Data quality has always been a challenge for enterprises, but it has never been so challenging as it is now, with increasing data needs and more complex business environments.
Starting a data quality program with focus on leveraging the business strategy is an ambitious goal and often, the results are far from the expected in multiple levels.
When we look at the characteristics of the implementation of a data quality strategy in an organization some characteristics are easily identified:
These are expensive initiatives, they’re time and resource consuming and span through long time frames.
Also, they are deeply intrusive and disruptive, creating the natural resistance to change within the organization, creating a very challenging ecosystem to work on.
Finally, we are talking of the kind of initiative that might take years to break even and deliver ROI, making it hard, even with a strong sponsorship, to keep the necessary traction to complete all the necessary changes.
In these last few years I had the chance to test a less disruptive approach that allows organizations to start their data quality programs and quickly gain traction.
The application of these principles will allow to leverage more ambitious programs, starting with projects that have in common the following attributes:
- Reasonable funding model.
- Focused effort.
- Short timeframes.
- Increase internal engagement.
- Delivering targeted return on a short timeframe.
A sequence of these targeted initiatives has the benign effect of increasing the awareness of the importance and impact of data quality across the organization, increasing the overall internal engagement, turning critics into evangelists and paving the way to a more structured and strategic approach enterprise wide.
- Start with business areas than can clearly identify and measure the business impact of bad data on their processes – In every organization the opportunities to identify these cases are abundant. Across all the business areas there are pain points related with the quality of data and identifying them is not a challenge.
- Build your business case with those willing to defend it. – Once you’ve identified a critical pain point, you’ll have the business stakeholder that can passionately and effectively articulate the impacts of poor data quality in their processes and that will be eager to defend the project.
- Focus on turning insights into action. – Having the business stakeholder working by your side will accelerate the process of quickly move from the findings to specific actions.
Establish data quality targets based on the criticality of data. – A deep understanding of the impacts of bad quality data on the business processes enables a more accurate prioritization of the criticality of the data, hence making easier to identify clear targets on an early stage.
- Understand the data environment. – With special focus on these three vectors: People, process, and technology
- Profile – Understand the data and its impacting factors. – A detailed profiling of the data and subsequent analysis of the results, identifying the root causes and possible remediation actions.
- Define specific metrics for data quality. – Engage business and IT stakeholders to define specific business rules to assess the data quality and create metrics that resonate to the business and that are aligned with the business strategy.
- Define performance measurements. – Create a framework of metrics that will enable a clear view of the evolution of data quality to all the stakeholders.
- Set performance targets. – Be ambitious with the pace that you want to print on the initiative.
- Design and implement quality improvement processes. – Act on the causes of the data quality issues on both the preventive (the process) and corrective (the data) fronts.
- Track the remediation progress over time. – It’s critical that a every moment the results of these actions are measured and given visibility.
- Data quality process is an ongoing process of assessing the data and remediating the identified data quality issues.
- Continuous monitoring will improve and sustain the quality of critical data and simultaneously emphasize the importance of data leading to better data quality.
- Making the option for data quality initiatives that are more focused and efficient creates and increases the awareness across the enterprise and ends acting as the motor from within the organization for a full Data Quality Strategy program.
About the Article Author: Jose Almeida is a Data Management Consultant for twenty years enabling organizations across the world to proactively manage their data asset and to address their challenges and gain more value from their data. Providing solutions through the usage of best-of-breed technologies and methodologies in the Data Governance, Data Quality and Master Data Management areas