Lab Test Information Management: Challenges & Way Forward

In an ever more competitive landscape, Testing Laboratories need to address their challenges by focusing on the core drivers via a cohesive information management strategy.

Lab Test Information Management: Challenges

Testing Laboratories in the TIC industry have grown aggressively, both inorganically via acquisitions and organically, while simultaneously striving to maintain and grow margins. This growth model has brought with it significant, and I must say, inevitable challenges.

Some of the key challenges are listed below:

  1. Lack of standardization – Variability in core information and operational execution platform across lab locations and regions, including those driven by client specific customization.
  2. Ad-Hoc and inefficient processes – Redundant and excessive manual verification steps have been introduced at every stage throughout the operations.
  3. Ageing delivery platforms – The industry continues to rely heavily on human capital and has a lot of catching up to do in terms of technology adoption. Full automation in Lab data capture, auto report generation, B2B customer intimacy platforms are dreams that continue to elude even the most innovative, market leading service providers.
  4. Declining Margins – Given an increasingly competitive landscape and increasing input costs coupled with stagnant pricing (or even increased pricing pressures), margins are increasingly coming under pressure. This deserves to be mentioned separately as a Challenge on its own as it is one of the single most critical metric on which management performance is measured today.
  5. Revenue Leakage – This is a direct and inevitable result of lack of traceability in lab information management, all the way from the Test Request Form (TRF) being submitted to the corresponding invoice being issued. Labs all over the world end up under-invoicing. While the percentage loss in revenue varies from lab to lab, it does happen in every lab nevertheless.
  6. Impatient, Unhappy Customers – Let’s not forget the customer in all this. Customers want faster time to market and are unhappy with the Turn Around Time (TAT) delays they face from the TIC service provider. The list of impediments are long – rework due to lab reporting errors, invoicing mistakes (they will all complain if labs over-invoice!), the lack of reliable business intelligence, etc.

Lab Test Information Management: Way forward

Focus on the drivers!

  • Gain Competitive Advantage – Do you believe you are in this business just to provide a test report? If not, how will you differentiate?
  • Retain & Grow – Customers no longer want just a test report, even if you can manage to send an accurate report within the committed TAT. How will you ensure customer retention? How will you delight your customers continually?
  • Improve Margins – It is difficult to maintain high margins while simultaneously aiming for higher growth, especially if organizations continue using human capital-intensive processes and systems. What are the operational objectives? In which areas will you be able to drive efficiency and improve cost competitiveness?

Align your objectives to the drivers

Ensure Customer Delight a. Improve consistency and granularity of reporting data by adopting a robust universal master data management (MDM) system that permeates across all operational transaction management and reporting systems.

b. Accelerate Turn Around Time (TAT) by promptly responding to regulatory or customer requested changes to test protocols and packages. This is a lot easier to achieve once you have a workflow based, global test, protocol/package management system such as the TRIMS in place.

c. Automate test data capture, reporting and invoicing.

d. Deliver real time operational information and rich business intelligence reporting through a smart customer portal. Once again, this cannot be achieved if you don’t have the basis to efficiently manage rich referential data.

– Differentiate

– Retain & Grow

Improve Scalability a. Implement best practice processes to improve operational governance across the network.

b. Adopt a scalable technology platform to handle simple to complex change management requests, quickly and accurately, impacting a large and ever increasing number of test protocols and packages.

– Differentiate

– Retain & Grow

– Improve Margins

Improve efficiency and lower risk a. Single source of truth for your test protocols and packages – eliminate reliance on stand alone, document/paper based test protocols and with it the risk of non-compliance with current regulatory and customer requirements.

b. Drastically reduce errors in test assignment, data capture and reporting, thereby minimizing rework.

c. Save paper and printing costs. This is not just about being socially responsible – it is pure margin, very much part of the current Total Cost of Ownership (TCO) – this cost will go away with adoption of the new test protocol and package management platform.

– Improve Margins

Let us know what you think – leave a comment below.

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Governance of Master Data: Definition, Structure and Implementation

Learn about the importance of establishing a master data governance model and how you can structure and implement a governance model for your enterprise.


We have discussed before the importance of Master Data Management (MDM) to your business (with a focus on testing laboratories). So – once we have agreed upon the need for MDM, we have to consider very seriously the matter of governance of that master data to ensure that it is a reliable central repository of your core information assets.

What is Governance?

Governance is everything that an organization does to ensure the ownership and accountability pertaining to master data management. This includes:

  • The items that form the basis for master data and their structure.
  • The processes to create and maintain master data
  • The metrics and business rules that pertain to master data management
  • The rules and processes around master data access, security and consumption

Effective governance of master data can be enabled by the establishment of a governance organization (team) whose responsibility it is to create policy and procedures that will be adhered to pertaining to the creation, maintenance and consumption of master data while ensuring that there are appropriate metrics in place to ensure the quality and business use of the master data.

