Your data team has to produce solid data. The pipelines have to run, the logic in your transformations has to be sound, and the report has to show accurate revenue. Those fundamentals are hard to argue with. But if that’s all you’re doing, your team is probably bored and your organization definitely isn’t getting as much value as it could out of its data. Open-ended creative work is a huge part of the appeal of working in this field - identifying opportunities to improve processes, appeal to new customers, or build better products adds value for the organization, but it is also just incredibly personally satisfying. One of the fundamental challenges of managing a data team is balancing the need for rigor and reliability with the team’s desire to spend most of their time creating new knowledge. How do we manage those sometimes conflicting priorities?

Providing clarity and structure around expectations empowers your team members to decide what skills and competencies to focus on improving. However, writing the career ladder is only the beginning of using it well - to get the most value, you have to effectively communicate it to your team and use it as a jumping off point for specific, growth-oriented development conversations. This is part 3 of a series on career ladders - you may want to read part 1, why a career ladder is so important, and part 2, how to create a career ladder before proceeding.

After last week, I hope you’re on board that a career ladder is important, so let’s jump right into creating one that works for your team. This post will walk you through the two key parts of a good career ladder - guiding principles and specific competencies - and will point you toward some examples of ladders others’ have created for inspiration. This is part 2 of a series on career ladders - you may want to read part 1, why a career ladder is so important, before proceeding and follow it up with part 3, how to use a career ladder once you’ve got it.

Happy New Year! In the blank slate of January, many of us are thinking about what’s next. Maybe you need a road map for the projects your team will tackle this year, or maybe you need a road map for yourself. What should you focus on this year to get yourself to the next level? How do you help your team do the same? A career ladder is one effective tool to help answer those questions - and on top of that, a good ladder can help mitigate bias and eliminate glue work.

If you’re lucky, you know what it will take to get your next promotion. Maybe you need to work on communicating your analyses at the C-level instead of primarily to functional managers. Maybe you need to finish a complex project like a predictive churn model. But there is lot of work in every analytics role that just has to be done, even if it doesn’t seem to help you get from where you are to where you want to be.

The fastest way to doom an Analytics team (and any hope of building a data-driven organization) is to present data and analyses that are often flawed or inconsistent. When people don’t believe they can trust the data, they will stop using them (and, if you are an analytics leader, you might be soon looking for a new job).

People often ask for advice about building out an analytics organization – How to structure the team? What skills to hire for? Do we need engineers? What about data scientists? How big should the team be? Unfortunately, there is no easy answer to these questions, because the best analytics team is the one that best supports the organization and its specific needs. To make things even more complicated, A) different organizations have very different needs and B) your organization’s needs today will be very different from its needs in the future. In this post I will discuss some of the different dimensions that are import to evaluate when thinking about how to structure an Analytics team.