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. 

The questions above pertain equally to data analysts and data scientists. The primary difference is that data scientists have deep training in statistics and know how to build and tune sophisticated machine learning models, whereas data anaysts do not. Eckerson Group recently defined a data analysts as:  A business-savvy data professional who answers business questions by finding, cleaning, transforming, visualizing, and analyzing data. These include finance analysts, sales analysts, and other functional analysts.

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 analyst network. 

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

1= Agree Entirely, 2= Agree, 3= Disagree, 4= Disagree Entirely

___ 1. Our data analysts spend more time finding and cleaning data than analyzing it.

___ 2. Our data analysts never rotate through departments or build cross-functional knowledge. 

___ 3. Our data analysts lack curiosity or time to explore data beyond actual projects.

___ 4. Our data analysts spend more time maintaining past reports than building new ones. 

___ 5. Our data analysts continually reinvent the wheel and rarely reuse work from other analysts.

___ 6. Our data analysts create more reports than they decommission, creating report sprawl. 

___ 7. Our data analysts can’t keep up with business requests.

___ 8. Our data analysts are order takers who do not probe deeply into business needs. 

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

___ 10. Many of our data analysts are glorified report writers.

___ 11. Our data analysts lack sufficient domain knowledge to be effective. 

___ 12. Our data analysts struggle to communicate findings the business can act on. 

___ 13. Our data analysts are isolated in departments and rarely interact with each other.

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

___ 15. Our data analysts 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 

Data analysts are the lynchpin of any data & analytics strategy. They are the bridge between enterprise data & analytics teams and business units. They are also the biggest consumers of data in the organization and, hence, the leading source of requirements for the enterprise team. As a result, they are a political asset or liability for a data & analytics leader. Happy data analysts often serve as strong advocates for the enterprise data & analytics team, while disgruntled analysts quickly become its biggest detractors. 

Effective data & analytics leaders focus first and foremost on meeting the needs of data analysts throughout the enterprise. If you need help optimizing your network of data analysts, give us a call. We’d be happy to discuss your issues and provide some free advice.