Actions > Words
In our second installment of busting Data Governance Myths (check out part 1 if you missed it), we continue to explore treating data as a strategic asset. Access to high-quality, well-defined, and harmonized data for analytics remains out of reach for most of the healthcare workforce. Out of necessity, analytics and informatics teams are forming in other business units, creating a rogue wild west of data use across organizations.
Data Governance in Healthcare: Myth Busters Edition, Part 2 of 2
As industries evolve and assets become critical to a business the need to manage, fund, govern, and apply structure to these assets develops. Technology, procedures, policy, and formal decision rights and accountabilities emerge. I would argue that when an asset becomes critical to a business, managing that asset becomes part of everyone’s job description.
For example, I do not work in Human Resources. I do, however, have specific decision rights and accountabilities related to our company’s workforce. My job description includes tasks for hiring, coaching, and managing employee performance. It also includes requirements for meeting certain performance standards as does every other job description.
Working towards better data should be part of everyone’s job
We each collect and interact with data in unique ways. We have gained experience regarding data use, data nuances, and data quality issues. It is time to tap into the value of the tacit knowledge that exists in each organization and divide and conquer the wild west of data together. It is our workforce that will enable scale, and the institutional knowledge they possess about the data.
Let’s dive back in to some common data governance myths and reveal the truth.
Misperception #3: Data Governance is new work, and we do not have the resources to do it.
Truth: Your Organization is already doing Data Governance Work
A simple survey will provide an estimate of resources currently allocated to resolving conflicting metric definitions, working around data quality challenges, tracking down the best source of the truth, and obtaining access to data. Implementing enterprise capabilities for metadata, master data, reference data, and data quality management will result in countless efficiencies.
A Data Governance Office can be launched with just one full-time FTE and the partial engagement of resources currently working on data quality, data definitions, and master data challenges. Asset management tasks will be strategically divided up across your entire workforce, duplicate efforts will be significantly reduced, and what is learned and discovered will be published for reuse by all.
An executive once said to me, “I just want to Google It”. Working with healthcare data should be as easy as working with Google! Divide and conquer. Give everyone a small data role and watch data literacy elevate across your entire organization.
Misperception #4: Standing up Data Governance is too hard. We can’t boil the ocean.
Truth: Deploying enterprise data asset management capabilities is the solution.
There is more than one type of approach to data governance. Find the right partner.
Look for partners focused on:
Custom Advisory Services
Each organization has its own specific culture, available resources, and desired rate of change. Look for agile programs that will meet you where you are at, work with existing resources, and at a flexible pace.
Continuous Guided Action
Look for a partner who doesn’t want to “do it to you” or “do it for you” but instead, will enable you to govern your data by using existing resources. Operationalize enterprise capabilities that will sustain and continue to mature long after the partner exits.
Rooted in Asset Management Capabilities
Use techniques you are already familiar with and skills your organization already has. Inventory your assets, assign specific decision rights and accountabilities, proactively manage the quality of critical data elements, standardize processes and back them with policy. These capabilities, when applied to data, will result in the elevation of data literacy, data trust, and the data analytics abilities of your internal resources.