Data Credibility: It's a Leadership Issue

Tuesday, July 28, 2020

The old adage, “Garbage In, Garbage Out” has been used to describe a variety of processes, but it has never been used more than to describe the effect of bad data on business decisions. An article in Harvard Business Review attempts to address “Data’s Credibility Problem” by suggesting that proper management is the key to solving the issues with data quality (Redman, 2013). Mr. Redman states that “the solution is not better technology; it’s better communication between the creators of data and the users, a focus on looking forward, and the shifting of responsibility for data quality from IT folks to line managers, who are highly invested in getting the data right.”  I agree with the author that the focus must shift away from technology in order to address the real data quality issues.  However, I believe that effective leadership, not management is where one will find the solution to the credibility issues. Mr. Redman was on the right path, he just stopped at management. The solution to poor data quality lies within the leadership of the organization and their ability to create a data quality culture, treat data as a strategic asset, and having a mature mindset with respect to their data governance.

It is said that management is making sure that people are doing things, but leadership is making sure they are doing the RIGHT things. In this context, one can see that the act of bringing data creators together with data customers is a management task whereas developing a data quality culture is a leadership endeavor. According to Anthony Fisher, organizations need a collaborative, aligned, and integrated data environment to facilitate the development of an enterprise-wide approach to data quality (2009). He further states that a “successful enterprise strategy will encompass three main elements:” people, process, and technology.  It is the people following the established policies utilizing the appropriate technology that help create a data quality culture. In order to reap the rewards of an enterprise data quality solution, an organization must be properly aligned, focused, and directed. In addition, the C-suite needs to “grease the wheels” by providing high-profile executive endorsement and sponsorship. The improvement of data quality requires “maneuvering the dynamics across various business groups (Minelli, Chambers, & Dhiraj, 2013).” Lastly, the organization needs to make significant investments in human capital. This last piece is incredibly important. As such, Avinash Kaushik developed the “10/90 rule” for technology and talent. His rule states that organizations should invest 10% in systems and technology and 90% in people. 

People are important. Employee engagement has a significant impact on the development of a data quality culture within the organization. According to Daniel H. Pink, “organizations can achieve excellence when their employees are driven by intrinsic motivation and not extrinsic motivation. Organizations need to create a culture that focuses on our innate need for autonomy (freedom over some or all aspects of work), mastery (to learn and create new things), and purpose (focus on the higher purpose that is larger than oneself) (Minelli, Chambers, & Dhiraj, 2013).” If this is the case, then organizations that seek to improve their data quality through organizational alignment, executive sponsorship, and significant investments in human capital stand to create a virtuous cycle where employees feel valued, believe in the quality of their data, and are proud to work for their respective organizations. 

The importance of executive support and leadership cannot be understated. It is absolutely imperative that C-level executives walk the talk with regards to a quality culture.  Mr. Lush states that a quality culture “doesn’t just evolve or happen by accident. It is a product of disciplined focused effort from the top down (Lush, 2013).”  Leaders demonstrate the behaviors they expect from others and set standards that provide a rules network. In addition, leadership cultivates a culture of continuous improvement not perpetual crisis management. “Changing your quality culture takes strong leadership, company-wide engagement, and disciplined execution (Lush, 2013).”  In order for the culture to develop, employees need to see organizational leaders actively engaged in the process and accept full responsibility for data quality within their respective business units. Once this has been accomplished, data quality will become everyone’s responsibility and not just IT.

The forward looking focus that Mr. Redman was referring to in his article was the ability of an organization to begin cleaning up its data entry/data management processes so that quality data would be collected from a given point forward.  It is understandable that organizations may not have the resources (financial, human, or temporal) to reach back and clean all of their historical data and establishing a focus on the new and near is cost-effective, but in the context of data quality, this is a management-oriented perspective. The ability to treat data as a strategic asset, one that can be used to propel an organization forward by learning from the past and predicting its future belongs to the realm of leadership. 

Furthermore, Damien Georges defines data governance as “the formal orchestration of people, processes, and technology to enable an organization to leverage data as a strategic asset (2013).” 

Finally, Mr. Redman contends that shifting the responsibility for data quality from the IT department to the individual lines of business will solve the problem, but he does not address the level of data governance maturity inherent within the organization. Damien Georges adds “the main problem is invariably the disconnect between business and technology resources (2013).” Since data doesn’t necessarily belong to any one group, everyone assumes that IT “will take care of it.”  Nothing could be further from the truth.  Everyone within the organization is responsible for the quality and governance of the data. In essence, a data quality culture is developed when its governance and management becomes everyone’s responsibility. 

Returning to the “Garbage In, Garbage Out” concept, one must realize that data quality is not an input, but an output generated by an organization based on its culture, strategic thinking, and data governance. Data is created, collected, managed, and consumed by the people and processes of an organization. Poor data quality is a symptom of a larger leadership problem which cannot be solved by the existing management approach. As Albert Einstein once said, “we can’t solve problems by using the same kind of thinking we used when we created them.” The organization that has a data quality issue must be willing to look at itself and change its mindset in order to truly improve its data quality. This is the role of leadership, not management.

References

Dale Carnegie & Associates, Inc. (2012). What Drives Employee Engagement and Why It Matters. New York, NY: Dale Carnegie & Associates, Inc.

Einstein, A. (2013, December 13). Albert Einstein Quote. Retrieved December 13, 2013, from BrainyQuote.com: http://www.brainyquote.com/quotes/quotes/a/alberteins385842.html

Fisher, A. (2009). The Data Asset: Govern Your Data for Business Success. Hoboken, New Jersey: John Wiley & Sons, Inc.

Georges, D. (2013). Data Governance: A Six Step Solution. Jersey City, NJ: Hipercept, Inc.

Lush, M. (2013). Quality Culture for the 21st Century: Is Your Quality Culture Fit for Purpose? The NSF-DBA Journal, 3-5.

Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Business. Hoboken, New Jersey: John Wiley & Sons, Inc.

Redman, T. C. (2013). Data's Credibility Problem. Management - Not Technology - Is the Solution. Harvard Business Review, 84-88.