While organizations are increasingly aware of the importance of data, they collect, process, use and share it on a daily basis. However, the issue of governance is not yet well addressed despite its criticality.
- Indeed, at a time when data is becoming more numerous and more complex every day,
- where new regulatory and legal constraints are multiplying (RGPD, DSA, …),
- and where new technologies exacerbate competition,
It becomes vital to think and implement a data governance aligned with business challenges.
In this article, you will find below some considerations from the field.
Serve the organization
Data governance is the strategy to apply to data. Data governance is the management of data and processes to ensure effective and efficient data usage to serve the organization’s goals. The ultimate goals of data governance is to give everybody in the company access to the data and extract value from the data itself.
Data governance aims to provide better visibility of internal and external data. It is built to ensure compliance with regulatory law, and serve the enterprise by providing knowledge workers a common, understandable and efficient vision of the data they are producing and using.
Make data an asset
Life sciences organizations like any other organization today have incredible amounts of data about products, services, therapeutical targets, methods patients… Data governance is not anymore an option as it is a necessary engagement for:
- A common understanding of data: It provides a consistent view of data not only in the organization but also in the overall life science industry thanks to standardized vocabularies (thesaurus, ontologies, dictionaries…)
- An understandable data mapping and classification within the organization. Most of the organizations have got siloed data making it difficult to access. Data governance enables building data mapping to clarify and ease data access.
- Long-term data management: as part of the data governance, data management establishes rules and best practices ensuring that from production to consumption, data is consistent, accessible, meaningful and trusted.
Data compliance, integrity & quality: especially in life sciences industry, data compliance, integrity & quality are key cornerstones to avoid inadequate strategic data insight, product suspension, loss of reputation, any audit warning letter…
The sooner, the better
The sooner, the better! It is never too early to start thinking about data governance. It is also never too late to implement data governance. Data Governance can be addressed with a step-by-step approach, focusing on the most critical data and/or business case in priority.
Data governance is not a project, data governance is a long-term vision, in perpetual evolution.
Build a strategy
To ensure data governance, the company needs to apply a strategy. This strategy implies to:
- Have a map of the current data management in place to build the target data governance. The data governance to build depends on the organization, the size and the maturity of the company. Even if the project is ambitious, it must respect the capabilities of the company. Data governance needs to serve the organization to reveal data insights.
- Define the data governance organization considering
- Rules and standards
- Data referential, data catalogs, master data management systems.
- The data life cycle
- Plan, Do, Control & Act through the stakeholders assigned to the project and even after the project.
- Communicate, train and make data governance a company objective involving users from data producer to data consumer
The data governance is an iterative approach, built on prioritized use cases, with key stakeholders able to drive business transformation.
Everyone is concerned
A data governance project is first a business project that must be supported by the top management. To make the project a success, one of the most important tasks is business acculturation. Every employee within the company must take part in data governance objectives and have a data culture. Nevertheless, for the project’s success, it is necessary to engage a team of experts in the data governance project.. The key players of this team are the following:
- Data Officer: he is the transverse manager of the data policy. He sets the rules, leads the teams in the proper use of data, and oversees the company’s overall data strategy. He is in charge of the entire project management process
- Data Owner: responsible for the content and quality of the data
- Data Steward: translates business needs into data needs, leads the data community, acts as a liaison between the business and the IT department to manage IT implementation and change management
- Data Analyst / Data Scientist: he assists the business on all available information, data analysis or reporting needs.
Set up data governance
Data governance should be applied in all the company departments (finance, marketing, R&D….). In Life Sciences companies, data governance for R&D data is particularly important to provide access to data to as many people as possible to carry out their mission and create value. The company must democratize the use of the data. To do this, all business teams must be aware of all the data managed within the company, its meaning and its context. They need to know where it is and how to access it to get the most out of it.
After these initial considerations about Data Management, the only question left is: When do I start my next Data Management action?