What Drives Quality? – Part II

As we explore the central question of what drives quality one thing becomes clear – it’s easier to define what quality is than to define how you go about delivering it. That’s because ensuring excellence across a portfolio of projects demands that you’re able to harness what we call the ‘three pillars of quality’ – people, process and technology. These three elements need to come together in near-perfect harmony if you’re to deliver consistent quality across the board.

Let’s take a look at each of these in turn to see if we can define why each pillar is so important and how they can work together to deliver the right results.


The first pillar. Over the last 10 years or so, the industry has fallen in love with outsourcing as a way of reducing costs. This has worked to an extent, but it’s also introduced a problem, because the moment you reduce people to a commodity – a line in a budget sheet – you lose sight of all the subtler ways they can contribute to a company’s success. Things like their experience, their understanding of nuance, and their ability to work as part of a multi-functional, multi-disciplinary team.

How do you find these people? It starts from the first interview where you’re looking not just at the person in front of you, but also who they could become – tapping into their potential.

Why? Delivering quality in a small team is easy, but in a larger team or across multiple teams you need more than just highly qualified people, you need a skilled, well-rounded workforce with a questioning outlook – the data scientist mindset if you will – that approaches high volumes of data and complex derivations with a sharp eye and appraising attitude.

The People Challenge – One of your team discovers a problem. Do they try and cover it up or stare the mistake straight in the face and ask what they can do differently in order to avoid it next time around?


The second pillar. Everything in our industry has a rigorous process – a set of repeatable steps tying a project together – that team members need to understand and follow. However, if the process becomes too prescriptive then there’s a very real danger that quality control becomes an end in itself and you lose sight of why the process is there in the first place.

You need good processes of course, because the industry demands it and even the best, most highly qualified people can be disorganised without a proper structure. Processes also evolve: ICH E6 Good Clinical Practice (Revision 2) came into force in late 2016 and whilst the focus is on risk-based monitoring and client oversight, it can equally be applied to support the QC and oversight of statistical reporting deliverables. Namely, checks that focus in on the most important aspects of a study – like the primary endpoint or key safety outcomes. But like all good processes it’s important to remember that these changes facilitate quality – they don’t equal quality.

The Process Challenge – A team follows a 20 step process methodically, one step after the other. Is this an exercise in box-ticking or a framework for delivering excellence?


The final pillar. In daily life, nearly everything that used to be done on paper is now done by computer, and the temptation in our industry is to do the same: employing a bit of technology to solve each and every problem. The key driver here is to understand which parts of the process should be computerised and which parts are still best performed by people. Whilst computers are good for replacing laborious tasks, overall efficiency should always be front of mind. Automating the right processes allows people to use their insight and experience to assess the data from raw data through to final deliverables. In this way, technology can be used to give your team time to do the things that machines cannot – consider, reflect, question and troubleshoot.

The Technology Challenge – Is comparing two outputs side-by-side like an old-fashioned proof reader really a good use of your team’s time?

This then, is the secret to delivering consistent quality across multiple projects. You must strike the right balance in each of the areas so that you get experienced, highly qualified people who can follow your processes intelligently – rather than slavishly – using technology in ways that frees them to stay creative and innovative, all the while applying a data scientist’s questioning mindset to their work. It’s not easy, but in terms of consistent quality, the results are well worth the effort.

Making these three pillars work together in harmony requires something that we’ve referred implicitly to throughout this article – balance.