IQ Challenge

How is information quality impacting your business results?

According to Gartner (https://www.gartner.com/smarterwithgartner/how-to-create-a-business-case-for-data-quality-improvement/):

Poor data quality destroys business value. Recent Gartner research has found that organizations believe poor data quality to be responsible for an average of $15 million per year in losses.

Here’s a list of questions that every organization should try to answer.

• What’s the impact of data management on strategic business goals?

• What’s the financial value of the organization’s data?

• How doe’s data management impacts business process KPIs

• What’s the cost savings due to more efficient data maintenance processes, automated data cleansing/data import processes?

• What’s the impact of data quality in the satisfaction of customers, consumers, or business partners?

• How fit for use is the organizations data?

• What’s the number of corporate data quality related violations during an audit?

• What’s the satisfaction degree of the organization’s internal stakeholders as data consumers in business processes?

• What percentage of use cases are fully supported by data management?

• What percentage of data domains are covered by governance processes?

• What percentage of data records are covered by quality rules?

• How much information is unusable due to data quality problems?

• What percentage of recipients didn’t receive your email because it went to the wrong address?

• How much does it cost to store your data, and what percentage of that data is redundant or dark data?

Information Quality Challenge

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 are 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 an incredibly 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.

• Targeted.

• 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 small

• 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.

Assess

• 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.

Measure

• 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.

Improve

• 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.

Monitor

• 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.