A consultant walks into a board room, filled with the top Senior Leaders of an organization, and defines the importance of Data Governance. Does this sound familiar? Like a starting statement of a bad joke. But often enough, this is the beginning cycle of repeated Data Governance initiatives. Which most often leads most senior leadership to say, “That’s great but where is the Business Value?”
Many Data Governance or Management consultants know it is a hard pitch to convince leadership that Data Governance really will solve most of their operational and decision-making issues. For me, it took multiple trials to finally see Data Governance program activities put into action and move away from hypotheticals.
How did it happen? What was the “secret” to making an organization perfect and govern their data assets? A combination between executive leadership support, operational teams, and the coordination of cross-functional teams all directed by one mission! As well as to be patient and work closely with business stakeholders and stewards to solidify the best path forward. “Make our strategic data assets ready for both internal and external consumption”. How do you imbed and fix years of mismanagement of data while steering the boat down the best path? By putting into practice what myself and many teams called a “Data Readiness Framework”.
What is data readiness? In the most common of terms, it is a formal assessment to measure the maturity and readiness of data management assets, capabilities and processes. Where in which, during the final assessment phase, potential gaps and areas of improvement are highlighted and addressed to deliver trusted data.
The framework defined assesses not only capabilities of data assets but across its lifecycle, and at the most granular level. Where datasets, across multiple systems, models, operational processes, and teams, are constructively questioned for the current state. You maybe asking yourself, “Isn’t this a data maturity assessment”, and the answer is yes, but addressing and providing clear guidance to team members across an organization. The processes for data readiness include an initial assessment where cross-functional teams, who are involved with the creation, storage, and management of data are asked questions to the data sets maturity state. Where a question scores “low” or not optimal, it is flagged to be reviewed by both the facilitator and governance lead.
Each question is scored in a range (1-5), where both processes, capabilities, and team members are assessed for the dataset’s maturity. The criteria, as displayed above, include questions about the rules or expectations of governing and management of data assets.
Data Collection:
- A reliable process proven for collection of data
- An established data quality process for assurance and monitoring
- Understand customer expectations (business requirements)
Data is Mastered:
- Identified a database of record or master database for data
- Understand and/ or defined the digital rights of master data
- Properly tagged and classified
Data is Governed:
- Defined the business terms and technical metadata in a data dictionary
- Documented the data flow process with technology framework (i.e., ELT)
- Data quality metrics and rules are defined with established processes
Phases:
Initial assessment, is where the cross-functional team, is introduced and walks thru an assessment questionnaire. Depending upon the maturity of the cross-functional team, an initial assessment could take one workshop (1 hour) or multiple. But the point of the assessment is for everyone to understand the questions and “paint” the picture of current state.
After an initial assessment, there is a formal review (gap analysis) of questions that scored “low” and potential data risks. During a gap analysis, the data governance lead, provides the cross-functional team the results of each section and highlighted priority items to action upon. For instance, a data model that is to be published for internal consumption, doesn’t have a secure repository for sensitive or intellectual property inputs and outputs. This is a red flag to the cross-functional team and the data governance lead should address by defining key actions to imbed in the data project initiative. Last phase is the possibility for re-assessment, after a gap analysis report, which is up to the “Owner” and team to decide upon.
Over the last few years, I have applied this data readiness assessment on multiple “new” data projects, as well as legacy or BAU datasets in an organization.
Best practices:
- Work with your business stakeholders to apply the data readiness framework of critical data projects that are a part of their business goals.
- Data readiness assessments applied to “new” data projects may result in a lower maturity score. This is normal, and in fact, the data readiness can support project execution, if imbedded in the activities or sprints.
- This is a cross-functional exercise and needs participants to be open and transparent of current state. So, it is important that an “Owner” in a senior position who allows time to their teams to take part in this framework.
- What a perfect way to find areas for improvement!! You will have clear guidance and focus, based upon the gap analysis, and find high risk items.
- From a project management perspective, this is not a “Go” or “No Go” assessment but a guiding point for teams to prioritize their data governance and management activities.
- Lastly, there is always an opportunity to re-assess once items are actively addressed and executed upon.
A combination of thoughtful research, management, strategy and best practices will help you to implement processes to optimize your data.