Structuring a Governance Organization:

A well structured governance organization has:

  • A Governance Council: Made up of senior members of business units and corporate functions, the council establishes the strategy and defines the tactics via which master data will be governed. The council also owns the responsibility and accountability to propagate the directives of the governance council in their respective business areas via the stewardship team. The council oversees aspects pertaining to investments pertaining to master data and its consumption, and regularly evaluates the effectiveness of the governance model via metrics.
  • A Stewardship Team: Made up of information/data stewards, this team is constituted by individuals (generally business leaders or managers) from different business units who are tasked with propagating the program and monitoring progress. This team needs to be well defined as it is a critical factor for the success of governance initiatives. The stewardship team defines the specific policies, procedures and practices via which information will be governed. They constantly monitor the efficacy of the governance program and determine the projects that will make the biggest impact to information quality and leverage and report into the governance council. They also drive the organizational change initiatives – including training and evangelization – that are required to ensure the adoption of master data governance throughout the enterprise.
  • A Custodian Team: Information Custodians are individuals from various business units that are responsible for specific master data domains (e.g. people, customer, standards and regulations, methods, etc.). They are directly responsible for the management of the master data in their domain and work closely with the information steward for their domain to ensure alignment and adherence to governance policy.

Governance Policies & Procedures:

To ensure clarity a few policies should be established:

  • Policy around ownership and accountability pertaining to master data
  • Policy around governance procedure development & implementation
  • Policy around governance issues and their resolution
  • Policy around compliance audits
  • Policies around governance training

Once these policies have been established, they should then be materialized through well defined processes and procedures such as:

  • Identification, definition and implementation or new policies/procedures
  • Identifying and making the case for new master data initiatives
  • Making the case for modifications to the master data governance model
  • Definition of an oversight process to measure and monitor effectiveness
  • Oversee and manage the progress of existing initiatives
  • Definition and capture of metrics to measure performance
  • Defining the calendar and execution of compliance audits
  • Day to day management of governance and compliance issues

Centralized vs Hub & Spoke models in Testing Laboratories:

Often times we are asked the question by Laboratories about whether they should have a centralized governance team or a distributed one. The answer lies in the way the master data elements are managed. For small to medium sized organizations, it may be easier to implement a centralized model and then evolve that over time to grow with the geographical expansion. For large organizations that have a geographically distributed footprint, we most recommend the hub-and-spoke model – wherein collaboration and alignment are critical components to success.

As with most things, there is no one-size-fits-all prescription when it comes to this. The choice of architecture depends on specific business problems with processes or data domains. Organizations should remain flexible in their choice of architecture and select those that best fit their business needs.

Our Test Reference Information Management Solution (TRIMS) supports both models via its definition of various roles and collaboration workflows.

Critical Success Factors:

Establishing the right governance model is critically important – and it is hard. Especially for organizations that have never had that kind of structure. Here are some things that will help you along the journey of defining and maturing this capability over time:

  • Identify an Executive Sponsor: It is critical that a senior executive in the organization invests their time to be the executive sponsor of this initiative. They are positioned to ensure that the model aligns to corporate strategy while messaging to the enterprise the value the leadership team places on the initiative. As the chair of the Governance Council, they are also the final decision maker around investments related to master data and its adoption.
  • Identify a Chief Information Steward: The Chief Information Steward should be an individual who is the lead evangelist for the program and should advocate continuous improvements to master data quality (regardless of their current or past business roles / domain affiliation). They are key to driving organizational change.
  • Assigning the right people to the Information Custodian Role: Different business units and functions have different needs – and the information custodians should be picked to align with those needs. Most importantly, the information custodian should believe in and fundamentally propagate the values and ideals of master data management and its governance inside their respective business areas.
  • Build a scalable model: Try not to build too bureaucratic a model to start. Start small (but with clearly defined roles and responsibilities) and constantly measure/ change / improve. The intent should be to build something that scales with the needs of the business.
  • Build data quality into the model: Information quality should be an integral part of the model. In fact it should be one of the deliverables of the model to ensure that the output of the team results in ensuring the core strategic value of the initiative are being delivered.
  • Make the right choice around Centralized vs Hub-and-Spoke: We talked a bit about this before – make the right choice for your organization. Be willing to revisit the choice as the organization grows and matures.
  • Measure both efficacy and efficiency: Efficiency metrics answer the question, “Are things being done right?”. Efficacy metrics answer the question, “Are the right things being done?”. It is important to review and analyze both types of metrics to determine and improve the business value of governance.
  • Build a culture of information quality: If the enterprise does not see the value of master data and its governance, your initiatives will fail. Build a culture where information is treated as a key asset. Empower decision making at the nodes, but governed by the right framework that ensures quality. Always evangelize the need and value the initiative to the enterprise AND to the people that are doing the work.

If you are a testing laboratory, we strongly suggest you take a look at QualNimbus Test Reference Information Management Solution (TRIMS). It not only provides you with a centralized repository to manage your fore master data information, but also provides a full fledged governance enabling model built right into the solution.

The system allows for the definition of business units and departments, and ensures that information creation capabilities are restricted by the same. So – for instance – someone from the softlines business units can only create SL related packages (but still consume artifacts created by other departments such as chemical/analytical testing which could be a centralized capability). It has clearly demarcated roles for master data management, creation of test lines, protocols, packages, pricing management, etc. that ensures that information custodians work on those aspects that they are accountable for.

