What sets apart companies that are able to truly excel with their analytics implementations and those that struggle with issues such as adoption? I don’t hold all the answers, but during my numerous customer visits, I have noticed certain traits bubble to the top among the highest performing companies.
Here I have pulled together 4 best practices to creating a data-driven culture. These are traits I have noticed in customers who use analytics throughout their organizations and are achieving tangible results from their efforts.
1. Initiative is driven from the top
At many high-performing organizations, the analytics implementation is driven from the C-suite. If an executive is championing the initiative, it is much more likely to become part of the culture, as employees will be expected to be looking at the same data in the same way that their managers are looking at it.
For example, at Doctors Center Hospital in Manatí, Puerto Rico, the analytics champion is the president of the organization. Every morning, Dr. Carlos Blanco wakes up, and his Diver dashboard is one of the first things he looks at. How is the census at each of the health system’s four hospitals? How is the ER volume? Has anything changed significantly from the previous day? Once Dr. Blanco gets his information from the dashboard, he starts asking questions of his employees. That is a great motivator for everyone in the organization to also understand what the numbers mean and what decisions they can make off those numbers.
At Allied Beverage in Carlstadt, N.J., analytics is embraced by both the CEO and the CIO. When Brian Margolies joined the organization as CIO nearly a decade ago, he championed greater use of data and helped expand the company’s Diver implementation from a sales-only initiative to an enterprise-wide initiative. Allied’s CEO also sees the value of analytics and has advocated for its continued expansion in all areas of the enterprise.
2. Analytics steering committee
Other high-performing companies have analytics steering committees that help determine the most meaningful data projects and provide prioritization. These steering committees are often comprised of executives in different departments throughout the organization and help ensure widespread adoption.
For example, EvergreenHealth, based in Kirkland, Washington, has an analytics steering committee that is co-chaired by the chief medical information officer for the hospital and the chief medical information officer for the ambulatory group. The committee is comprised of executives from multiple departments within the hospital. It helps the IT team determine which analytics projects will have the most immediate and meaningful impact. This, by nature, helps provide some prioritization to the multiple project requests that the IT team gets from users and allows the team to focus on what will generate results.
3. Tying analytics into strategic goals
Every organization has (or at least, should have) strategic goals that it is working towards. In the healthcare world, those goals are often tied into providing better patient care. In other organizations, strategic goals are varied, but could relate to improving shareholder value, providing better customer service, or ensuring employee satisfaction. Those organizations that excel at analytics often keep these goals front and center when determining how they will gain insight from their data.
For example, Munson Healthcare, based in Traverse City, Michigan, has a guiding strategy for its organization called “True North.” This strategy is centered around the patient, but also includes goals around safety, operational performance, quality, and the healthcare team. As the organization builds out its analytics projects, it creates them with these goals in mind. The strategical framework ensures that the data team is always creating projects in each area of importance. It also ensures that the overall goal – the patient – is always center of mind.
As a result of tying analytics to its strategic goals, Munson Healthcare has been able to see some phenomenal results as a result of increased insight, including a 24% reduction in newborn readmissions, a 14% increase in patient satisfaction in its Women & Children’s department, and a 10% to 41% decrease in C-section rates among its hospitals.
4. Having an analytics resource
Providing self-service analytics is the goal at many organizations. And while that is important in order to increase adoption and improve results, companies are finding that self-service with zero guidance is not helpful in obtaining results. The most data-driven companies provide self-service, but with an analytics resource who can provide guidance and ensure that users are asking the right questions to get the most meaningful results.
For example, at Campari America in New York City, Matt Enny is well-versed in Diver and serves as the primary analytics resource for employees. The organization has Diver users in sales, marketing, and finance, and most of these people are daily users of the system, individually diving into the numbers and creating relevant reports in their areas of expertise. However, Enny serves as the overall Diver resource, able to answer questions for users or direct them to the right resources.
The same is true at Western Maryland Health System, based in Cumberland, Maryland. While many users at the hospital are using Diver to create meaningful projects, such as this acetaminophen project that resulted in cost savings of 78% created by Surender Kanaparthi, director of pharmacy services, the team relies on business intelligence analyst Colby Lutz to provide the overall guidance and serve as the main resource. The result is innovative ideas created at the point of care, with the assurance that the team is implementing these projects in the most straightforward way possible, and is doing so with data that is trustworthy and consistent across the organization.
Learn how to become more data-driven
If you’re reading this post and think, “My company ticks none of these boxes,” never fear. I have some tips to help improve the data culture within your company. We’ll take a look at those in a future blog post.
- How Data-Driven Decisions Can Drive Changes in Healthcare
- How Curiosity Can Improve Decision-Making
- How to Motivate Your Team to Get Analytics Results
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