As organizations rush headlong into digital transformation initiatives, a critical factor often gets overlooked: data quality and the resistance to support ongoing data quality efforts. In the race to implement cutting-edge technologies and overhaul business processes, many companies fail to recognize that the success of these efforts hinges on the accuracy, completeness, and reliability of their underlying data. This oversight can lead to disastrous consequences, undermining the very goals that digital transformation aims to achieve.
Data quality plays a critical role in digital transformation efforts and business performance.
The High Cost of Poor Data Quality
The consequences of neglecting data quality extend far beyond immediate financial losses. A study by Gartner found that poor data quality costs organizations an average of $12.9 million annually. More alarmingly, the same study revealed that only 24% of organizations rate their data quality level as “high.” This disconnect between the importance of data quality and the actual situation in most organizations is a ticking time bomb in digital transformation.
Industry Perspectives: Car Rental and Manufacturing
The Car Rental Company's Data Quality Awakening
A major car rental company was struggling to manage fleet investment. Executives used dashboards summarizing key performance indicators that tracked strategic investments. However, those key performance indicators depended on fleet managers’ manual data entry nationwide. This poor data quality led to misplaced investment, incorrect forecasting, and frustrated customers.
For decades, fleet managers have relied on manual tallies and line of sight of the fleet managers to ensure adequate inventory. This behavior has been hard for executives to change. This clash between traditional methods and digital demands creates a perfect storm for dirty data, potentially leading the entire company astray. The consequences of these disconnects between ground-level reality and top-level strategy can become far more significant than many realize.
Addressing the Data Quality Challenge
So, what can organizations do to address this critical issue? The first step is acknowledging that data quality is not just an IT problem but a business-wide concern that requires top-level commitment. Companies must establish a robust data governance framework with clear ownership and accountability for data quality across all departments.
Investing in data quality tools and processes is crucial. This includes implementing data profiling tools to identify issues, data cleansing solutions to correct errors, and ongoing monitoring systems to maintain data quality over time. However, technology alone is not the answer.
Equally important is fostering a culture of data quality awareness throughout the organization. This involves training employees on the importance of data quality and their role in maintaining it. As discussed in our previous blog on data literacy, data leaders should never invest in technology without considering investment in human skills - the “insurance policy” that ensures the adoption and effective usage of these tools [link when live]
Data quality initiatives should be viewed as a critical component of change management in digital transformation. By investing in technology and people, organizations can create a robust foundation for their data-driven future.
Plan for Resistance:
When we think of resistance, we often think of it at the time of its occurrence rather than planning for it. But what if we backed up a bit?
We should ask four or five important questions before deciding to accept a data project with a key stakeholder. The first is the most important, but it’s also the most bold, which is why most people don’t ask it:
What is your contribution to creating the thing you want to see changed?
If people think it’s others who need to change—direct reports, peers, board members, etc.—it will be a bumpy ride until they realize that they’re creating the world they’re inhabiting. Then, there’s a whole set of questions regarding how you confront people to acknowledge their role in the problems they wish to have solved. You are there to help them honestly examine their behaviors.
What doubts do you have about the way things are going?
What’s the resentment you have that nobody knows about?
What gifts are you trying to bring to this situation?
What deficiencies do you notice that should be filled?
Help the stakeholder explore and discover. (For more on resistance and practical guidance on managing sponsors and stakeholders, see Driving Data Projects.)
The rental car manager enabled poor data quality by remaining analog, and the executives enabled poor data quality by only investing in infrastructure and not capabilities, and not prepping their culture for change. Both will remain stuck until one of them moves toward progress (however that is defined or incented).
The Path Forward: Quality Data as the Bedrock of Digital Success
While cutting-edge technologies are alluring, the success of digital initiatives ultimately hinges on data quality. As demonstrated by the car rental and manufacturing examples, investing in data quality yields significant improvements in customer satisfaction, operational efficiency, and overall business performance.
Organizations must prioritize data quality as a fundamental aspect of their digital transformation strategies. This means investing in the right tools and processes and fostering a culture that values data quality at every level. By doing so, companies can avoid hidden pitfalls and position themselves for long-term success in the data-driven era.
CDOs and data leaders must take a holistic approach to managing data initiatives from inception to completion, aligning with their end-to-end responsibilities. This comprehensive view ensures that data quality remains a priority throughout the entire data lifecycle and across all digital transformation efforts.
Remember, in the digital age, your business is only as good as your data. Make it count.
For more insights on managing data quality and driving successful data projects, CDOs and data leaders must take a holistic approach to managing data initiatives from inception to completion, aligning with their end-to-end responsibilities. Check out the Data Management Association (DAMA) International's comprehensive guide on data quality management. This resource provides valuable strategies and best practices for organizations looking to improve their data quality practices.
References:
Gartner. (2020). How to Create a Business Case for Data Quality Improvement.
Haskell, C. (2024). Data Literacy: The $100 Million Insurance Policy You're Probably Ignoring.
Haskell, C. (2024). Driving Data Projects: A Comprehensive Guide. BCS.
DAMA International. (2017). DAMA-DMBOK: Data Management Body of Knowledge.