Creating a Data Governance Framework That Drives User Adoption

Data

Creating a Data Governance Framework That Drives User Adoption

In this six-part series, we’ve discussed the adoption history, planning a BI Rollout, decentralizing ownership of data workflows and operational definitions of key terms like data governance, data lineage and data provenance. We discussed change management and ideas for spreading data literacy throughout your company in every team and for every user persona. Then, we discussed the need for system maintenance and some of the services InterWorks provides to help you succeed in every one of these areas. 

This final post in the series will discuss data metadata KPIs, how to capture wins credibly, processes for validating the wins and ways to share those best practices throughout your company. 

Monitoring Metadata 

When a Business Intelligence system is deployed, the most time-consuming step is auditing the quality of the data coming from your data sources, correcting errors, eliminating missing information and loading your data warehouse with reliable and useable data. 

Using data transformation tools and coding is necessary to automate these data workloads. A byproduct of this process is metadata on your data that includes: 

  1. Identifying missing data. 
  2. Correcting and completing data. 
  3. The volume and velocity of data. 
  4. The timeliness of data. 
  5. The sources of data. 

This metadata can become the basis of building a governance incentive plan framework that is objective and measurable at every level of your company. 

Business Intelligence Team’s Role 

The technical experts who build the ingesting and transformation workflows should be tasked with creating metadata report cards for each manager responsible for a data workflow. They are the independent observers. 

They are also internal experts who should be available to all of your operating managers so that they make the best use of their data. 

Operating Unit Manager’s Role 

These are the people who run different parts of your business. They are responsible for overseeing multiple data workflows. Their incentive plans can be updated to include consideration of: 

  1. Improving the metadata of the operational workflows they manage. 
  2. Improving the education of their team. 
  3. Improving the adoption and use of the tools. 
  4. Improving their knowledge of data structure.
  5. Improving their knowledge of your selected software. 

Creating these incentives requires planning and knowledge. I’m not suggesting it is something you can implement in a week. A dedicated effort of months should be enough to get started. Perfection is the enemy of progress. Get started. You can adapt your plans as you learn more about the details. 

The Workflow Manager’s Role 

Adoption happens when the data tools developed become the easiest and best way for business managers to get what they need to make better decisions, deal with problems faster and understand opportunities sooner. 

If these managers learn basic data structure and your selected tools, they will understand what is possible and how to articulate their needs effectively. 

They will be able to communicate more effectively with technical resources to get what they need. 

Your Non-Technical Staff’s Role 

You must train everyone in the basic terminology of data and the best practices with your selected toolset and reward practical usage and outcomes. Skills testing run by your Human Resources team can be the first way to promote adoption.  

Once basic skills are proven, the focus can shift to process improvement and financial payback. 

How to Begin 

Develop a baseline for your business intelligence adoption rate. Determine an objective and measurable goal for adoption improvement through a governance framework that considers systems usage, data usage and successes that lead to quantifiable process improvement, cost reduction, sales increases or problem mitigation.  

Building inventive plans that also consider data skills acquisition and higher quality data creation creates a framework that improves the quality of your data assets and facilitates objective business process improvement. 

Create a governance framework that doesn’t focus only on security. Develop incentives that help you increase adoption, spread data competency and improve the health and efficiency of your business intelligence tools. This will set the stage for building an information culture that leads to business process improvement. 

Give us a call. Let us help you find your path. 

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More About the Author

Dan Murray

Director of Strategic Innovations
Creating a Data Governance Framework That Drives User Adoption In this six-part series, we’ve discussed the adoption history, planning a BI Rollout, decentralizing ownership of data workflows and ...
Business Intelligence System Maintenance An issue and an opportunity for nearly every existing business intelligence system is keeping it running well and minimizing costs.   ...

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