Make your new Smart Lab really deliver: The Bigger Picture

If you have been following my Smart Lab blogs, you’ll have seen a range of topics discussed, the common theme being the risk of shiny new IT systems not fitting in with the people and processes in the lab.  In this last blog drawn from Tessella’s white paper Five Factors of Smart Laboratory Success, I’m going to take a look at the Bigger Picture.

It is entirely reasonable that a project team should focus on the problem in front of them: the challenge of building and rolling out a new system.  But someone in the organisation must find the time to step back and look at the bigger picture.  There is a good chance of failure if no one thinks strategically about the other pieces in their application and services jigsaw, to make sure that any update aligns with the architecture of information and processes, as well as wider business needs.

In a research lab, processes change all the time as scientists develop new experiments and generate results that are differently structured or in new formats.  These changes are often the driver for introducing new systems.  However in this moving picture, it is essential to have some fixed points and standards e.g. around basic data structures.  What do you mean by a batch, sample or lot?  How will you in the future be able to compare results between experiments and samples, to track quality trends or to carry out more sophisticated predictive analytics or data mining?  Doing this successfully requires hard thought and experience in data modelling, data architecture and analysis of metadata.

It is also important to think about application architecture at the conceptual level, in order to avoid messy and expensive collisions between, say, cross-functional workflow designs (e.g. request management) and more complete, integrated functional solutions, as are typical in compound management and high-throughput synthesis or screening.  If you have a common services infrastructure, one vital question is what standard services you need to define at the global and cross-functional scale (e.g. substance identification) and what, if any, constraints this places on your choice of vendor or solution that will consume these services.

Here is an example of where looking at the bigger picture made the difference between failure and success for one our customers.  This large multi-site R&D organisation was running several global projects to roll out applications across all their sites as part of a larger drive to harmonize workflows and practices.  The new global systems were replacing local systems in use at individual sites, with functions ranging across much of the pharma research workflow from molecule synthesis and assay requesting, to compound management, data analysis and an ELN.  There was a high level of integration between the systems, both old and new, and on top of that, regional differences in working practices.  Poor communication and coordination between projects meant that unexpected dependencies and other nasty surprises kept emerging.

Working with the client, we analysed the dependencies between the projects.  We looked at harmonizing local workflows to the new systems, and what the consequences would be for old systems.  These legacy systems needed to be either decommissioned or reintegrated into the new system landscape.  Our analysis helped the global program managers to work out a timeline for the various system rollouts, and overcome issues that had proven difficult in previous plans.

Of course every project is different and there is no magic formula for success.  But my colleagues and I have found that remembering to think about the bigger picture is almost always helpful.  This is just one of the common factors that we have found help us to deliver successfully.  The others have been covered in earlier blogs, or, if you want to see them all in one place, please have a look at the white paper: Tessella’s Five Factors of Smart Laboratory Success


Simon is a business analyst and consultant with more than 10 years experience in pharmaceutical ...

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