One of the most common challenges organizations face when developing an enterprise data governance program is the presence of data silos or pockets of data initiatives scattered throughout the company with little to no association or collaboration of efforts. While many describe their data systems as “siloed,” the disjunction is less about the underlying technology and far more about the division created between lines of business. This is because the complexity in business processes and misunderstandings about the multiple purposes of data use typically result in disagreements and exclusionary data practices.
To create an organizational structure that promotes enterprise data efforts and does not foster silos, it is necessary to first understand the two functions of data practices: efficiency and effectiveness. The core purpose of these data practices differs so dramatically, there is no natural alignment. With the wrong organizational structure and leadership, the efficiency and effectiveness of data can quickly become opposing forces and wreak havoc on the business.
The efficiency of data focuses on reducing costs of data management, mitigating risks in data use, and optimizing technology for data access and availability. Success is a measure of speed and cost. Those who have roles in data efficiency are measured and incented on lowering expenses and eliminating unnecessary efforts. Because of this, people in roles of data efficiency are judicious and prudent and are more likely to say no to data “ideas” until a full business analysis is made.
The effectiveness of data is all about possibilities, opportunities, and capabilities. Effectiveness is often phrased as “getting things done.” There is little concern for process since the primary focus is on achieving a desired result. Data effectiveness is typically where the excitement for big data begins because the inclusion of big data capabilities presents fantastic opportunities. Data exploration and experimentation are core practices of those in roles of effectiveness. These individuals embrace failure and often see it as necessary on the path to success.
Combine the opposing forces of efficiency and effectiveness with the challenges of siloed business departments and you have the perfect storm for data chaos. Don’t take this the wrong way, though, because a healthy combination of efficiency and effectiveness is required for businesses to function successfully. It is a difficult balancing game because with too much of either side, you create new problems, especially in scale.
Peter Drucker, often hailed as the founder of modern management, once said, “There is surely nothing quite as useless as doing with great efficiency that which should not be done at all.” This is the trap of too much focus on efficiency within an organization. Whether executives heavily focused on financial reporting create the imbalance, there are too many roles of efficiency throughout the organization, or the culture is fixated on increasing margins, too much focus on optimization can stifle innovation, limit growth, and create an organization that is unable to adapt to change.
At the other end of the imbalance spectrum, if too much time and attention is given to effectiveness, the organization is put at greater risk. Because there is little focus on how projects are executed in “effective-heavy” businesses, costs are not monitored, standards are not created, and risk exposures are not considered. When the only goal is to achieve a specified, desired result, organizations tend to disregard or completely discard existing processes. The lack of monitoring, processes, or standards removes reuse potential and extensibility for future projects throughout the organization. Just because the business is “getting things done” does not mean tasks are being completed in the best manner.
Drucker also said: “Efficiency is doing things right; effectiveness is doing the right thing.” Getting the right balance of efficiency and effectiveness is the key to defining your data governance program and associated organizational structure. To extend Drucker’s premise, efficacy is doing the right things right. It is the extent to which a desired effect is achieved successfully. To realize data efficacy throughout the organization, the business must clearly define the desired effect (by the effectiveness folks), the measures that define success (by the efficiency folks), and then design the strategy to execute together.
The two functions of data practices are efficiency and effectiveness, but the core purpose of each differs so dramatically, there is no natural alignment.
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