Data analysts occupy the front lines of business readiness by turning business questions into timely decisions using data and analysis. Ensuring that data analysts are productive, happy, and loyal employees is critical to the success of a data and analytics program. One key to creating an effective platoon of data analysts is to establish formal career paths along with companion training, support, and mentoring.

Career Pathways

Data analysts are intelligent and ambitious: 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 analysts sit at the nexus of business, data, and analytics. Their position within an organization makes them uniquely suited to pursue careers in business, information technology (IT), or analytics. Their data and analytics background makes them valuable as business resources, while their business knowledge makes them suitable for managerial posts on the analytics team and select positions on the data team. (See figure 1.) 

Figure 1. Data & Analytics Career Pathways for a Large Company

The figure above shows that data analysts and data scientists sit at the center of a data and analytics program, linking business users in departments with developers and engineers on the corporate data team. Most data analysts (and many data scientists) are managed centrally but report to a business unit head. This hybrid role gives them the luxury of choosing multiple career paths. 

Lateral Pathways. For example, a business-oriented data analyst can move laterally to the left and become a business requirements analyst or product manager, advancing up the business food chain from there. Technology-centric analysts can move laterally right and exercise their technical skills by becoming a business intelligence (BI) developer, perhaps moving up the corporate data ladder from there. 

Analytics Pathways. Most data analysts, however, want to pursue a career in analytics. Here, two pathways exist, one technical and one managerial. Data analysts who like to analyze data may seek training in statistics and machine learning to become data scientists. Those who prefer to manage and motivate people can aspire to run the analytics team, and perhaps, someday the entire data and analytics program. (See appendix for role descriptions.) 

Training, Support, and Mentoring 

A formal set of career paths blessed and documented by human resources is a great first step. But data analysts and their organization don’t reap any value unless the career program is reinforced with sufficient training, support, and mentoring. 

Training. Technical training for data analysts should combine in-house events and self-directed options. Periodically, the director of analytics should bring in outside speakers to give lectures or run workshops on technical topics. In addition, data analysts should select a mix of training courses from internal and external sources that aligns their personal interests with those of the company. 

Analyst Rotations. For data analysts and data scientists, business domain experience is just as important as analytical skills. To foster business knowledge, organizations should rotate data analysts and data scientists through different parts of the business every two to three years. This solidifies their business knowledge, helps them expand their network of contacts, and keeps them engaged with the organization. Rotational programs are also a good way to retain and recruit top talent. 

Support. Data analysts, and especially data scientists, require a lot of mentoring and support. 

The analytics center of excellence can support analysts by holding weekly standup meetings, quarterly retreats, periodic webinars, and lunch & learns where colleagues can share tips, tricks, and successes. 

Mentoring. For career pathways to work, organizations need a mentoring program to cultivate the next generation of business and technical leaders. A data analyst who signs up for mentoring meets one-on-one with a senior manager for a set period of time. The analyst performs a self-assessment of their current abilities and gaps and sets goals for what they want to accomplish during the mentoring period. The manager holds the analyst accountable for making progress while coaching them on techniques to achieve their goals. 

Summary

An organization that establishes career pathways for data analysts and data scientists creates ample benefits for both the analysts and the organization. Formal career paths keep data analysts productive and engaged while raising their business and technical skill levels, laying the foundation for the next generation of analytics leaders. 

Appendix – Select Analyst Roles 

• Level 1 Data Analyst. Here, a data analyst has analytical training but very little business or practical experience. Level 1 data analysts are paired with level 2 data analysts who train the new hire and check their work. Level 1 data analysts handle straightforward data access and analytics tasks and do not lead an analytics initiative or project. A level 1 data analyst could move laterally into the business as a project manager or the data team as a BI developer. More than likely, they will continue on the analytics pathway to becoming a level 2 data analyst.

• Level 2 Data Analyst. At this level, a data analyst has sufficient business, data, and analytics expertise to handle complex analytics tasks and initiatives on their own using various analytic techniques. Level 2 analysts can work solo in a small department or serve as the business point-of-contact for a larger analyst team. Some level 2 data analysts join the business team they serve as product managers, while those who like data exploration and munging might join the data team as an engineer. Others pursue intensive training to advance into a data science role. However, most will climb the analyst ladder into management.

• Analytics Manager. This role runs multiple teams of data analysts. The person spends significant time recruiting, training, and managing analysts and interacting with business unit heads to prioritize analytics initiatives and allocate resources. This person is on a fast track to run the analytics center of excellence.
  

• Data Scientist. This role is staffed by new data scientists with little practical experience or business domain knowledge. These data scientists are teamed with senior data scientists who guide them and check their work. Most aspire to advance into a senior data scientist role although some may move laterally into the business as a business manager. 

• Senior Data Scientist. Senior data scientists have sufficient business, data, and analytics expertise to handle complex advanced analytics initiatives. More importantly, they know how to interact with the business, set up projects, define goals, set expectations, and communicate results in language the business understands. They also know how to select projects that the business will implement and evaluate the ROI of their results. They are primed to advance into the role of analytics director if they want to move into a management role.