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?
Congratulations, you are launching an analytics/data function! Maybe you are the first analytics hire at a brand new company, or perhaps you are bringing analytics to a mature company that historically lacked a comprehensive data strategy. You sold the vision and also put together a roadmap based on your experience and current best practices. What could go wrong (and who could argue that refactoring from a star to snowflake schema is an important company-wide goal)?
I have often found that it is difficult to explain to business stakeholders what exactly a data analyst on my analytics team does. And even beyond that, I have sometimes found it difficult to explain to the data analysts themselves why their job is so valuable to the business and what their future career opportunities might look like. Data analysts at early stage, tech-adjacent start ups have unique jobs that allow you to work on a bunch of different things which then opens up a ton of opportunities for career progression, and in this post I will talk about all of the different roles data analysts play and what that means for future opportunities.
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.
For the last year or so I’ve been working on building a software application to help marketers allocate their marketing spend. This software is statistics and data-science powered and my partner and I have spent more hours than I’d like to admit struggling to squash bugs, achieve model convergence, and generally answer the question “why on earth could that be happening?” In this post I’ll discuss the history of the lab book and how it’s used generally before discussing how to use it for data science and software engineering projects and providing an example lab book template.
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.
While efforts to build a data dictionary are often undertaken out of a zeal for documentation that we would normally applaud, in practice data dictionaries and data catalogs end up being a large maintenance burden for little actual value, and tend to very quickly become out of date. Instead of investing in building out traditional data dictionaries, we recommend a few different approaches for achieving the same goals in ways that are less burdensome to maintain and better serve the original objectives as well.
Key Performance Indicators (KPIs) are management tools for monitoring and improving business processes. KPIs are helpful in understanding if you’re hitting your business objectives, improving over time, and helping to forecast future growth. They are also a symbol for folks in the organization to rally around and anchor against, providing clarity and aligning cross departmental objectives.