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While data governance continues to evolve as a discipline, we view it as the process by which online marketing and digital analytics organizations define and manage different types and categories of data related to behavior tracking, audience measurement, ecommerce and other aspects of online business. Synonymous with “quality control,” data governance strives to ensure companies have reliable and consistent data sets to assess performance and make management decisions.
It’s critical to remember that effective governance is not a one-time exercise, but rather a fully developed effort and repeatable process. That’s the only way to ensure ongoing compliance with corporate standards and requirements, data integrity in light of future changes (like evolving business challenges, emerging technologies and new data flows). Good judgment and sound decision making are at the heart of data governance.
There is a fairly common perception that governance is all about policies, and having them captured in a full set of documents that live on the company servers somewhere. But the most effect governance programs are active and ongoing, consisting of regularly scheduled audits, reconciliations, compliance reviews and quality control activities. Following are most commonly done mistakes by companies about data governance
Mistakes to Avoid When Launching Your Data Governance Program
Mistake #1: Failing To Define Data Governance: The most common definitional mistake companies make is using “data governance” synonymously with “data management.” Data governance is the decision-rights and policy-making for corporate data, while data management is the tactical execution of those policies. Both require executive commitment, and both require investment, but data governance is business-driven by definition, while data management is a diverse and skills-rich IT function that ideally reports.
Mistake #2: Ready, Shoot, Aim: Failing To Design Data Governance: As with any strategic initiative that enlists both business and IT and is process-centric and highly visible, data governance must be designed. Designing data governance means tailoring it to your company’s specific culture, organizational structure, incumbent ownership environment, and current decision-making processes.
It means articulating the value proposition for cross-functional and formal decisions about corporate information—whether by minimizing compliance exposure or security breaches, over- or under-communicating to customers, consolidating product catalogs, or supporting dozens of other potential business drivers. No two companies treat these issues in exactly the same way, and data governance is never exactly the same across companies.
Mistake #3: Prematurely Launching A Council: Well-meaning people who see the need will gravitate toward the “who” conversation (Who should be on the council? Who will sponsor it?) before understanding the “what” and the “how.”
Until a core team of stakeholders deliberately designs a data governance framework (including guiding principles, decision rights, and the appropriate governing bodies), no sanctioned, cross-functional council will have either the clarity or the mission to affect change.
Mistake #4: Treating Data Governance As A Project: In a well-intended effort to fix what’s broken, many companies will announce data governance with much flourish and fanfare. An executive might assemble a cross- functional team, extracting its members from existing projects, creating an ersatz data governance SWAT team.
Others will hang the Center of Excellence shingle and treat data governance as an isolated organization of data-savvy individuals. Still others will inaugurate a data quality task force and call it data governance. In each example, data governance is formed as a discrete effort, when in fact it should be “baked in” to existing development and decision-making processes.
Mistake #5: Ignoring Existing Steering Committees: A key indicator of data governance success is an environment that encourages decision-making bodies. Call them councils, steering committees, management roundtables, or advisory teams. These bodies are usually composed of individuals from across business functions who have both the authority to make decisions and the accountability to ensure that those decisions are enacted and ultimately drive business improvements.
By inviting incumbent decision-making bodies to participate in the data governance process, you effectively institutionalize data governance as a component of corporate policy making. You also implicitly enlist the support of a variety of individuals, and change occurs one person at a time.
Mistake #6: Overlooking Cultural Considerations: Changing entrenched organizational paradigms and behaviors is perhaps the biggest obstacle for any governance effort. Examples include a corporate culture that stresses consensus over clear accountability, the absence of decision-making protocols, individuals unaccustomed to making decisions, or poor communication and planning. Common organizational constraints can derail governance before it begins.
