Make the Most of Your Data Analysts Part 2: How Effective Are Your Data Analysts?

It’s hard to quantify the value of data analysts, but this quiz will help you identify obstacles to success.

Most executives can’t tell you how many data or business analysts they have or how much money they spend annually on their salaries. If they knew, they might be astonished. Most companies have hundreds of data analysts, most embedded in departments, working full- or part-time on data analysis activities. An organization’s total investment in data analysts often exceeds $100 million annually.

Once they recover from their initial shock, executives ask what the data analysts are doing and whether their output is worth the company’s unexpected investment. This puts data & analytics leaders in the uncomfortable position of having to justify these resources, most of whom they do not own and only indirectly manage or influence, if at all.

Quantifying the Value of Data Analysts

It’s hard to put a dollar figure on the value of data analysts. It’s much easier to intuit their worth with a few incisive questions. Are they working productively? How are they perceived by the business? How well do they answer business questions? Do their insights lead to actions that generate tangible business value? Are the business units they support achieving their goals? Are their units becoming more data literate and data-driven?

Today, it’s hard to find data analysts who have strong analytical skills, deep domain knowledge, and know how to communicate effectively with businesspeople. It’s rarer still that their organizations have invested sufficiently in data infrastructure, data standards, and analytical processes to maximize their productivity and effectiveness.

Data Analyst Effectiveness Quiz

In the absence of an ROI calculator for data analysts, Eckerson Group has created the following quiz to help you estimate the value of your data analysts. Once you complete the quiz, total your scores and use our rating chart below to determine the effectiveness or value of your data analysts.

Evaluate each statement below and assign it a number based on the following scale:

1= Agree Entirely, 2= Agree, 3= Disagree, 4= Disagree Entirely [WE NEED TO JAZZ UP THIS QUIZ!]

Our data analysts….

___ 1. Spend more time finding and cleaning data than analyzing it.

___ 2. Never rotate through departments or build cross-functional knowledge.

___ 3. Lack curiosity or time to explore data beyond actual projects.

___ 4. Spend more time maintaining past reports than building new ones.

___ 5. Continually reinvent the wheel and rarely reuse work from other analysts.

___ 6. Create more reports than they decommission, creating report sprawl.

___ 7. Can’t keep up with requests from the business.

___ 8. Are order takers who do not probe deeply into business needs.

___ 9. Build lots of disconnected dashboards, creating a fragmented view of data for business users.

___ 10. Are glorified report writers.

___ 11. Lack sufficient domain knowledge to be effective.

___ 12. Struggle to communicate findings the business can act on.

___ 13. Are isolated in departments and rarely interact with each other.

___ 14. Don’t apply the right type of analysis (e.g., descriptive, diagnostic, predictive) to business questions.

___ 15. Don’t contribute to improving the data literacy of their departmental peers.

___ TOTAL

What Your Score Means

If you scored 45-60, then you have a world-class data analyst ecosystem; if you scored 30-45, your data analyst network is healthy and strong; a score between 15 and 30 means you have a lot of work to do to make good on your company’s investments in data analysts; and if you score less than 15, your data analyst community is dysfunctional or non-existent. Give us a call!

Summary Scores:

45-60: World-class data analyst network

30-45: Effective data analyst network

15-30: Ineffective data analyst network

  0-15: Dysfunctional or non-existent data analyst network

Conclusion: Focus on Data Analysts

Effective data leaders focus first on meeting the needs of data analysts throughout the enterprise. They know data analysts are the lynchpin to their data strategy and a key bridge to the business. They can be either a political asset or liability. Happy data analysts often serve as strong advocates for the enterprise data team, while disgruntled ones are its biggest detractors. It pays data leaders to find people serving as data analysts in their organization and nurture them.