AI strategy in healthcare (Part II): The data and AI strategy dyad for synergy

“Strategy is about setting yourself apart from the competition. It is not a matter of being better at what you do- it is a matter of being different at what you do.”

Michael Porter, founder and author on strategy in business

Data management is how data as an asset is operationalized to support the organizational strategy; it is mainly about logistics and is predominantly in the information technology (IT) domain.

Data governance, on the other hand, is more about strategy and is the policies and procedures that are in place to ensure the accuracy and safety of the organization’s data. This governance includes the establishment of infrastructure and technology with its processes and policies as well as its leaders with authority and responsibility to ensure data quality and reliability.

Data strategy is the roadmap that defines people, process, and technology to move an organization to a data-driven culture. The majority of organizations, however, do not have such a data strategy and often believe that data governance is its data strategy (rather than data governance being a part of its data strategy).

It is uncommon for a healthcare organization to have a comprehensive, enterprise-wide data strategy. Most healthcare organizations do not even have its data shared across the organization. It is not uncommon, for instance, for the business intelligence unit of a healthcare system to have information about its heart program that is entirely different than the information derived directly from the clinicians and their practice in the heart program.

The traditional data strategy, when it does exist in a healthcare organization, focuses on the collection, curation, management, storage, access, and security of the data for the organization; in other words, it is mainly focused on data governance. This data strategy is typically within the domain of IT with its own budget and management as well as leadership, and therefore lacks integration and collaboration within the healthcare organization. In short, healthcare data is considered a relatively static entity contained in fragmented silos and not significantly leveraged to change outcomes.

The AI-centric data strategy, conversely, focuses on leveraging data in the healthcare organization. This data strategy entails a synergy with the artificial intelligence strategy as one greatly enables the other. This forward-thinking data strategy takes the data strategy out of the aforementioned IT domain and shares it with other relevant areas such as data engineering and architecture units, business units, data science and artificial intelligence domain, and others.

The data strategy will need to rethink data governance with a new approach to data management and integration as well as new sources of relevant data. For instance, outpatient management can include data from wearable devices with an embedded AI. In short, data can then become a much more dynamic resource that will be leveraged to change outcome.

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