Ali Baghshomali, former data analyst manager at Bird, hosted a talk with Pear on data and analytics for early stage founders. We wanted to share the key takeaways with you. You can watch the full talk here.
While a lot has been said around building go to market and engineering teams, there’s not much tactical coverage for analytics teams. Yet analytics is one of the most fundamental and crucial functions in a startup as it launches and scales.
When should you start seriously working on analytics? Why should you work on analytics? Who should you hire? What should be in your analytics stack? What are some case studies of company analytics operations? What should you do moving forward?
You should start thinking about your analytics platform when your company is nearing product launch. After your product is live, you’ll receive an influx of data (or at least some data) from customers and prospects, so you want to be prepared with the proper analytics infrastructure and team to make the most of this data to drive business growth.
If you are just starting out and would benefit from working with analytics but don’t have much in house, consider using third party data sources, like census data.
If done well, analytics will pay back many, many times over in time, work, money, and other resources saved as well as powerful insights uncovered that drive meaningful business growth.
In conversation, people often use “data scientist” and “data analyst” interchangeably. While fine for casual conversation, you should clearly understand and convey the difference when writing job postings, doing job interviews, hiring team members, and managing data teams.
Data scientists work with predictive models through leveraging machine learning. Data analysts, in contrast, build dashboards to better display your data, analyze existing data to draw insights (not predictions), and build new tables to better organize existing data.
For example, at Spotify, data scientists build models that recommend which songs you should listen to or add to particular playlists. Data analysts analyze data to answer questions like how many people are using the radio feature? At what frequency?
Similarly, at Netflix, data scientists build models that power the recommendation engine, which shows you a curated dashboard of movies and TV shows you may like as soon as you log in. Data analysts would conduct data analysis to determine how long people spend on the homepage before choosing a show.
Unless your core product is machine learning driven, you should first hire data analysts, not data scientists. In general, a good rule of thumb is to have a 3:1 ratio of data analysts to data scientists (for companies whose products are not machine learning driven).
For early stage startups, stick to the core titles of data scientists and data analysts rather than overly specialized ones like business intelligence engineers because you’ll want someone with more flexibility and who is open and able to do a wider range of work.
Here are examples of tools in each part of the analytics stack and how you should evaluate options:
Helping Hands Community is a COVID inspired initiative that services high risk and food insecure individuals during the pandemic.
Bird is a last mile electric scooter rental service.
Make a data roadmap just like you make business and product roadmaps. Data roadmaps are equally as important and transformative for your startup. List the top 5 questions you foresee having at each important point along this roadmap. Structure your data roadmap in a way that your stack and team addresses each of the questions at the point at which they’re asked.
We hope this article has been helpful in laying the foundations for your analytics function. Ali is available to answer further questions regarding your analytics strategy, and he is providing analytics and data science consulting. You can find and reach him on LinkedIn here.