Upskilling Analysts

Do your analysts want to be analysts forever? Maybe not.

A few weeks ago, I got curious about how organizations can intentionally retrain analysts for data science roles.

This post is the result of a few conversations with data leaders who have been there, done that, and my own research on the topic.

Who Are You Calling Analyst, Anyway?

When you hear the word analyst, what kind of role do you imagine?

The actual job duties of someone with a title that contains the world analyst have an astounding breadth.

Analyst capabilities can be divided into three broad areas:

  • business acumen

  • domain expertise

  • technical skills

Analysts use these capabilities to make decisions, help others make decisions, or solve problems for their organization.

The diversity of analyst duties is important to think about when you think about retraining analysts to be data scientists. Data science may not be an end goal for every analyst, and the learning curve will be steeper for some analysts than others.

Considerations for Data Leaders

This post is for leaders who want to increase their organization’s data science capabilities or output by upskilling analysts.

This post is not about how individuals retraining themselves for data science roles, but I think y’all are awesome, too!

To get your program off the ground in the first place, you’ll need to answer some key questions. Each section below addresses a strategic consideration you’ll have to assess as you design and pitch your program.

Why Reskill Analysts?

Analysts are already insiders. They have hard-won understanding of your organization’s data, knowledge of its business processes, and the trust of its decision makers.

The best analysts can listen to a business problem and a list of wants, and instead of just delivering data, solve the heart of the need, delivering a quick and accurate answer.

The business acumen and domain expertise analysts have gives them a running start on solving data problems. The problems that come their way are a good representation of questions that are hard to answer within your organization, which is useful for finding potential data science projects.

Domain expertise allows analysts to communicate concepts in ways that decision makers can understand and relate to. They know what concerns are likely to come up, and will be able to answer them.

Great data scientists often have some or all of these competencies, too. For the data leaders I talked to, leveraging the experience of analysts was an intentional strategy to speed up the scaling up of their data teams.

How Will You Retrain Your Analysts?

In running an upskilling program, you need to identify the skills you are expecting your analyst team to acquire, and provide resources.

Data leaders I spoke to had a variety of strategies, platforms, and resources from formal training to supported self-study.

Best practices:

  • Carefully consider the relevance of any training you assign

  • Ensure a smooth ‘hello world’ experience

  • Great documentation makes learning easier

  • Create space for daily practice

  • Assess progress to identify deficiencies and provide individual coaching

  • Give analysts the chance to practice and reinforce their skills on real projects

Other considerations:

  • Who do the analysts report to while they are retraining?

  • What incentives are in place to encourage learning and reward those who upskill?

  • How will you create safety if an analyst chooses not to continue?

  • How many analysts can you retrain at a time? Will you do multiple waves?

So I Don’t Need Data Scientists?

Nope, you need data scientists, too.

The leaders I talked to hired data scientists, leveraged internal talent, or acted themselves in this capacity — to mentor and work with the upskilling analyst team. If you are not the talent, you’ll need to justify hiring it or borrowing it from another part of your org.

Data scientist mentors to the analysts played important roles including:

  • Mentoring on technical skills unique to data science like:

    • Building and testing data pipelines

    • Feature engineering

    • Model selection and evaluation

  • Helping analysts navigate new platforms and tools

  • Pairing to work on a project

  • Helping connect the dots for concepts or ideas

  • Encouraging and supporting skill development

Support of experienced practitioners is critical for upskilling analysts to reinforce their learning by applying their skills.

But is it “Really” Data Science?

And do you care, if you’re helping the company increase ROI or satisfy customers?

Data science is a discipline that has been highly and widely hyped for years. Business leaders will have preconceived understandings or misunderstandings of data science. These can work for you, if an outstanding business result can be achieved with a technically simple and straightforward solution. It can also work against you, if a business leader expects data science to deliver miracles, and has little patience for projects that take time to impact business results.

With business leaders, clarify expectations early and often. Engage them in decisions about project direction and ensure they understand the nature of data science.

Consider where your org is on the maturity curve and the motivational fit of the talent you assign to each project.

An upskilling analyst may be incredibly motivated by a simple data science solution that wins widespread praise. An analyst that feels pressure to learn too much, too soon (to keep their job) may be burned out by a high profile project, even if they are successful. A trained data scientist may be frustrated that no one is interested in a more complex solve or bored because they aren’t technically challenged.

Can You Manage Your Stakeholders?

Leading technical functions is a balancing act.

It’s especially challenging in mature organizations, and more change management is needed for new-to-your-org capabilities like data science.

You need to deliver wins for the business leaders who will sell the value of your team laterally and upwards in your organization. Data leaders need to invest time into building relationships and trust with these leaders. Calibrate expectations and involve stakeholders in deciding which tradeoffs to make.

At the same time, you need to keep technical debt low, ensure your team is doing quality work, and keep your team engaged.

Leaders who balance these competing priorities of speed and a happy data team will deliver quick wins and sustained successes.

Data leaders focused on getting wins that mattered to stakeholders. Your team’s conclusions need to be trustable and communicated in language that makes sense to your business leaders.

In some cases, a few high profile wins were enough to build the budding org’s reputation. In each case, the data leader could clearly connect the win to a financial measure for the company.

What Will Your Org Look Like 3 Years From Now?

The final consideration is long term. How will you retain the talent you’ve just upskilled? As your org grows, who can step up into management roles? How do you define success for your team?


Samantha Zeitlin and I overran our planned one hour chat because she had so many great insights and nuggets of wisdom for me. You can find Samantha’s writing here, several of her posts address this topic.

My deepest thanks to the other data leaders who were generous with their time and ideas, but who did not choose to be named in this post.

Other Reading on This Topic & Things That I Read Along the Way

  1. Analytics Translators

  2. An Upskilling Program at Living Social

  3. What Makes Analysts Excellent

  4. Data Science is Different Now, from the one and only Vicki Boykis

  5. Lean Data Science

  6. Gartner Case Study