Regardless of your organization’s explicit structure and biases, establishing unambiguous decision rights is a requirement for governance to thrive. Existing cultural norms should inform, but not necessarily dictate, how decision rights and accountability are assigned. Effective governance often challenges intrinsic ideas about what decision making means. Therefore, the governance program must also clearly articulate its mission and value, develop communication plans, and plan for, champion, and reward change—often one business constituent or person at a time.
Mistake #7: Prematurely Pitching Data Governance: In today’s environment, executives and staff alike are wary of the sweeping reforms and lofty benefits typically promised by enterprise programs. (Remember CRM?) As a result, even the most crucial enterprise data governance effort can start with one mark against it. Soliciting C-level executive sponsorship, broadly evangelizing expected outcomes, or establishing working teams without a clearly defined vision or framework to achieve the intended solution are all fraught with risk.
In the first phase of its data governance program, a national financial services company solicited several business and IT subject-matter experts to function as data stewards. The stewards were tasked with identifying high-impact data issues within their domains that governance would rectify. The stewards did an excellent job. The problem: there was no defined procedure to validate, prioritize, or resolve the ever-increasing flood of identified business problems whose root causes could be attributed to data issues.
Mistake #8: Expecting Too Much From A Sponsor: Executive support and management sponsorship for data governance are critical. A motivated sponsor, with a clear vision and the ability to communicate it to both senior executives and those he manages, is an important contributor to governance success. That being said, there is a limit to what even a great sponsor can be expected to do.
Consider this scenario: A company initiates data governance after a strategic business intelligence program fails to deliver expected results due to various data issues. Sponsorship for the data governance program will change over time to reflect current business priorities and needs. The framework and process under which governance executes should not.
Mistake #9: Relying On The Big Bang: The mantra think globally, act locally is particularly apt when embarking upon data governance. The issues addressed by data governance are far-flung and pervasive, ranging from arbitration of cross-functional data usage to information privacy, security, and access policies.
As a result, governance initiatives attempting to address an array of enterprise needs in one big bang are quickly squelched by role confusion, prioritization debates, “emergency” development projects, and a general backlash of the incumbent culture. Add the inevitable kinks to be worked out in any new process, regardless of how considered the design, and failure inevitably follows.
Mistake #10: Being ill-Equipped To Execute: Most companies do a good job of implementing governance policies within the scope of an initial business process or application release. However, the need for ongoing maintenance and auditing is frequently overlooked or underestimated.
Because data is constantly being generated, new applications are added, business processes change, and users come and go, data management becomes a full-time endeavor. Anyone who has been involved in a massive, one-time data clean-up or conversion project, only to have “dirty” data reappear over time, understands this all too well. Vigilance is required to monitor compliance with existing standards, enforce new behaviors, and ensure that old habits don’t creep back into common usage.
One size does not fit all. To avoid these risks, successful programs begin with a series of tightly scoped initiatives with clearly articulated business value and sponsorship. In the case of one pharmaceutical company, a state compliance reporting project served as the initial proving ground for governance. As state reporting issues were resolved, the director of compliance unveiled the program’s success to other decision makers in their respective areas.
The success of the initiative was related to the effectiveness of data governance, thereby encouraging participation among additional stakeholders and helping to enlist new sponsors. The design of your governance must address the unique challenges and biases in your organization. Although change is hard, companies with effective governance processes can generate up to 40 percent higher ROI on their IT investments than their competitors, according to researchers.
At easySERVICE Data Solutions, we define data management as the tactical, day-to-day execution of data governance policies. For example, a typical data governance policy may mandate that sensitive customer data be stored in secure formats and available only to authorized users. Implementation of an appropriate storage algorithm and ongoing maintenance of user permissions are data management functions typically handled by resources in IT, security, or by a formally designated data management group. Such a group should be equipped to tackle these issues as the business continues to evolve.
If you’d like to discuss any of the above best practices or lessons learned with us or to learn more about how we are partnering with companies just like yours to ensure the availability of mission critical applications, please contact us at (855) US STELLAR. When it comes to governance, patience and perseverance really do pay off.