- September
Posted By : Jerry DiMaso
Collaborative Data Discovery: the Art of Datastorming

Personally, I think the word “datastorm” is pretty weak as far as creativity goes, and it doesn’t do a great job of describing what it means on its own, which is a word’s singular purpose. However, when you say you’re going to do a datastorming session, at least to anyone familiar with the concept of brainstorming, they seem to understand what you mean. Context clues.

Sadly, there is not a ton of information out there on datastorming because it’s a fairly difficult thing to do with data of any complexity due to technology constraints. Consider a brainstorming session: you’re in a room with some colleagues trying to figure out what your company softball team mascot should be. Theresa is up on the whiteboard drawing out a matrix with ferocious animals down the side and the corporate color palette across the top. Bill is handing out pastel post-it notes and crayons, and Tammy is firing up a team MURAL (btw, if you haven’t used MURAL during a brainstorming session, you definitely should because it is amazing).

A team, workshopping as generically as possible

This environment is great for generating ideas, capturing parking lot items, and most importantly, ensuring everyone is looking at the same content. Collaboration tools like MURAL and Figma do a fantastic job at digitizing the team workshop experience, but unfortunately, modern analytics tools really don’t. However, as remote working becomes more prevalent, demand for faster and better analytics rise, and our ways of working evolve to incorporate a heavier blend of synchronous versus asynchronous work streams, tools that enable Collaborative Analytics are going to be critical for analysts in every industry.

Solving a complex problem takes creativity; even rockstar analysts who’ve had Excel functions named after them (usually) can’t run a couple of their secret algorithms on some sales data and tell you why Beanie Baby™ sales dropped by 24% yesterday. They have to leverage multiple data sources, test hundreds of hypotheses, pull in more data, remove data, twist, turn, model, remodel, visualize, tablularize, revisualize, and on and on…

With today’s tools, this is happening in a silo, with one analyst looking at one set of data, with one set of tables and visualizations. This analyst may invite a colleague to take a look at her progress over her shoulder or send out an email with a screenshot or two, and maybe her colleague points something useful out in response, but this is about the extent of collaborative capabilities in the data visualization tools today.

This is one of the many reasons we created Knarr — we were constantly using collaboration tools to get things done quickly with our remote teams, but when it came to data and analytics, a lot of the collaboration either had to be in-person or shared through email links. We wondered why there wasn’t a tool that enabled us to connect to data and invite team members to build out analytics together in the same way we were used to with other collaboration tools. We still don’t know why there wasn’t, but now there is!

Datastorming, or whatever it is called in the future (maybe we’ll coin the phrase, Knarring? No…), is inevitably going to be required for data and analytics tools in the future. And the main point of this article is that, while it is most definitely an art, you need a technology that is built from the ground up with collaboration in mind to make it work. Any analytics technology that meets that singular criterion will do.

Follow us on Twitter or LinkedIn to learn more about our philosophy on collaboration in analytics and check our our site at https://knarr.io to sign up for our Beta, launching in a couple weeks!


Jerry is the CEO and Co-founder of Knarr Analytics with a background in Business Intelligence and Analytics consulting. His work in more than 100 organizations across various industries has inspired him to take on the mission of enabling analysts to solve business problems faster and more collaboratively.

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