A Culture of Partnership During my time leading an analytics and data science team, I spent a lot of time thinking about how an ideal analytics team should operate – how the team should work together, how the team should prioritize their work, and how the team can most effectively partner with the broader organization to generate business value. I believe that for an analytics team to be effective, the team must develop a strong culture of partnership in order to actually drive business value.
The landscape of the data and analytics world is shifting rapidly. In many companies, the roles and responsibilities of data engineers, analysts, and data scientists are changing. This change has created the need for a new role on the data team which some have taken to calling the “analytics engineer”.
As the new year rolls around, many Data leaders are thinking about (or have already created) 2019 road maps for their team and function. Since Data often works cross functionally with other teams, it’s key that you consider other team’s priorities and objectives in developing your road map. Below is a blueprint you can use to get started.
The notion of an A/B test is premised on the fundamentally flawed assumption that there exists one version of some treatment that is better on average for all users. Analytics practitioners should reject the assumptions of homogeneity and start designing systems that allow for (and encourage) non-binary outcomes of tests.
The first data hires at an early stage startup face numerous challenges — an infrastructure built to run the business but not analyze it, an organization hungry for information without a process for requesting and prioritizing it, and little documentation on how anything is done. What should they do first?
I often get asked by junior data professionals how they can improve as data scientists. Today I will outline a generic framework for thinking about learning and provide a few concrete examples in support of it. These are tools that I still employ in my day to day learning and growing as a data professional.
I have spoken to many fellow analytics practitioners who are adament that they want their team to never touch “production.” While there are good reasons to be careful whenever you make changes that could impact customers, I believe that as software becomes more data-driven it is critical to find safe ways to empower Analytics teams to build and deploy data-driven applications.
Often, Data and Analytics teams go under-utilized in their organization because they can not collaborate effectively with the broader Technology and Software Engineering teams. By designing software following the “code as configuration” pattern, software engineers can enable and empower Analytics teams to work independently: both taking advantage of their technical skills and removing drudge-work responsibility from the Software Engineering team — a win-win.
A hypothetical tech company just completed an A/B test of two experiences, A (the test) and B (the control). The test was set up properly and executed successfully. The following dialogue is taking place between Diane the Data Scientist and Marty the Marketing Analyst at the conclusion of the test.
Everyone has their own reaction when discovering wrong data. It might start with a double take or maybe an itching feeling that the number should be a higher. However it starts, it usually leads to an investigation to discover what went wrong. While this is a very normal reaction, I offer an alternative. Before turning over every stone in your ETL, ask a few questions to discover if your “wrong” data really is wrong. In this post I explore what wrong means when it comes to data (spoiler alert: it is not black and white). I also offer a few tricks to diagnose which of the buckets of wrong your problem falls into. Yes, this approach may add an extra step or two in your process, but it can also save a day of work trying to fix something that isn’t even broken.