Analytics, especially self-service analytics, is a hot topic in the healthcare industry these days. Many organizations are seeking to extract additional value from their electronic systems in order to gain business insights. For many institutions, facing constant downward pressure on profitability, analytics excellence is becoming a matter of survival. Many organizations have made wise investments that have allowed them to become leaders in the field. Others have spent millions on new technology, salaries and consulting fees, with little to show for after several years. With my present employer, we have managed to rapidly deploy a series of self-service analytic tools in less than a year’s time, without outsourcing or adding to our employee head count. During the same time, we have modestly shrunk our information technology head-count (by attrition). I am happy to share some observations on what has worked for us.
“For many institutions, facing constant downward pressure on profitability, analytics excellence is becoming a matter of survival”
Culture: It pays to stop and assess the culture to make sure that your analytics strategy is a good fit. For example, both centralized and decentralized data curation can be effective. Executive leaders may sometimes be impatient with obtaining clarity on these points as being too ethereal or tedious. Some encourage a “fail fast and forward” strategy. My experience is that healthcare institutions, by virtue of their regulatory and patient-safety cultures, ultimately push back on a highly entrepreneurial model. Likewise, academic and research wings of the institution may want highly localized control of data, while those with more hierarchical information practices will want to centralize them. In general, institutions with a strong tradition of program management and data transparency will find these characteristics to be synergistic with self-service analytics.
Sponsorship: Second only to culture is the importance of right sponsor. The sponsor should be an executive with significant influence and the respect of the organization, especially its clinicians. A good working knowledge of analytics, not just report-writing, is essential to a frugal model. An effective executive sponsor will shepherd analytics programs via influence and direction-setting, not by setting hard deadlines or objectives. Sponsors who push hard for certain endpoints and who want to over-manage the final product run the risk of wasting time and money on dead-end or low-value projects. The wise leader keeps a little distance from debates over solutions, and allows room for organic ideas to flourish.
Governance: Your governance body will ensure alignment of data definitions and set broad, institutional priorities. In chartering this group, there are several pitfalls to be avoided. One is to be too inclusive, which prevents the team from making forward progress. Another is to choose leaders who have a strong interest, but a narrow perspective, skewing priorities in a self-serving manner. An effective council will include senior leaders with experience in making data-driven decisions, and are facile at looking at problems across a wide continuum. Although not absolutely necessary, it is helpful if members hold advanced degrees in their respective fields.
Grow from within: The next ingredient in keeping your analytics budget under control is to look for underutilized talent among your existing employees. At first glance, this may seem a daunting task and something that will unnecessarily slow down the progress. Finding the time to identify emerging capabilities will bring long term benefits to your organization. You may find the right skills in your current report writers. Often they are found in unexpected places, such as educators, clinic managers, and nursing leaders. Our organization has been embarked on an initiative to train all leaders in the use of data to analyze and address waste in the system. A side benefit of this effort is the discovery of individuals with an aptitude for data science. Consider adding analytics proficiency to a number of different roles in a way to create career pathways for those who have the inclination and drive.
Know when and why to use consultants: In my experience, it is virtually impossible to jumpstart your analytics journey without at least some externally sourced talent. An effective consulting team may help with setting up governance in a way that neutralizes some of the organizational politics. Specific technical expertise is of great value when making large capital decisions related to data systems. We’ve also found that consulting groups that invest in mentoring and training are very cost effective compared to sending employees out of town for training.
End user engagement: Let’s say you’ve done everything right so far. This next one will make the difference on how efficiently you roll things out, and how fast they catch on. The most successful project seem to be built from the transaction level up, with data validation and end user experience baked into the pie. Doing this right diffuses the age old protest that the “data isn’t right”, and begins to inculcate principles of data stewardship and exploration. We’ve also found that it is a highly effective way to glean organizational priorities, as end-user engagement will tend to be higher where there is a greater hunger for reliable and timely analytic tools.
The last point to mention in this practice is to manage the disruptions that will inevitably occur. Existing report writers may naturally feel that their careers are threatened. Departments that are heavily invested in organizing and presenting data may wrestle to keep control over data distribution. Human resources may experience barriers in market analysis of job descriptions that now have additional data analysis expectations. Staying ahead of potential backlashes and roadblocks are key to keeping analytics initiatives on time, and on budget. With a strong cultural foundation, sponsorship and governance, you should be able to strengthen the capabilities of your current workforce and leverage frugally-deployed analytic tools to do great things.
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