a testing strategy to verify that the data has been entered accurately, creating a proper naming convention for your master data, and having an appropriate amount of time scheduled for entering the data into the system and testing it.

A Master Data Plan is a formal document that identifies the following:

• The rational for different aspects of the plan (e.g., why you have a specific naming convention)
• List of the organization’s master data that needs to be put into the system
• Schedule for when specific tasks need to be done
• The place(s) where the master data is created
• The people who will be doing the work of entering and testing the data. Note that these people need to have appropriate training for the job.
• How data is transferred into the system (e.g., the data migration plan)

One of the most important aspects of the Master Data Plan is determining what data needs to go into the system. This will involve scheduling an assessment of your data to determine what needs to be classified as master data, and also the master data entities in the LIMS being implemented to know which ones you will use and how. Note that this assessment may be utilized as an opportunity for you to do some housecleaning on your data. For example, you may decide not to add in master data for any test older than 5 years.

Another important feature of the Plan should be deciding on a naming convention. Here, it is important to get agreement on master data naming conventions amongst your user base so that they will be able to easily search for the data they need. Additionally, in organizations with multiple sites, using naming conventions that allow users to find their site-specific master data is crucial.

In a regulated environment, testing and documentation of testing may need to be included as part of your validation package. Towards this end, it is important that the person who tests the master data be different than the person who creates it. The person who does the testing must also have an understanding of the data they are testing and be trained in both the testing procedure and how the test results need to be documented. In addition, the Master Data Plan should document the procedure for updating master data in the system when necessary.

Conclusion

Your organization’s master data should serve to reduce the cost and time to integrate new facilities and enhance your organization’s flexibility to comply with regulations or enter new markets successfully. Over time, the master data contained in your LIMS will likely expand as your business expands with new products, facilities and regulatory bodies. Efficient master data management (MDM) will thus become critical to your operations. Be sure to tune in for the remaining parts of our master data series, where we will discuss the important best practices necessary to ensure your master data is designed and configured to deliver maximum business value for your organization.

Astrix is a laboratory informatics consulting firm that has been serving the scientific community since 1995. Our experienced professionals help implement innovative solutions that allow organizations to turn data into knowledge, increase organizational efficiency, improve quality and facilitate regulatory compliance. If you have any questions about our service offerings, or if you would like have an initial, no obligations consultation with an Astrix informatics expert to discuss your master data strategy or LIMS implementation project, don’t hesitate to contact us.

The post LIMS Master Data Best Practices Part 1: Defining the Terms appeared first on Astrix.