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TDWI Upside - Where Data Means Business

Who Is Responsible for an Organization’s FAIR Data?

When businesses and their leadership prioritize FAIR data principles, effective and compliant data usage and analysis becomes a collaborative effort across the enterprise.

FAIR principles, first described in a March 2016 paper in the journal Scientific Data by a consortium of scientists and organizations, are guiding principles for data management that define and advocate for “good” data. FAIR stands for findable, accessible, interoperable, and reusable. The acronym represents guidelines for ensuring data is organized for the best outcomes.

For Further Reading:

Solving “Bad Data” -- A $3 Trillion-Per-Year Problem

Building Customer Trust in Your Data Policies

Three Ingredients of Innovative Data Governance 

Even though the original article was published six years ago, IT visionaries around the world still advocate for and rely on the principles. These guidelines are considered essential, so why do IT teams continue to struggle to implement them? In reality, the responsibility for FAIR data doesn’t fall only on the shoulders of the data team.

The Need for FAIR Principles

Researchers, businesses, and society as a whole benefit from data-driven research to instigate positive change. In today’s digitally connected world, successful research is increasingly dependent on the ability to collect, organize, and analyze huge, complex, or constantly evolving data sets and related materials. To support that work, stakeholders involved in research and data ecosystems adopt FAIR data principles to ensure data can be accessed, read, and utilized widely by machines and people alike.

FAIR data principles apply to metadata, data, and supporting infrastructure such as search engines, but the framework is also useful for ensuring published findings, software, and research methodologies are effective and continue to provide value.

Defining FAIR in the Context of Enterprise Data

In the context of enterprise data, FAIR data includes these key attributes:

  • Findable: Both people and machines can locate the data. This requires unique identifiers among all systems within an enterprise.
  • Accessible: Users can retrieve the information they need on demand, based on user permissions and governance rules. Varying levels of an organization require different levels of access.
  • Interoperable: Systems can interact with each other, ensuring data remains intact when utilized by multiple systems, and the data itself can integrate with data sets from other systems. This requires specific language sets and references to refer to the data, alongside effectively integrated systems.
  • Reusable: Users can refer to past data, such as vital client relationship data. Repeat client interactions don’t start from scratch, requiring re-recorded data. Instead, the data is already available and organized, including the option to send or integrate this data with other systems.

How Does FAIR Data Aid Enterprise Business?

The basis of FAIR data principles originated with the scientific community, but they apply across organizations and enterprises, wherever consumer or research data is collected. The guidelines help ensure effective data usage for decision-making but also for the benefit of customers. FAIR data principles align with data compliance and privacy standards, helping businesses prioritize the safe collection, use, and sharing of data as an up-front, transparent, and communal responsibility. It’s both the law and an ethical business practice to prioritize customer data protection and combat data breaches.

Consider recent compliance regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or the Virginia Consumer Data Protection Act (VCDPA). Although these laws vary in scope and language, fundamentally each provides the consumer with the right to:

  • Know what personal information a business collects about them, how it is used internally, and who it is shared with
  • Delete personal information from a business (with some exceptions)
  • Opt out of the collection or sale of their personal information

FAIR data principles, when effectively practiced throughout an organization, benefit the entire business ecosystem, including customers, employees, leadership, stakeholders, and surrounding communities.

Findability and accessibility eliminate data silos within an organization. Governed access to organizational data permits the right users across all departments to collaboratively utilize and contribute to all data taken in by the organization with safeguards in place. From there, active efforts to ensure data’s interoperability and reusability improve its lifespan by ensuring the data already gathered remains useful and adaptable while meeting compliance standards.

Why Companies Struggle

Too often, organizations collect and interact with mass amounts of data, then turn to the data management and analytics teams to say, “Ensure this data is FAIR.” However, it’s a difficult, if not impossible, task for the data team to make the data FAIR when the system collecting it doesn’t account for FAIR principles in the first place. Instead, a successful approach for FAIR data begins much higher, at the application level, and as an integrated part of the data processing and consumption design principles.

For Further Reading:

Solving “Bad Data” -- A $3 Trillion-Per-Year Problem

Building Customer Trust in Your Data Policies

Three Ingredients of Innovative Data Governance 

Further, due to the changing landscape of rules and regulations related to data collection and consumption, organizations now face more challenges when retrieving customer data. A highly competitive marketplace causes businesses to be more protective of their customer and internal data than ever, hindering interoperability.

What You Can Do to Make Data FAIR

It’s vital to recognize that FAIR data cannot be achieved by the data team alone. Businesses need to look at FAIR principles as a function rather than as a response to compliance or a singular responsibility of data teams. They must design data processing and consumption applications to include FAIR data principles such as:

  • Applications that capture and organize data efficiently with unique, common identifiers for data entry to ensure findability
  • Identity governance solutions that ensure the right employees have access to the right information when they need it -- and don’t have access to too much information
  • Integrated systems across the entire enterprise to ensure interoperability, communicating across platforms and applications
  • A thoroughly organized, central data system to guarantee data collected and analyzed in the past can be reused and processed in multiple contexts
  • Transparent communication with clients about what data will be gathered and how it will be used

Consider patient management systems as an example. When new patients visit, it’s essential for their new data to be findable and accessible for administrative teams, medical practitioners, customer service, and payment management, often before reaching the data teams. If patients return after their first visit, their data must be retrievable. Each of these teams requires a different degree of access to a patient’s information at varying stages of the patient’s life cycle.

With FAIR data prioritized at the application level, each team can find and access the data it needs and provide consistent updates across all applications, shared across the organization. FAIR data ensures internal teams are communicating efficiently on the patient’s behalf, while prioritizing data privacy and ensuring the patient’s interactions at every stage start where the last interaction ended rather than at zero.

FAIR Data Is a Collective Effort

Business leaders know that customer data is a must-have when addressing customer needs, values, and challenges. When businesses and their leadership consider FAIR data principles as a shared function of the organization, rather than a responsibility of data teams, effective and compliant data usage and analysis becomes a collaborative effort across every department and role within the company. If data isn’t siloed, it’s possible to achieve aligned growth, with a clear understanding of the customer in mind, across every level of the organization. The end result is empowering organizations to make better customer-centric decisions.

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