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 If that data is patchy, inconsistent, out of date, or incorrect, risks will be magnified each time someone re-uses that data.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.

Big Data challengers

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?

Changing behaviour

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. Although there may be some level of understanding that ‘good data’ is everyone’s responsibility, a lack of clarity around data ownership, and cross-company data handling and use, is obscuring the way forward.

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.

Looking deeper at data quality

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 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.

Tech capability

Inevitably, there will be technology considerations as part of this journey – particularly given the need for a smoother flow of consistent, compliant data between departments and across use cases. This will require appropriate data and technology architecture and integration skills.

Smarter tech can automatically flag any gaps in data ownership or sources if someone leaves or moves on. This also serves as an impact assessment tool whenever data changes. Technology, however, should be part of a broader strategy, where leaders, employees, partners and technology combine to build on a foundation of robust enterprise data governance.

Of course, time and budgetary considerations will always be a factor, but it is important to have a North Star to aim for even if improvements are incremental. That is, each step should contribute to the wider goal. If the foundations are solid, the rest will flow organically

About the authors

Patrick Middag is a Director at Deloitte, with a focus on Regulatory Information Management and IDMP, based in Brussels. He joined Deloitte after a career of more than 20 years in the pharmaceutical industry, including 14 years at Bristol Myers Squibb, where he was an Associate Director in IT for Global Regulatory Sciences. Dennie van de Voort, MSc, is a Jr. Manager at Deloitte, specializing in IDMP/structured data best practice and advanced use cases for the last six years. He is based in the Netherlands.

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However, as healthcare providers and organisations continue to look to data-driven digital solutions such as apps, virtual warehouses, automated stock replenishment, and the application of AI and machine learning, their need for effective data governance practices will also grow in order to keep private data secure. From engaging in research to providing emergency care, healthcare organisations must ensure that they can efficiently and effectively use data. Here are my five steps for creating effective data governance in healthcare:

1 ) Determine data governance goals and objectives

Whether it’s a pharmaceutical company looking to breakthrough in its latest piece of research or the NHS Digital strategy looking to review the digitalisation of England’s hospitals, healthcare organisations have many different data use cases. At the outset, any healthcare organisation enabling a data transformation must decide how data governance fits into their overall strategy and define objectives accordingly. Defining objectives helps teams create a transparent data governance framework that supports key areas such as improving patient outcomes, mitigating supply chain disruption or creating a more mature cybersecurity strategy.

2 ) Identify, categorise and prioritise sensitive patient information

Patient data is arguably the highest risk data that a healthcare organisation manages. To stay compliant with data regulations and provide the best standard of patient care, identifying and categorising patient data should be a top data governance priority. From clinical data to lab data and even payment processing data, knowing where data lives and how it is classified will determine how it is governed, and ensure it is properly protected according to GDPR regulations.

3 ) Assess and assign privileges and permissions

Privileges and permissions define who can access what data, and what they may do with it. As a best practice, data access should be governed according to the principle of least privilege. This means limiting access to information as much as possible without getting in the way of someone’s ability to do their job.

The healthcare sector has a growing number of interoperability standards that dictate how information is stored and shared between devices. Before you assign privileges it’s important to:

• Define types of data that different areas need to access
• Define who within a functional area needs to access the data
• Outline how they can access the data, including details about devices, geographic locations, and time of day

For example, a phlebotomist needs to know the patient’s name and date of birth. However, they may not need access to the patient’s entire medical history. Too much access increases the risk that data can be changed or stolen.

4 ) Remove low quality, unused, or stale data

In healthcare especially, data integrity is incredibly important. Low quality, unused, or “stale” data can negatively impact research by skewing findings. From a physician’s perspective, bad data can lead to care issues. For example, outdated patient prescription information can impact a doctor’s diagnosis and treatment plan. Keeping data fresh helps to achieve both care and operational goals.

5 ) Assign key roles and train employees

Finally, it’s important to have the right people with the right training in charge of data governance. To do this, you should create teams based on role, including practitioners, IT team members, and finance.

Accountability is important. Every functional area that manages sensitive information needs to ensure that the data managers, data owners, and data analysts understand their responsibilities. Data owners are in charge of their data, and they must know who has access and who should have access.

The healthcare sector is increasingly digitalising, and while simultaneously creating and storing vast amounts of data. To get the best use out of that data, as well as to ensure compliance with data protection laws, it is crucial for healthcare organisations, be they private hospitals, or the wider NHS, to implement and maintain good data governance practices.

By Matt Turner, Alation

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