Building a Mature Analytics Culture at Any Budget

In general, there are few industries more data-intensive than healthcare.  But in today’s digital health environment, providers and patients alike are producing unprecedented amounts of data for a variety of health-focused reasons. Successful organizations, of all type and budget, mine their data for actionable intelligence to their impact financial, operational, and clinical outcomes.  Building a mature analytics culture at your provider organization is absolutely possible at any budget or resource level, but requires organizational buy-in that must be cultivated and maintained.  

This value guide, developed in collaboration with HIMSS corporate member, Oracle Health Sciences, provides healthcare organizations advice on how to establish and optimize a mature analytics culture within your organization no matter the budget or resource level.  


Understand the Data You are Already Collecting

As a healthcare provider organization, you are already in possession of an enormous data stockpile from the administrative, clinical, and customer databases you maintain.  In some instances, you share that data with local, state, or federal health officials in order for your organization to be in compliance with current reporting requirements.  

  • Discover what type of data you are collecting for your MedPAR, for any local, state, or regional reporting compliance. 
  • See what information your marketing team may have in your customer relationship management databases.  
  • Consider the operational and financial data sets available for analysis within your administrative data and billing files.  

As you work to build a mature analytics culture, make sure you know what data you already have available to analyze at the start of your journey. 


AHRQ: Understanding Data Sources: Depending on the measure, data can be collected from different sources, including medical records, patient surveys, and administrative databases used to pay bills or to manage care. Each of these sources may have other primary purposes, so there are advantages and challenges when they are used for the purposes of quality measurement and reporting.

Find the Questions the Whole Organization Wants to Answer

Every area of your organization has questions that can be answered with the data your organization collects.  Figuring out what those questions are is a good place to start when trying to build cultural buy-in while establishing a mature analytics culture.  

  • Find a Question that Helps “Keeps the Lights on”: Using data sets already in your possession (see above), work with a small group of stakeholders from across the organization (clinical, IT, Quality) along with a C-Suite champion to define a question for the organization to answer through data analysis.  It is helpful to focus first on questions that provide insights on data already collected to meet local, state, or federal reporting compliance.
  • Define Questions You Want Answered by Third Parties: Once your organization has built cultural buy-in from analysis of compliance-related data, consider how your organization can leverage analytic services from third parties that analyze your collected data to respond to operational questions, like patient satisfaction, that are assessed by established third party analytics firms.
  • Evolve to Personalized Questions You Want Answered Internally: Once your organization is ready to mature its analytics culture beyond compliance-related and third party analysis, define the questions that drive your analytics practice around personalized questions unique to your own organization’s clinical, operational, or financial processes.  Support strengthening your internal analytics capabilities. 

Evaluating Data Sources for Real-Word Evidence: Electronic medical and healthcare data enhance our understanding of medical interventions, mhealth , precision medicine and randomized clinical trials continue to find ways to use real-world evidence to support both the safety and efficacy of drugs.  This clip explores how the Veterans Health Administration and Moffit Cancer Center think about the data sets they need to explore to support their care delivery.

Show the Organization That Their Questions Can Be Answered

For a mature analytical culture to take hold, it is important to establish some early precedent that illustrates for those across the organization the impact data analysis has in answering the questions you’ve chosen to explore.  Communicate regularly with various constituencies about the progress your organization is making to leverage data analytics to answer questions that impact their daily activities.  Make the maxim “things that get watched get improved” a common refrain amongst your stakeholders.

  • Start Simple:  With your first question ready, collect 30 days’ worth of data to analyze.  Make sure to reconcile the data against another data source to ensure the data you are collecting is valid.  Your first goal should be to prove to your organization that you’ve built a repeatable analytics process.
  • Consider Starting with Operational Questions: Look for questions about operational areas within your organization’s that offer rich and specific data sets to support analysis.  Areas such as materials management or supply chain will offer a linear process to analyze related to widely understood metrics.  It will place the organization’s initial analytical focus on areas that are better positioned to be more easily received by the wider organization.
  • Once Buy-in is Established, Start to Analyze Clinical Data:  Once your physicians and nurses have internalized the effectiveness of your operational data analytics, begin to approach this constituency about their participation in clinically-focused data analysis on areas such as care quality, wait time, or provider workflow.

Real-World Advice on Analytics for Value-based Care: Tressa Springmann, senior vice president and CIO, information services at LifeBridge Health, explains how the organization’s analytics journey started with developing a scalable and effective analytics roadmap to build organizational support for an analytics-driven culture.


Build Governance That Expands Organizational Buy-in 

Once your organization’s initial embrace of analytics has laid the foundation for a data-driven culture, establish governance structures that support the expansion of data analytics use throughout the organization.  Make sure your governance model is designed to meet your particular organization’s analytics needs.  Be sure the following constituencies are represented in the design of your governance model:

  • Physician Champion
  • Data/Business Analyst
  • IT Leadership
  • Quality Leadership
  • EMR Representative (if available)

For larger organizations such as health systems, consider including in your governance model

  • Physician practice representatives
  • Individual Hospital-level IT Leadership  

Make sure your governance structure is designed to build on data analytics successes, that governance communicates these successes regularly with the wider organization, and that governance works to increase the use of data analytics to respond to the clinical, operational, and financial needs of your healthcare organization.


A Roadmap to Effective Data Governance- How to Navigate Five Common Obstacles: A HIMSS C&BI Committee paper examining five obstacles encountered by healthcare organizations when implementing data governance and recommended approaches to achieve the desired results.

Practical Steps to Enterprise Data Governance: In this companion piece to "A Roadmap to Effective Data Governance- How to Navigate Five Common Obstacles," learn how to create a program that succeeds in getting your data governance started and sustained, understand the importance of data stewardship, and take the long-term view of data governance.