Make the Most of Your Data Analysts Part 4: Career Paths for Data Analysts
Establishing career paths for data analysts is a great way to improve their skills, keep them engaged, and develop the next generation of leaders.
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.
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.
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 project manager 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 or data engineer, 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.
Training, Support, and Mentoring
A formal set of career paths blessed and documented by human resources is a great first step to optimizing a data analyst network. 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. The analyst performs a self-assessment of their current abilities 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.
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.