A Recipe for Analytics, Key Ingredient #3 – Data Governance
This 4-part blog series focuses on building a successful analytics strategy with each of these 4 ingredients.
- Very liberal amount of Data Content
- Proportional dose of Infrastructure
- Quart of Data Governance
- Several stalks of Analytics Competency
Some people are very stingy with their recipes. The chef at a restaurant with really good food may tell you generally how they cook up that amazing meal but you always suspect that a critical secret ingredient has been left out, something that makes a noticeable difference in the way the food looks and tastes. Let’s continue our cooking theme in this blog series and talk about our next key ingredient for healthcare analytics, data governance. Data governance is that basket of secret ingredients on Chopped that are a part of every dish. Data governance is a part of every analytics program and project whether you realize it, acknowledge it - or not.
Key Ingredient #3: Data Governance
Data governance is often a mystery to healthcare organizations; “What is it? How do I do it? and Where can I buy it?” are some of the thoughts it provokes. Data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise. Data governance ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality. It’s the management of the availability, usability, integrity, and security of your organization’s data asset(s). Best practice data governance programs include a governing body, and this body works to set standards, refine procedures, and creates accountability for all data assets.
Data governance typically happens organically at first in organizations without analytics programs or new to analytics. As data issues and concerns arise or are more frequent a more formal management led effort typically evolves. If data governance is not apparent then data is not being governed, and data quality and credibility will inherently be suspect.
The HIMSS Analytics Adoption Model for Analytics Maturity (AMAM) provides a roadmap to help healthcare organizations to begin and advance their analytics journey, with one of the model’s focus areas being data governance. The AMAM helps organizations develop data governance in a natural evolutionary way.
The lower AMAM Stage “data governance secret ingredients” are designed to initiate and help organizations get a handle on data quality, security, timeliness, and propagate understanding about data assets.
Higher level AMAM Stage data governance requirements ensure analytical activities are “shake-and-baked” to strategically align with the organization’s planning. Stage by Stage improvements of data governance are represented by these key ingredients:
Stage 0: Fragmented solutions and approach
Stage 1: Analytics strategy with executive support, regular meetings
Stage 2: Patient registry evolution, Master Data Management, data literacy
Stage 3: Standard terminologies, external data release policy and process
Stage 4: Widely accessible analytics driven dashboards track KPIs
Stage 5: Supporting organization-wide quality-based performance measurements
Stage 6: Accountable for managing the economics of care (cost & quality)
Stage 7: Tightly aligned with the organization’s strategic, financial, and clinical leadership
The AMAM data governance requirements for each Stage are designed as a roadmap to help healthcare providers assimilate a strong, personalized, well rounded data governance program that enables a wide variety of clinical, operational, and financial analytics insights (recipes).
Other key ingredients in the analytic recipes call for fresh data, advanced analytical skills, and a supportive infrastructure. We’ll provide an overview of those in the other parts of this 4-part blog series.
If you have questions about AMAM or healthcare analytics please reach out to our client relations team and read more here.