[A version of this post appears on the O’Reilly Data blog and Forbes.]
I talk with many new companies who build tools for business analysts and other non-technical users. These new tools streamline and simplify important data tasks including interactive analysis (e.g., pivot tables and cohort analysis), interactive visual analysis (as popularized by Tableau and Qlikview), and more recently data preparation. Some of the newer tools scale to large data sets, while others explicitly target small to medium-sized data.
As I noted in a recent post, companies are beginning to build data analysis tools1 that target non-experts. Companies are betting that as business users start interacting with data, they will want to tackle some problems that require advanced analytics. With business analysts far outnumbering data scientists, it makes sense to offload some problems to non-experts2.
Moreover data seems to support the notion that business users are interested in more complex problems. I recently looked at data3 from 11 large Meetups (in NYC and the SF Bay Area) that target business analysts and business intelligence users. Altogether these Meetups had close to 5,000 active4 members. As you can see in the chart below, business users are interested in topics like machine learning (1 in 5), predictive analytics (1 in 4), and data mining (1 in 4):
We’ve had similar indications from recent surveys of people who attend Strata. Thus the democratization of analytics is something we wanted to add to the Strata Santa Clara program. To that end, we have two excellent tutorials that will introduce non-experts to advanced analytics. John Foreman (Chief Data Scientist at MailChimp) will use spreadsheets to give a detailed step-by-step tour of some important techniques and algorithms. Leland Wilkinson5 (VP of Visualization at Skytree), will lead a tutorial on an interesting expert system that opens up machine-learning and statistics to business users. These back-to-back tutorials from two respected teachers and practitioners, provide business users a solid introduction to analytics. If you’re a non-programmer looking to add machine-learning to your arsenal of tools, these tutorials are the place to start.
- From Data Scientists to Marketers: Making Machine Learning Accessible & Usable
- Data Transformation: Skills of the Agile Data Wrangler
- Data analysis tools target non-experts
(1) Since my post last August, I’ve come across several other tools and projects. Come to the Startup Showcase at Strata to meet some of them.
(2) Analytics experts should still be available for consultation and advice.
(3) Data is from the Meetup API (“A sampling of 50 topics each member has subscribed to”). As you can imagine, members of the Meetups I analyzed were also very interested in “business topics” like “Startup Ventures” and “Entrepreneurship”. I included only “technical topics” in the chart above.
(4) I came up with the following arbitrary definition of “active”: An active member is someone who has visited the Meetup’s homepage at least once over the last six months.
(5) Leland and Alex Gray are holding an office hour at Strata on Wednesday, February 12th.