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What other industries can teach healthcare about data governance

A guide for healthcare as it brings the promise of consistent and reliable data measurements to life.

George DealyBy George Dealy

Basing decisions on reliable, well-understood data has become table stakes in many industries. Furthermore, the advanced use of analytics to derive hidden insights from information has quickly become the new frontier for creating competitive advantage. Data-driven decision-making has completely transformed a variety of industries, beginning several decades ago. Healthcare, on the other hand, is still in its infancy with respect to leveraging reliable and consistent data to improve decision-making and ultimately organizational performance and the health of populations.

Adoption of clinical analytics and business intelligence capabilities is on the rise as healthcare organizations begin to see the value of investing in analytics. However, there are many challenges to implementation, including the governance necessary to ensure that analytics initiatives are well understood and enthusiastically supported throughout the organization. The results of Dimensional Insight’s survey of more than 100 CIOs and CMIOs highlight some major challenges to implementing the enterprise-wide data governance necessary to drive improvements in care quality and patient safety:

  • Lack of organizational or leadership buy-in
  • A culture of resistance and interdepartmental conflict
  • Lack of trust in data
  • Discrepancies in how measures are defined
  • Lack of resources, limited resource availability, stalls the adoption and implementation process
Challenges to implementation in the healthcare industry include the governance necessary to ensure that analytics initiatives are well understood and enthusiastically supported throughout the organization. Photo Courtesy of | © scanrail

Challenges to implementation in the healthcare industry include the governance necessary to ensure that analytics initiatives are well understood and enthusiastically supported throughout the organization.
Photo Courtesy of | © scanrail

Industry Examples

Thankfully, a number of industries have already overcome a number of challenges associated with integrating data governance and analytics, and they are now reaping the benefits. These successes can serve as valuable lessons for healthcare.

Industry example No. 1:
Oakland Athletics revolutionize the use of sabermetrics

The challenge: Around the turn of the 21st century, Major League Baseball’s Oakland Athletics found themselves playing teams with payrolls millions of dollars greater than what they could afford. Nonetheless, A’s general manager Billy Beane was determined to be competitive. They needed to change the rules of the game, and information that they already had – combined with analytics – turned out to be hidden assets that would be central to the plan. The goal: significantly improve performance without increasing costs.

One of the A’s tactics was to optimize players’ offensive performance. However, instead of relying solely on batting average, stolen bases and runs batted in, which had been the gold standard for a century, they looked for other angles. For example, they included on-base percentage (OBP), which measures a batter’s ability to get on-base, whether by hit, walks or getting hit by a pitch, rather than just hits, and the slugging percentage (SG), which assigns greater weight to extra-base hits over singles. These measures had been proven to be reliable indicators of run production by baseball statistics enthusiasts, but their use in the professional ranks was controversial and not generally accepted when compared with the intuitive judgement of scouts and coaches. The A’s experiment paid off and they went on to make the playoffs in both 2002 and 2003.

Since then, sabermetrics, the practice of applying analytics to baseball, has played an increasingly significant role in how the game is played. The Boston Red Sox hired Bill James, the founder of sabermetrics, as a consultant during the run up to their World Series victory in 2003. The number of measures that have proven to be useful continues to increase, and those teams wanting to stay ahead of the game strive to create and harness information in increasingly creative ways.

Lessons for healthcare: Although the story above is about baseball, there are several relevant lessons that apply equally to healthcare. First, the impetus to depend on analytics came from the top of the organization. Commitment of leadership literally turned the game around to what it is today because of a creative general manager prevailing over a culture of conventional wisdom. Healthcare organizations can follow suit by establishing governance steering committees with strong leadership representation to guide analytics implementations.

Second, management trusted the data. What turned out to be relevant, reliable and actionable information was based on measures that had previously been undervalued. Incorporating these additional measures into the data set exposed new revelations that enabled the team to succeed.

Third, the A’s use of analytics was partially a result of their resources, or the lack of them. Their success showed that an organization doesn’t need to be the largest or best funded to be capable of delivering the best outcomes. What’s crucial is an intense commitment to improve.

Industry example No. 2:
Netflix, Inc., masters of metadata

The challenge: Netflix, Inc. burst on the scene as a new paradigm in movie viewing by creating a movie rental experience modeled after the local gym membership. Periodic subscriptions replaced the usage fees, and Netflix individualized recommendations helped them to manage inventory.

Remember that before the days of streaming video, your movie-viewing options were limited to going to a theatre or a brick-and-mortar movie rental store where you could rent and then return movies. The costs for rent-and-return were based on the number of movies you rented or possibly a discount if you stocked up and rented several at once.

Initially, as we all now know, Netflix provided mail order DVD subscription service with unlimited usage and free shipping. You selected movies, which would show up in your mail box (physical mail box, that is), and after viewing, you would mail them back. For your next movie, you could request one that you’d previously selected, or perhaps one that Netflix suggested for you.

How does Netflix make these individualized suggestions for each viewer? That’s one area where Netflix uses data analysis to provide customized suggestions based on customers’ viewing preferences as well as to optimize the Netflix inventory circulation. For example, Netflix could recommend movies that fit a viewer’s profile and are available in inventory rather than movies that are in higher demand.

Of course, today the paradigm has shifted again, and streaming video has overtaken snail mail delivery. But the genius of Netflix algorithms remains. How does Netflix make those recommendations? Netflix keeps extensive metadata on movies and viewers in order to match the viewer’s movie choices with the genre of available movies.

