Ahead of the game

Critical processes in the life sciences are increasingly dependent on high-quality data.

The impact of any digital process transformation programme relies heavily on the quality and reliability of the data that feeds into those processes. If that data is patchy, inconsistent, out of date or even incorrect, risks will be magnified each time someone re-uses that data.

Operationally, inconsistent information exchanged with the regulators will lead to non-compliance risks, risks of poor quality and, ultimately, a risk to patient safety and public health. More strategically, it will limit companies’ ability to perform useful data analytics and increase their business efficiency – reducing the time to market and patients.

All kinds of critical processes in the life sciences now require a dependable flow of agreed data – in addition to compiled documents – and major international regulators including the EMA and FDA have set out firm plans and frameworks through which this must be captured and exchanged.

The need to adequately record and maintain data elements (e.g. product ingredients, formulation, packaging, clinical particulars, manufacturers) from different functions, requires clear alignment and consistency across the enterprise and its different product touchpoints. Furthermore, companies must keep up with ever-changing data standards.

Tackling these challenges requires effective data governance practices. However, as we have seen with clients, this is one of the most difficult and complex topics for organisations to address.

Big data threat

A sobering point here is the potential for mass-scale market disruption by big data players, which have all the skills and resources to challenge the life sciences and healthcare industry head on with their strategic use of data and analytics.

For example, it would not be much of a leap for companies like Amazon and Google to enter the generics world, attract the right scientific talent and branch out into research around innovative therapies.

This is a real wake-up call to any company currently not taking the strategic role of data as an asset seriously. So where should companies start in embedding the right mindset and everyday working practices?

Most pharma organisations are already aware of the role data will play in their operational and strategic future, but in the majority of cases there is a gap between that appreciation and what it means for managers, teams and individuals – not to mention day-to-day behaviour.

The first element that’s needed is a clear overarching data management strategy, with cross-functional buy-in and active sponsorship from the top of the organisation.  Without this, any progress with data governance and data quality will be confined to the given team or department. This will limit the benefits and undermine the potential return on investment – especially if other teams go on to duplicate or dilute the good data’s value, e.g., by editing it in a way that conflicts with the correct source information, or does not adhere to the defined data standards.

Although regulatory teams may seem the obvious point of call for good, correct data about a product across its life cycle, once companies start to trace that data back to its source, they begin to realise that the reality is far more fragmented.

The real originators of that data are likely to be CMC teams or clinical research scientists, who often don’t realise that they hold a responsibility toward regulated product information and who are often also unaware of the importance of their data in other processes and systems elsewhere in the company.

So senior stakeholders higher up the organisation should seriously consider an organisation-wide data governance strategy.

Clarity and direction

With the vision in place, companies will need to define roles and responsibilities linked to data’s quality. They must also communicate data’s value to the company, create clear rules and measure progress of data governance.

Up to now, if an issue emerges with data quality, business functions typically look no further than one or two steps forward or one or two steps back to resolve the issue. Their focus is often on the immediate use case, on how they can fix the issue to satisfy local needs or compliance requirements.

There is no deeper investigation to ensure that information is correct at the source, or how any issues with the data have arisen – so that these can be avoided in future. Most of the time, such shortcuts are taken because there is no time, no budget, or no immediate business driver.

The lack of clarity around data ownership, data consumers or data business purpose, are typical causes of all this. Establishing a data governance structure, including appointing a data governance lead, data owners and data stewards, an interaction model and company awareness through change management efforts, will help provide that clarity and direction.

Hand-in-hand with establishing data governance roles and responsibilities, senior stakeholders need to communicate the evolving role and value of data, so that the entire organisation begins to see and treat data as an asset to be harnessed across a range of contexts. In large organisations, creating this new mindset requires a proper change management programme.

Defining and clearly documenting the company’s preferred data standards is another important activity. Simply copying and pasting, let’s say ISO IDMP specifications into new procedures could do more harm than good: guidance needs to be practical, actionable and clear, so that users can absorb the new rules at a glance.

Final analysis

Inevitably, there will be technology considerations as part of the journey. This will require appropriate data and technology integration skills. A smarter system capability, which will be necessary for large international organisations should, for example, automatically flag any gaps in data ownership or sources if someone leaves or moves on. Such capability also serves as an impact assessment tool whenever changes occur, ensuring that details are reflected elsewhere.

There is a growing appreciation across the pharma sector that data is everyone’s responsibility. A lack of clarity, however, around data ownership and enterprise data handling means that the primary focus must be on developing an effective enterprise data governance strategy. One that will yield a smoother flow of consistent, compliant data between departments and across use cases.