Instilling enterprise data governance practice

The impact of any digital process transformation program 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 If that data is patchy, inconsistent, out of date, or incorrect, risks will be magnified each time someone re-uses that data.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 patient safety and public health risks. More strategically, it will limit companies’ ability to perform useful data analytics and to increase their business efficiency, reducing the time to market and patients.

All kinds of critical processes in 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 data 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.

Embedding a data-first mindset

A sobering point here is the potential for mass-scale market disruption by the Big Data players, which have all the skills and resources to challenge the Life Sciences & Healthcare industry head on with their strategic use of data and analytics. For example, Google and Amazon have already started to make strides into the industry. It would not be much of a leap for these companies 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?

Leading from the top

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 - and for day-to-day behaviour.

The first element that’s needed, then, is a clear overarching data management strategy, with cross-functional buy-in and active sponsorship from the top of the organisation, typically starting with the CDO, CIO or CDTO. 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 lifecycle, 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 will need to sponsor an organisation-wide data governance strategy.

Communication and clarity are key

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 often 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 program.

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.

Smarter tech underpins strategy

Inevitably, there will be technology considerations as part of this journey. This will require appropriate data and technology architecture and 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 to data, ensuring that the details are reflected elsewhere.

Technology is important but it must play a supporting role to a strong management lead in driving an effective enterprise data governance strategy, designed to foster consistent use of robust, reusable, standards-based data that will underpin a flow of data between departments and across use cases. This will be key to remaining competitive while delivering the optimum outcomes for patients. Of course, time and budgetary considerations will always be a factor, but it is important to work on incremental improvements and each step should contribute to the wider goal.