Data Analysts are the Key to Your Success

I’m always surprised when data leaders can’t readily name or identify the data analysts in their organization. Data analysts are the foundation upon which to grow a successful data & analytics program

Depending on how they are managed, data analysts can be a data leader’s biggest proponent or loudest critic. They are also a bridge to the business, serving as the “eyes and ears” of a data leader who needs to keep a pulse on business needs. As such, they are a reservoir of future requirements and can help data leaders enhance a data environment before users abandon it. Finally, since they are embedded in the business, data analysts play a key role in data literacy programs. In short, it pays to cultivate this small, but influential, cadre of data experts. 

What is a Data Analyst? 

Data analysts have many nicknames: “power user”, “Excel jockey”, “Tableau guru”, and “departmental analyst.” Some practitioners refer to them as “business analysts” but many IT departments use that label to refer to business requirements analysts, so I avoid it. Data scientists are a type of data analyst—one with additional training in statistics and machine learning who know how to code analytical models in Python or R. Collectively, data analysts comprise about 8% of employees while data scientists are about 2%, according to Eckerson Group research.

The job of a data analyst is to answer business questions using data.

The job of a data analyst is to answer business questions using data. They are technical resources dedicated to department heads who need quantitative data to help make a range of decisions, from pricing and campaign investments to the cost/benefits of a merger or how to respond to dips in performance. In the past, most used Excel to query and mashup data and create a report. Today, they are more likely to use Tableau, Power BI, or another visualization tool with built-in data preparation and data science capabilities.

Every department has at least one data analyst. Finance, sales, and marketing generally have small teams of them. Most data analysts sit in a department and report to a department head, but increasingly they reside in a corporate center of excellence where they are aligned with a department. Their hybrid role makes data experts who specialize in a business domain. 

What Makes a Good Data Analyst?

Good data analysts are curious by nature and have a strong desire to explore data and unearth interesting patterns, anomalies, and correlations. Most can write basic SQL and understand basic statistics. Many organizations don’t give data analysts enough free time to explore data in a business domain, which undermines their long-term effectiveness. The more comfortable data analysts become with the data and understand its shortcomings and strengths, the quicker and more effectively they can respond to business requests.

The most important job of an analytics leader is to teach data analysts how to communicate effectively with businesspeople.

The best data analysts are strong communicators who are comfortable working with business leaders to understand issues and develop a plan to address them. They also know how to present their analyses in language that business people can understand and act on. Unfortunately, many data analysts are introverted by nature and need to beef up their communications skills. The most important job of an analytics leader is to teach data analysts how to communicate effectively with businesspeople. 

Not All Data Analysts Are Created Equal

The title “analyst” is perhaps the most abused title in the corporate world. Even in the data & analytics realm, there is quite a difference between data analysts. Much of this has to do with the availability of data and self-service tools. Data analysts who work at organizations beginning their data & analytics journey are often glorified report specialists, while those at more mature companies handle more complex forms of analytics. (See figure 1.) 

Figure 1. Data Analyst Focus

Steps to Success

To get the most out of your data analysts, there are a few steps to keep in mind. 

1. Take an Inventory. First, take an inventory of your data analyst community. Since you can’t manage what you don’t know, it’s important to identify everyone in the extended ecosystem who creates data sets or reports for others to consume. At companies with lots of departments, this may take time since many people perform data analyses as part of a bigger job. Although we don’t recommend part-time data analysts, it’s important to account for them in an inventory. 

Eckerson Group recently performed an inventory of the data analyst community at a university and discovered 85 people who engaged in some form of data-related activities. (See figure 2.) When we added up the salaries of these individuals, the total was in the tens of millions of dollars. This is an eye-opener for most executives who have no idea they are spending that much money on data and analytics resources. Needless to say, a visual inventory of a data analyst ecosystem can help sell a data strategy project designed to optimize the use of those expensive resources. 

Figure 2. An Inventory of Individuals with Data-Related Jobs at a University

2. Appoint an Analytics Manager. Whether your data analysts are decentralized or centralized, they need to be managed by an experienced analytics manager who hires, evaluates, and coaches the data analysts. The manager could be the director of a centralized center of excellence or an analyst manager in charge of a team of data analysts within a department or both. If data analysts are embedded in departments, there needs to be a dotted-line arrangement in which the data analysts report to a department head but are managed, evaluated, and coached by a director of analytics.

This coaching is the biggest driver to creating a culture of analytics.

3. Coach the Data Analysts. The data analytics manager needs to teach analysts to work consultatively with the business. Rather than function as order takers, data analysts need to ask probing questions to unearth real business needs. They also need to present results in terms businesspeople can understand and act on. This coaching is the biggest driver to creating a culture of analytics. (See table 1.) 

Table 1. Coaching Data Analysts

Guiding Principles

  • Focus on questions, not answers 

  • Focus on impacts, not insights

  • Be a consultant, not an order taker 

  • Be strategic, not tactical

Questions to Ask 

  • “What are you trying to accomplish?”

  • “What’s it worth to you?”

  • “What actions will you take as a result?”

  • “Is your team ready to implement the actions?”

Presenting Results

  • Put everything on one slide! 

  • State the question or hypothesis

  • Provide the conclusion with supporting data

  • Never describe the analysis steps. 

4. Empower Data Analysts with a Self-Service Workbench 

Data analysts need several tools to become productive. They need a data catalog to search for data, a federated query tool to access it, a data prep tool to clean and combine data, a visualization tool to analyze data, an autoML tool to create statistical models, and a reporting tool to publish personalized reports at scale (i.e., bursting). (See figure 3.) 

Figure 3. Self-Service Analytics Workbench

Until recently, data analysts had to work with separate purpose-built tools and move data or context between them. Today, vendors are converging these capabilities into a single workbench. Larger companies, such as Tableau and Microsoft, integrate disparate tools either their own or others, while startups, such as Promethium and DataClarity offer organically-grown, integrated platforms. 

5. Create Career Paths for Data Analysts 

Data analysts are intelligent and ambitious people: they work best when they can see the future laid out in front of them. Most are in their twenties or thirties and want to see pathways for advancement inside a company with clearly defined rungs of achievement to climb. If the future is murky, they quickly look elsewhere to establish their professional roadmap. 

Data leaders should work with the human resources department to define data analyst levels and career pathways. The good news is that data analysts sit at the nexus of business and technology and can advance their careers in many exciting directions without ever leaving an organization. (See “Career Paths for Data Analysts: Building the Future”) 


Data analysts are the lynchpin of any data & analytics program. Although small in number, they have an outsized impact on the success of a data leader. Data leaders need to know data analysts and their capabilities, coach them on how to interact with businesspeople, provide them proper tools, and give them a career ladder to advance at the company.

Wayne Eckerson

Wayne Eckerson is an internationally recognized thought leader in the business intelligence and analytics field. He is a sought-after consultant and noted speaker who thinks critically, writes clearly and presents...

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