The system also has a well defined workflow for the management of core artifacts such as test lines and protocols/packages whereby an individual can author an artifact and it is automatically sent onward for review and approval after which its price can be configured in the system prior to it being available for consumption.

The system also provides operational reporting that allows for visibility to the work in progress (by status, stage, author, etc), the aging of artifacts, etc.

We would be happy to speak to you regarding how our system could be of benefit to your organization. Please contact us and we can set up a demo to walk you through TRIMS.

As always, we would love to hear your thoughts – so leave us a comment.

Master Data Management for TIC – Lab Testing

How a Master Data Management strategy implemented via QualNimbus TRIMS could help you be productive, profitable and deliver consistent service.

The importance of Master Data Management for Testing Laboratories

Most testing laboratories, in our experience as well as in speaking with many laboratories big and small, do not have a cohesive master data management strategy. While this could be for many reasons, they are generally doing themselves a disservice. Having a well structured master data management strategy allows laboratories to manage and leverage their core information assets.

Laboratories have to stay on top of the global compliance landscape and develop solutions that allow them to deliver services to their stakeholders in a consistent manner. This applies to aspects such as Regulations and Standards (which can vary by region/country/state/etc.), Methods for performing the test, associated Analytes and their limits, the definition of Test Lines and the consumption of those Test Lines in Product Protocols or Test Packages, etc.

Most laboratories that we have engaged with manage this information manually today – either in Word or Excel. There are several issues with this:

  • Information is not well structured (or consistently structured)
  • There is duplication of artifacts across the organization (across locations, for instance) which leads not only to governance issues but also the risk of inconsistency in service delivery
  • Making a change to a master data attribute can be heavily time consuming since every artifact that consumes that attribute will now need to be manually opened, reviewed, changed if necessary and reviewed.
  • The information in these files cannot be readily consumed – say by eCommerce solutions or by Laboratory Information Management Solutions (LIMS).

A lot of manual labor and errors in service delivery can thus be avoided by the implementation or adoption of a master data management solution. Besides this, having a MDM strategy also allows for highly effective data mining and business intelligence reporting around your transactional data which can help you significantly leverage your information assets.

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An Illustrative Scenario

Let’s say that a Laboratory ABC has a Retailer XYZ as a client. This retailer has its own specifications around the testing process – for instance, they sell a product in the US and Europe (i.e. they have to be in compliance with the regulatory requirements of those regions). Because of this, their specific needs – both from a regulatory and performance perspective could vary from the standard. For example, they could specify requirements that are more stringent than those being required by the regulations (e.g. if the US regulation requires <= 120 ppm for Lead, the retailer may chose to require their products go above and beyond with a requirement of <= 100 ppm). There are several other variances that could occur based on product attributes (e.g. applicability to specific materials, exemption of others), testing conditions (e.g. variances at different temperatures, wash cycles, etc.), etc.

As one can imagine, if this is being manually maintained, it is a nightmare to manage and govern. Often times, just because of the variances, laboratories choose to create multiple test lines that are identical except for these variances (which further exacerbates the situation). These test lines are also consumed in Protocols and Packages. So now there is an even higher number of artifacts that need to be managed and governed.

Now think of what the lab has to go through if a single analyte limit undergoes a change (say Lead goes to 90 ppm due to a new regulatory requirement in EU). The retailer asks the laboratory to ensure that they would be compliant to this requirement when the new requirement goes into effect. The laboratory now has to go in and find every test line and change the appropriate ones to reflect the new requirement (and ensure it has an effective date the same as the regulation “go-live” date) and then they need to do the same with every protocol or package. This often takes weeks of manual effort.

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MDM – Need for a simpler solution

The implementation of a master data management solution can dramatically reduce the effort associated with managing and governing this information.

QualNimbus has developed an enterprise class solution specifically for Lab Testing that allows centralized management of testing related master reference data. This solution is called the Test Reference Information Management Solution (TRIMS).

In TRIMS, all of the master data is defined just once – and discretely.

So in the scenario above,

  1. The client specific analyte-limit for Lead (which is managed separately from the regulatory analyte-limit) is managed in the Analyte Limit master.
  2. This analyte limit is then consumed in multiple test lines (as applicable).
  3. The Test Lines are then consumed in multiple protocols or packages.
  4. Changes to key artifacts are versioned, effective dated and governed (i.e. they require review/approval workflows).
  5. Every change in the system also triggers an audit trail so you can see who made the changes to what information and when.
  6. Once a change is made, it automatically cascades through the system and affects only those artifacts that consume that specific attribute or artifact.

The same scenario, which would take weeks of manual effort, can now be accomplished in a matter of hours using TRIMS.

If you manage a lab, we recommend you take a closer look at Master Data Management as a core aspect of your business. Please contact us and we would be happy to help you understand this better and demonstrate how TRIMS could help you.



Let us know what you think via the comments section.