Enabling the “Data-Driven Organization" - An Introduction to Enterprise Information Management

“Our leadership team continually stresses that we need to become a data-driven organization, but no one seems to know exactly what that means.”
~ Executive Director for Specialty Care Services at a West Coast Hospital

Many organizations have a stated goal to become “data-driven” included in their strategic plans. However, this vision is often not clearly defined, or there is an inherent assumption that the requisite people, process, and technology components have already been established.

CTG defines a data-driven organization as an enterprise that uses dashboard-enabled performance management techniques to improve business insights and information transparency. This requires providing timely access to clean, consistent, reliable, and most importantly, actionable information to the organization's decision-makers. Simply put, data-driven organizations are fully committed to the management of information as an invaluable strategic corporate asset. Actionable information provided to leadership allows the data-driven organization to thrive in an increasingly constrained and competitive fee for value environment. Rather than “driving by looking in the rear view mirror” with stale information, leaders in a data-driven organization can make timely, proactive decisions regarding access to care, costs of care, and quality of care – thus fostering greater stability and growth while better serving their communities.

In our experience, eight people, process, and technology components must be established in order for organizations to become truly data-driven:

1. 1. Governance – C-suite sponsorship of the Enterprise Information Management (EIM) initiative, with support from a blended team of business and IT stakeholders who establish priorities, obtain funding, eliminate roadblocks, and monitor implementation progress
   2. Data Governance – Roles, responsibilities, and accountability for data ownership and data stewardship, as well as viable workflows for data quality assurance and creation of the definitions and algorithms of the organization’s Key Performance Indicators (KPIs) and associated measures
   3. Master Data Management – Strategies and tools for managing the organization’s enterprise-wide reference data, such as Patient, PCP Provider, or Location
   4. Data Architecture – A three-tiered platform to “land” data from disparate sources, conform it to approved standards, and make it easily consumable for analytics and operational reporting
   5. Data Acquisition and Distribution – Tools to push or pull data from source systems, transform it into a standard and useful form and load it into an operational data store, an enterprise data warehouse, or departmental data marts
   6. Metadata Management – Tools to provide easy access to the definitions and business rules for KPIs and measures and the ability to perform impact analysis and data lineage reporting
   7. Business Intelligence – Analytic tools and trained resources required to implement executive dashboards, operational reporting, predictive analytics, ad hoc reporting, and data mining
   8. Technical Architecture – Servers, storage area networks, operating systems, database management systems, network bandwidth requirements, and security infrastructure

In order to assess your organization’s readiness to become a data-driven organization, I encourage you to ask yourself the following 10 questions:

1. 1. Has my organization developed a viable information management strategy that is supported throughout the enterprise?
   2. Are the goals and objectives of this strategy well defined and attainable, and is this strategy demonstrating incremental, tangible benefits?
   3. Does the executive leadership team fully support the transformation of the enterprise into a data-driven organization?
   4. Has my organization established an effective governance structure, composed of business and IT stakeholders from across the enterprise, to provide direction and oversight for Information Management initiatives such as data governance and analytics?
   5. Is an approved, comprehensive inventory of KPIs available to the leadership team on executive dashboards to enable effective monitoring of organizational and clinical performance?
   6. Has the organization reached a broad consensus on the definitions and algorithms for its KPIs and is this information readily available?
   7. Can business users easily conduct exploratory, drill-down, and root cause data analysis by querying a data warehouse or other analytic data repository without IT support?
   8. Is data quality assurance in the organization a key responsibility of the appropriate business owners rather than being delegated to IT?
   9. Are the “sources of truth” for my organization’s key Master Data Domains (e.g., Patient, Customer, Product, Provider, or Location) well defined and well-integrated?
   10. Is data from multiple, disparate applications being integrated or “conformed” in a timely manner for operational reporting and analytics? Is unstructured data from device monitors, social media activity, weblogs, or clickstream data available?

In a continuing four-part blog series, I will be providing additional information about the eight EIM disciplines outlined above, lessons learned over many years of developing and implementing EIM strategies, critical success factors, and specific next steps to become a truly data-driven organization.