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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThere is no single route into analytics leadership. A 2017 study of 38 executives identified three observed patterns: advancing within analytics, moving between analytics and other functions, and entering analytics leadership from outside the field. These are career paths people took—not a universal ladder or a guarantee of promotion.
Three routes into analytics leadership
The labels below come from a historical sample, while current role guidance helps explain what aspiring leaders may need to develop. In practice, careers can combine elements of more than one route.
| Route | Typical starting point | Experience emphasized | Transition to demonstrate |
|---|---|---|---|
| Linear | Already working in analytics | Increasing analytical depth and responsibility | Ability to lead broader analytical work and teams |
| Nonlinear | Analytics plus a business function such as IT, marketing, or accounting | Cross-functional context and problem-solving | Ability to connect analysis to decisions across functions |
| Parachute | Outside analytics, often in an adjacent technical or organizational field | Transferable domain knowledge and leadership | Ability to build analytical understanding while leading multidisciplinary teams |
1. Linear: grow within analytics
The linear path means moving upward within the analytics function. Drexel LeBow’s examples include progression from statistics or machine learning into analytics, sometimes within one organization. The broader idea is to build analytical expertise and gradually take on wider scope—from producing analysis to guiding analytical work and leading people.
In Drexel’s 2017 sample, 10 of the 38 executives followed this route. Half of that group had a master’s degree in computer science, statistics, or analytics. The article also noted strengths such as customer insights, data warehousing, and machine learning. Those figures describe this small historical group; they do not establish a graduate degree, a particular specialty, or staying with one employer as a requirement.
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2. Nonlinear: combine analytics with functional experience
The nonlinear route involves movement within and between analytics and other functions, including IT, marketing, and accounting. Of the 38 executives in Drexel’s sample, 17 followed this pattern. The article described strengths in cross-functional problem-solving, enterprise architecture, IT strategy, digital marketing, and demand generation. It reported that 38% of this group held MBAs—a cohort detail, not evidence that an MBA is necessary.
Functional experience can complement analytical skills: familiarity with business priorities and stakeholder needs can help a leader decide which questions matter and how to put findings to use. That is a practical implication of the cross-functional route, not a separate conclusion proven by the study.
3. Parachute: enter from outside analytics
Drexel used “parachute” for executives who entered analytics leadership without prior analytics experience. Eleven of the 38 executives in its sample had followed this path; their earlier work included engineering and IT. The article identified cloud computing, project management, mobile systems, telecommunications, and security among this group’s strengths.
This route may suit experienced people in adjacent technical or organizational roles who can connect their domain knowledge to analytics strategy. It is not a shortcut around understanding analytics. The UK Government Analysis Function says that more senior leadership requires broader analytical understanding and the ability to lead multidisciplinary teams.
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What the evidence can—and cannot—tell you
Drexel LeBow’s April 5, 2017 article by Murugan Anandarajan and Diana Jones examined 38 executives featured in the Boardroom Insiders database who had been appointed to analytics leadership in the preceding two years. The authors traced earlier job changes and grouped career histories into three categories. The resulting counts—10 linear, 17 nonlinear, and 11 parachute—describe that sample.
The study is small and historically bounded. It is not a representative survey of analytics leaders, a forecast of current hiring, or causal evidence that any one route produces success. Its categories are useful as possibilities to consider, not as a ranking of paths.
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Build skills and experience for the next step
The UK Government Analysis Function’s data analyst profile lists capabilities that can inform a development plan: analysis and problem-solving, data visualization, programming and reproducibility, data management, ethics and privacy, quality assurance, statistical methods, communicating insight, and project management. It describes skills as growing with seniority and says expectations vary by department and context. The framework represents around 17,000 analysts across government; that stated scope is not a measure of the wider labor market.
Microsoft Learn’s data analyst training page describes work that includes visualizing and reporting data, profiling, cleaning and transforming it, building data models, understanding stakeholder requirements, and turning raw data into useful insights. It offers self-paced and instructor-led learning routes. This is a vendor’s role description and learning resource, not independent evidence about how leadership hiring works.
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Turn the route into a practical development plan
- Strengthen the skills your current role uses. Identify gaps in analysis, data quality, visualization, programming, privacy, or communication that matter for the work you want to lead.
- Seek broader assignments. Work with another function or take on a problem that requires understanding both the data and the decision it should inform.
- Show how your work informs decisions. Explain the evidence, relevant caveats, and value created in language suited to different audiences.
- Take responsibility for delivery and people. Look for opportunities to lead a project, coordinate specialists, or manage work across disciplines—not only to produce an analysis.
- Choose development that addresses a real gap. Formal courses can be one option alongside learning through work and other people. The Government Analysis Function’s career framework suggests a 70% on-the-job, 20% learning-through-others, and 10% formal-learning model; treat this as that framework’s development model, not a universal finding or fixed formula.
These steps synthesize the skills and experience described in the role guidance and the historical executive sample. They are practical ways to prepare, not guaranteed promotion criteria.
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