But, the genres aren’t just the standard drama, comedy and horror. There are thousands of distinct categories, or microgenres, such as tearjerkers, sports comedies and zombie horror, as well as age-specific, actor-specific and director-specific genres. Specially trained movie reviewers watch and tag movies with the Netflix-created metadata using specific syntactical patterns. This level of detail ensures that the microgenre designations are applied consistently and comprehensively across the inventory to personalize recommendations that are relevant and compelling to viewers.

Lessons for healthcare: The Netflix story illustrates the power of developing reliable, agreed-upon metadata that can be used consistently across an organization. The Netflix approach to constantly be analyzing and fine-tuning the effectiveness of their metrics reflects the use of best practices for maintaining accurate and relevant metadata.

In healthcare, comprehensive metadata can’t be created just by watching movies, but it can through a collaborative methodology. Bringing teams together across departments to define terms and governance rules can ensure that the metadata is understood by all and applied consistently enterprise-wide.

Industry example #3:
Harrah’s Entertainment, Inc. wins with customer satisfaction

The challenge: Consider a casino. You’d likely bet that the greatest revenue is generated from the high rollers, those folks who spend thousands in a gambling establishment during a weekend. But, if you made that wager, you’d lose. With the help of analytics, Harrah’s Entertainment, Inc. determined that 82 percent of its revenue was generated by 26 percent of its customers, and those customers were mostly playing the slots. So, how do you make sure that those customers keep coming back?

Once again, this is an example of a company that uses analytics for competitive advantage. At first blush, it might seem difficult or unusual to quantify the soft skills of delivering a first-rate customer experience, but Harrah’s Entertainment, Inc. wins the lottery with its ability to do just that.

At a Harrah’s casino, the customer experience comes first. To increase customer loyalty, Harrah’s pays exquisite attention to their customer service, including product pricing and promotions, all of which are managed with sophisticated analytics. Employees at all levels are actively engaged in improving every customer’s experience.

A rewards program, similar to airline frequent-flyer programs, not only captures each casino customer’s preferences and behaviors, but also provides service and perks based on factors such as a customer’s spending patterns and demographics. For example, if a customer lives locally and generally plays slots in the morning and then heads to lunch, a hotel chit probably wouldn’t be the best reward. Harrah’s captures these types of customers’ preferences and offers individually tailored rewards.

This level of sophistication isn’t built on guesswork. Harrah’s marketing has accumulated customer data sets based on the rewards program information. That data is analyzed to determine exactly which rewards and services most contribute to customers’ satisfaction and loyalty. Then, the benefits are made available to customers at all properties.

Lessons for healthcare: It’s becoming clearer that the impact of “customer experience” is as crucial in healthcare as it is in the gaming industry. The emerging focus on “patient-centered outcomes” is helping to integrate the patient experience into the analytics process to better understand how to improve care and use information provided directly by patients to improve overall health.

While patients don’t use rewards cards, there is more and more patient data available for mining. Demographics, office visits, procedures performed, return visits and success of outcomes are just a few examples of patient data that can be aggregated and used for decisions that can advance patient-centered outcomes.

Collaboration and open, transparent departmental interactions can help to determine which measures and data elements are most useful for data-driven decisions similar to the models used by Netflix. This exploratory approach can help drive the discovery of which practice patterns deliver exceptional, individualized care and improve the healthcare experience of every patient.

Conclusion & Best Practices

Analytics, when coupled with comprehensive, timely and reliable data sets, holds the potential to transform entire industries – including healthcare. But a solid information foundation is a prerequisite. A strong governance, driven by leadership, is essential to ensuring that the foundation is both firm and resilient.

Among the best practices that are essential to the effectiveness of data governance in healthcare are:

  • Establishing a leadership-led governance steering committee to guide the data governance and analytics implementations
  • Evaluating data sources to identify hidden information assets
  • Creating channels for collaboration throughout the enterprise focused on continually optimizing the use of data and analytics
  • Automating the processes involved in transforming raw data into meaningful information
  • Leveraging full-function analytics platforms that provide integrated governance and automation capabilities
  • Implementing healthcare specific analytics solutions with standardized measurements that provide a starting point for integrating the use of analytics throughout the organization

With the emergence of modern analytics platforms, tools for integrating governance into analytical processes and the availability of more comprehensive and meaningful data, healthcare will increasingly be in a position to apply lessons from other industries to both sustain the organizations that provide healthcare and improve the health of entire populations.

George Dealy is vice president of Healthcare Solutions at Dimensional Insight, where he is responsible for Dimensional Insight’s product direction in the healthcare market. Dealy’s past experience working with healthcare organizations to streamline information delivery mechanisms gives him a unique perspective on the challenges all organizations face in effectively distributing business-critical information to varied user sets. Dealy has more than 25 years of information technology experience with past senior management roles in business development, product management, professional services and sales. He holds a master’s degree in computer science from Union College and a bachelor’s in applied economics from Cornell University.



  1. Michael Lewis, 2003, “Moneyball: The Art of Winning an Unfair Game,” W. W. Norton & Company, and Wikipedia, Moneyball,
  2. Thomas H. Davenport and Jeanne Harris, 2007, “Competing on Analytics, The New Science of Winning,” Harvard Business School Press.
  3. James Titcomb, 2017, “Netflix movie codes: The secret numbers that unlock 1000s of hidden films and TV shows,” The Telegraph,
  4. Alexis Madrigal, 2014, “How Netflix Reverse Engineered Hollywood,” The Atlantic,
  5. Thomas H. Davenport, Jeanne Harris and Jeremy Shapiro, 2010, “Competing on Talent Analytics,” Harvard Business Review,
  6. Gary W. Loveman, 2003, “Diamonds in the Data Mine,”

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