5 Tips to Hire the Right Data Governance Leader

September 28, 2016 Mackenzie Knapp

two women on either side of a desk talk, one woman holds a piece of paper

As digital analytics continues to be incorporated into the various facets of business, various roles within an organization get to dip their feet in the Big Data waters.

Understanding which skill sets and types of personalities best fulfill emerging corporate leadership positions needed to manage the ever-increasing digital pressures is critical to the success of any data management or data governance program a company hopes to adopt.

As one Forrester Research report from February of this year suggests:

“The need to keep up with rising customer expectations in the digital age will continue to drive major organizational changes within companies. The increasing occurrence of titles like chief customer officer, chief data officer and chief digital officer is an admission that firms need a higher level of cross-business unit coordination to provide compelling customer experiences.” (Brief: New Corporate Leadership Functions Will Address Rising Digital Pressures, Forrester Research 2016.)

But what characteristics should organizations be looking for when choosing a data governance leader? What hiring pitfalls for this position should be avoided?

Here are five tips to help get your data governance leadership position filled with the right person.

1. Stop Looking for Unicorns

Because effective digital analytics implementations are the product of a broad range of skill sets, some companies are now looking for analytics “unicorns”—individuals with advanced technical knowledge, prolific management skills, and superior analytical capacity—to fill their management positions.

Though these skill sets may not be mutually exclusive, the idea that a sole individual should be hired as an expert in each capacity is not only misguided, it’s dangerous to your data governance program.

Real success in a data governance effort comes with specialized skill sets distributed among teams and leadership that can coordinate these collaborative efforts—not leadership roles that are sabotaged with unrealistic expectations of having more subject matter expertise about all the data in the company than is possible to have.

Such expectations undermine a data governance leader’s likelihood to succeed with sustainable and scalable data governance initiatives.

In short, managers need to manage, developers need to develop and analysts need to analyze.

2. Choose a Tech-Proficient Data Manager Over a Tech-Genius

I was once extended an offer to work as an analytics manager, and the company that extended the offer listed extensive resume requirements for the position. The company wanted a manager that:

  • Was extremely well-versed in HTML5, PHP and JavaScript
  • Had a firm understanding of the software development lifecycle
  • Was a data guru in analytics, visualization, mining and architecture
  • Was an expert in several web analytics technologies and their implementation
  • Had at least five years of experience in management and exceptional leadership skills

While being tech-savvy is absolutely requisite to any job in the digital analytics space, I couldn’t help but think, “Does a manager need to be a tech-genius to effectively manage?”

Jim Sterne, seasoned thought leader and Chairman of the Digital Analytics Association, doesn’t seem to think so.

In a recent conversation between Jim and myself he shared this analogy with me:

“A hospital won’t thrust their best surgeon into an administrative position. That surgeon needs to be using his or her technical skill where it is most applicable and let someone else with management skills do the managing. The same thing goes in the field of digital analytics: techies focus on tech, and managers manage.”


@jimsterne “Techies focus on tech, and managers manage.” #DataGovernance
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I can corroborate Jim’s wisdom. As I have worked as a consultant for multinational enterprises concerning their data governance infrastructure, I have noticed that the organizations that are most successful in keeping their analytics on track don’t expect their project managers to be their best tech minds or their tech minds to be management savvy.

3. Don’t Rule Out High-Quality Data Governance Candidates

Companies drastically narrow their pool of valuable applicants when they adopt a “unicorns-only” policy.

These companies rule out candidates that could perform exceptionally as managers of technical minds despite not being expert techies themselves.

Some industry research even suggests that data stewardship positions born out of technology management experience are set up to fail. Consider this Forrester research released just this month:

“An underlying challenge of data stewardship in many organizations is that stewards are still born out of the technology management organization. But data stewards cannot be an extension of the data management organization — data management is just the enabler of data governance and data stewardship operations. Analysis of job descriptions and profiles of data stewards seen on LinkedIn and job boards shows that data stewards share more qualities, skills, and experience with data management pros than with business stakeholders (see Figure 2). This has a direct impact on the ability of data stewards to develop the right goals, policies, procedures, and metrics to get the most business value from data (see endnote 4). They see the world through a technology management lens rather than a business lens” (Data Stewards Are Set Up To Fail, Forrester Research, September 2016).

If you have candidates that are really good at managing, don’t discount them because they don’t speak 3+ programming languages.

4. Encourage Specialization Within Your Organization

Besides, if you have someone that is really good at coding, do you really want that person to be spending time staffing, delegating out assignments and performing other administrative functions?

The concept of specialization has transformed nearly every industry and business model since it first appeared on Henry Ford’s assembly line.

To make your data governance initiatives successful means hard work, not just from the C-level leader, but from all layers in the organization.

5. Foster Collaborative Diversity

If you hire data governance managers that have the same technical background as the people they manage, then you may miss out on a lot of collaborative creativity, innovation and vision.

If each member of a team is looking at a project from the same angle, then your business processes and data insights risk being painfully two-dimensional. But when each party can offer an objective view of an issue from a unique perspective, there are more angles to approach that issue and more potential for innovation.

This is especially important in the age of the customer, where user experience is essential for creating conversions.  If all members of a team think similarly, then the user experience may be shallow and unfulfilling.

Because Big Data will affect every role within a business—particularly digital marketers, developers, analysts, and C-level decision-makers—it’s important to diversify the ownership of data and how it is collected, analyzed and applied.

Reconsider Your Hiring Expectations for Data Governance Positions

Data governance managers and leaders do need to have some proficiency in the area they are managing, but ultimately they should rely on the technical skill of their team to accomplish their digital analytics goals.

There’s no such thing as a one-person team, and expecting a data governance leader to be an expert in all fields is not only unrealistic, but dangerous. In fact, searching for tech-geniuses to be your managers may be detrimental to your process of hiring, internally or externally, because you may end up:

  • Ruling out high-quality candidates
  • Destroying specialization within your organization
  • Diminishing collaborative diversity

Anthony Stagg, Manager of Digital Analytics at iProspect, said in a recent blog post:

“We expect an architect to draw up architectural plans and know something about electrical work, plumbing, cabinet making, dry walling, and building codes and everything else to do with building a house, but we don’t expect him/her to actually do the installation work.”

Want to learn more about how to build a great data governance team in your organization? Don’t miss the welcome keynote at the 2016 Analytics Summit.

This virtual event is free, and Forrester Analyst Rusty Warner and Adobe Digital Marketing Consultant Susan Vertrees will be discussing Data Governance Centers of Excellence in Any Organization. Register to attend today.

 

About the Author

Mackenzie Knapp

As the most tenured consultant for ObservePoint's Professional Services department, Mackenzie Rae Knapp consults over 17 multinational clients on their implementation of digital marketing and data governance. Her intellectual curiosity to find meaning and trends where there appeared to be none was heavily applied to financial analytics at startups and boutique firms and later expanded to Fortune 500 behemoths such as BlackRock & Cisco Systems. Mackenzie's innovative approach helped to collaboratively bring about several new product offerings such as Release Validation and Implementation Validation. She now helps clients polish or pioneer their data quality processes through SDR creation and automation and uses ObservePoint-based data to build historical reporting dashboards on web analytics KPIs.

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