As more organizations realize the critical importance of viewing data as a corporate asset, data quality is an increasingly prevalent topic of discussion, especially within the context of establishing a data governance program.
Data governance maturity models describe an organization's evolution through a series of stages intended to measure its capability and maturity, its tendency toward being reactive or proactive, and its inclination to be project-oriented or program-oriented. Historical approaches to data quality relied on reactive data cleansing projects focused on correcting existing data problems, but without resolving their root cause - and in some cases, without even identifying it.
Data governance, like other enterprise initiatives, is a series of policies, processes and interactions. What might go ignored when we reflect on the success or failure of these programs are the social and collaborative aspects that impact their ability to support business goals. Often the root cause of poor data quality can be traced to the lack of a shared understanding of the roles and responsibilities involved in how the organization is using its data to support its business activities. A proactive approach to data quality requires going beyond project completion dates and establishing a pervasive program for ensuring that data is of sufficient quality to meet the current and evolving business needs of the organization.
Although a common definition for data quality is fitness for the purpose of use, most data has multiple uses, each with its own fitness requirements. One of the central concepts of data governance is its definition, implementation and enforcement of policies which govern the interactions between business processes, data, technology and, most important, people.
People empowered by high quality data enabled by technology can optimize business processes for superior performance. Data governance policies for data quality illustrate the intersection of the business, data and technical knowledge spread throughout the enterprise, and transcend artificial boundaries imposed by an organizational chart (where different departments or business functions appear as independent of the rest of the organization). Recognizing the need for data governance reveals how truly interconnected andinterdependent the organization is.
Ownership, Responsibility and Accountability
Data is a corporate asset collectively owned by the entire enterprise. A data quality program within a data governance framework is a cross-functional, enterprise-wide initiative requiring that everyone, regardless of their primary role or job function, accept a shared responsibility for preventing data quality lapses and for responding appropriately to mitigate the associated business risks when issues do occur. Data governance not only reveals the business value of the organization's data but also reveals the communication and collaboration necessary to materialize that value as positive business impacts.
The enforcement of data governance policies is often confused with traditional management notions of command and control, which is the antithesis of what it takes to successfully implement a data governance program. What data governance really demands is an organizational culture that embodies collaboration.
A Collaborative Culture
Although all organizations must define success in business terms (e.g., mitigated risks, reduced costs or increased revenue), collaborative organizations understand that the most important factor for enduring business success is the willingness of people all across the enterprise to mutually pledge to each other their communication, cooperation and trust. The collaborative culture of these organizations pass what former U.S. Secretary of Labor Robert Reich called the pronoun test: when the employees of a collaborative organization make references to the company, it's done with the pronoun "we" and not "they" because "they" suggests at least some amount of disengagement, and perhaps even alienation, whereas "we" suggests the opposite - employees feel like part of something significant and meaningful.
As Daniel Pink explained in his book "Drive: The Surprising Truth About What Motivates Us," historical management techniques emphasize extrinsic motivation (external rewards and punishments, i.e., carrots and sticks), whereas 21st century management techniques emphasize intrinsic motivation (yearning to do what we do in the service of something larger than ourselves, i.e., purpose). Employees of collaborative organizations thrive because of the intrinsic motivation that comes from working toward the organization's shared and united purpose.
All too often, the initial focus of data governance is on how the organization is going to start doing things differently, how people's behaviors will be expected to change (and often demanded to change by executive management). However, one of the greatest sources of organizational resistance to change is the lack of a clear understanding of why change is necessary. A data governance program needs to begin by communicating why data governance is necessary before communicating how it should be done. To paraphrase Friedrich Nietzsche: "An employee who has a why to work for can bear with almost any how."
In his book "Start with Why: How Great Leaders Inspire Everyone to Take Action," Simon Sinek explained that it is not the products or services that bind a company together or make it strong; it's the culture, the strong sense of beliefs and values that everyone, from the CEO to the receptionist, all share.
An Interconnected Enterprise
Most people have heard the theory that each person in the world is connected to everyone else by no more than six degrees of separation (i.e., your friend is one degree from you, your friend's friend is two degrees from you, and so on). And many of us probably learned this theory from playing Six Degrees of Kevin Bacon (the movie trivia game where any actor can be linked through his or her film roles to actor Kevin Bacon within six steps).
An organization is comprised of a complex social network of people, but on a daily basis, it's often not readily apparent how interconnected the organization is. However, it's true that within even a large organization, each employee, from the lowest level intern to the highest level of executive management, is connected to every other person in the enterprise by no more than six degrees of separation. So although we frequently think of ourselves as individual employees separated from one another, we work within an interconnected enterprise, and therefore everything we do affects the whole organization.
But a natural organizational myopia restricts our vision to seeing only what is right in front of us, and most of the time that is not a person - it's a computer screen. We need to start viewing our computer screen as a two-way mirror of sorts, which allows us to not only reflect on how others affect us, but also see through the looking glass how we affect others. For example, when viewing a report, envision not only the business analyst who prepared it, but also the data steward who verified the data quality, the technical architect who designed the database, and the data entry clerk who created the data. It's easier for people to take others for granted when they only see data as a by-product of business processes and technology, which is why it's important to start seeing everything that happens within the organization as the interactions occurring among its people.
The Limitation of Influence
In their book "Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives," Nicholas Christakis and James Fowler detailed a theory called three degrees of influence: "Everything we do or say tends to ripple through our network, having an impact on our friends (one degree), our friends' friends (two degrees), and even our friends' friends' friends (three degrees). Our influence gradually dissipates and ceases to have a noticeable effect on people beyond the social frontier that lies at three degrees of separation. Likewise, we are influenced by friends within three degrees but generally not those beyond."
The limitation of influence to within only three degrees of separation is an important consideration for the planning and execution of a data governance program. Many industry analysts compare and contrast top-down and bottom-up approaches to data governance. The top-down approach is where executive sponsorship is emphasized. Although it's true that the sustained success of data governance requires executive sponsorship, grassroots advocates acting as bottom-up peer level influencers make more effective change agents than executive management, who are beyond the social frontier that lies at three degrees of separation from most employees.
Data governance requires a significant and sustained change management effort. You need to unite the organization around a shared purpose, encourage collaboration and elevate the change to a cause. Although some people will answer this call to action, many others will need more convincing. However, in the early stages of data governance, don't try to convert the naysayers. Instead, recruit those who are already willing to become champions of the cause. This army of change agents, who will adopt new best practices and lead by example, will facilitate widespread adoption of the data governance program because they operate within the social frontier of three degrees of influence over their peers. The data governance cause will permeate the enterprise by radiating from this social network of people who embody the ethos of a collaborative culture.
Data governance enables the organization to manage its data as a corporate asset, for which the entire enterprise has collective ownership and a shared responsibility, but that also requires individual accountability for specific roles. Organizations that successfully implement data governance view collaboration not just as a guiding principle, but also as a call to action in their daily practices. Successful organizations have this united and shared purpose, which fosters a culture of collaboration, allowing them to rely on the strength of their people assets in order to successfully manage their data assets.
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