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and data catalogs end up being a large maintenance burden for little actual value, and tend to very quickly become out of date.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.
Hi Everyone! It is hard to believe that Locally Optimistic started a year ago today. In the last year we evolved from a blog (29 posts!) to a thriving slack community (515 members!).
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.