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Five Common First (Mis)Steps When Establishing Data Governance

Author: InvestTech

03 | 21 | 2016

CDO_5_steps.jpg

If you are new to a senior data role charged with establishing Data Governance, you already understand the definition of Data Governance.  Early in your new position, you may quickly realize that establishing governance is harder than the articles, consultants, and software providers suggest.  Knowing what to do is not as big a challenge as knowing when to do it.  What are the first steps you should take and what is more effective when approached later?  What can you learn from others who have faltered or failed?

Let’s take a look at the most common first missteps made by Senior Data Officers when establishing Data Governance and why they aren’t the best initial steps to take (though they do hold value and should be done at some point).


Misstep #1

Why you should NOT lead with it

The value of doing it at some point

Writing a Charter

  • Asking people for time to review and sign-off on a document without any context could be an instant turnoff.
  • Many stakeholders already view governance as a bureaucratic layer and additional overhead to their day jobs. Use their time wisely – especially in the earliest stages.

 

  • Not every company needs a full-blown Charter document, but it’s important to have a communication vehicle that summarizes the organization, processes, policies and execution plan.
  • The act of creating the Charter achieves greater alignment.
  • The Charter can range in scope and depth depending on the size, culture, and requirements of a firm.

Misstep #2

Why you should NOT lead with it

The value of doing it at some point

Prematurely announcing roles
across the organization

  • This is the most common first misstep.
  • The rationale is driven by the hope that if data owners/stewards are identified and announced, they will help champion the effort and provide momentum.
  • Assigning Stewardship (willing or not) will not accomplish either.
  • Assigning Data Stewards is a critical requirement and naming stewards is a key initial step.
  • Prematurely announcing roles without having other foundational elements in place could create more questions than you have answers.
  • Whether a federated model or a centralized model, ultimately someone has to own the definition, standards, and decision rights over the data.

Misstep #3

Why you should NOT lead with it

The value of doing it at some point

Conducting Data Analysis/Lineage

 

  • This misstep is not the act of conducting the data analysis early in the process, but committing to fully analyzing too many data domains before implementing any aspect of Data Governance.
  • By casting the net too wide, data governance resources are improperly allocated, extending the launch of Data Governance and causing business stakeholders to become impatient and lose interest in the DG program.
  • Stakeholders have a short attention span and need to experience the benefits of Data Governance early and often.
  • If the DG team is not visible, people tend to wonder what is really being done and what the value proposition is to the business.
  • It is imperative that firms understand their data, including the life cycle and root causes of their quality issues.
  • This step is a critical early step for any firm desiring to achieve and maintain Data Governance.

Misstep #4

Why you should NOT lead with it

The value of doing it at some point

Implementing an
EDM Tool

 

  • This misstep occurs when a firm goes too far down the path of implementing a data management software solution without any focus on Data Governance.
  • The cost and resource demands of the implementation could reduce support for the Data Governance program.
  • It is imperative for the business to understand that software is an enabler of governance, not the other way around.
  • If your end goal is to migrate data ownership from IT and establish common definitions and policies, having a data management software tool alone will not get you there.
  • For mid to large size firms, selecting and implementing a tool to manage and master your shared data is a big step forward in achieving greater efficiency.
  • Significant benefits can be observed, such as improvement in data quality, greater engagement of the business, and ease of access to data.

Misstep #5

Why you should NOT lead with it

The value of doing it at some point

Focusing on
day-to-day quality issues

 

  • DG teams want to clean up the data to deliver instant value to the business, but they should not spend the bulk of their time chasing and fixing reported bad data issues – ever!
  • This tends to result in “organizational quicksand,” stifling any Data Governance effort.
  • This trap is most common for data leaders who transitioned to the Governance role from a day-to-day operations/IT quality role.
  • This mindset instantly defines the Data Governance as a quality control team and significantly limits any chance of finding the time or organizational support for Data Governance.
  • The business ultimately stops supporting the need for policies and processes because data quality has improved as a result of the Data Governance team.
  • Over and over you will hear the importance of the Data Quality function and the necessary linkage to the Data Governance program.
  • Data Quality is not only Data Governance in practice, but is the feedback mechanism to know whether Data Governance is effective.
  • Effective Data Governance teams have a role responsible for monitoring and measuring quality.

 

Posted in: Data Management | Data Quality | Data Governance