Make the Most of Your Data Analysts Part 1: What is a Data Analyst and Why Should You Care?
Data analysts have many nicknames: “power user”, “Excel jockey”, “Tableau guru”, and “departmental analyst.” Some practitioners refer to them as “business analysts” but many organizations use that label to refer to business requirements analysts, so I avoid it. Data scientists are a type of data analyst—with formal training in statistics and machine learning and the ability to write Python or R code. Collectively, data analysts comprise about 8% of employees while data scientists are about 2%, according to our research.
The job of a data analyst is to answer business questions using data. They are data savvy business resources dedicated to a department head who needs quantitative data to 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 data analysts 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.
The job of a data analyst is to answer business questions using data
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 them 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 more quickly and effectively they can respond to business requests.
The best data analysts are strong communicators who are comfortable working with business leaders to understand issues and develop plans to address them. They also know how to present their analyses in language that businesspeople can understand and act on. Unfortunately, many data analysts are introverted by nature and lack adequate communications skills. The most important job of an analytics leader is to teach data analysts how to communicate effectively with businesspeople.
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 can mashup data and create analytic models.
Steps to Success
Here are a few tips to get the most out of your data analysts.
1. 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 their primary 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 1-2.) 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 clue the amount of money they are spending on data and analytics resources. A visual inventory of a data analyst ecosystem can help sell a data strategy project designed to optimize the use of those expensive resources.
2. Appoint an Analyst 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 one or more subordinates who manage the organization’s data analysts, whether centralized or distributed, 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 the Analyst Manager.
This coaching is the biggest driver to creating a culture of analytics.
3. Coach the Data Analysts. The Analyst Manager needs to teach analysts to work consultatively with the business, and not function as order takers. Data analysts need to learn to ask probing questions to unearth real business needs. They also need to present results in terms business users can understand and act on. This coaching is the biggest driver to creating a culture of analytics.
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 how to interact with businesspeople, provide them proper tools, and give them a career ladder to advance at the company.