“Our product is for business users.”
You hear this all the time from software vendors. Who is this business user?
Well, he’s probably a sibling of the equally ubiquitous “non-technical user.” Oh, and they are both poor cousins to the all powerful, possibly none-too-bright, most likely impatient and lazy, “decision maker.”
I think it’s time to move way beyond these oversimplified personas. They relate to a view of business and work that is increasingly out of date.
In the area of data and analytics, there has been a historical divide between platforms and tools that are used by software developers, data engineers and data scientists to wrangle raw data into results, and the reporting and business intelligence tools that are meant for these so-called “business users” to passively consume them.
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The much hyped trend toward self-service BI doesn’t really change any of this. Self-service BI tools still rely on extensive ETL to massage data into a form that us poor dumb lazy business users are believed to be capable of interacting with. The questions we get to ask are severely circumscribed by the decisions made by our “more technical” (and implicitly wiser) data overlords.
Today’s businesses are being transformed by technology based services where person-to-person interactions have been replaced by person-service and thing-service interactions. They are thus filled with a new class of knowledge workers who spend their time figuring out how these services should operate. They divide into product managers, marketers, growth hackers, brand managers, hardware and software engineers, QA engineers, support staff and sales people. The executives that lead them increasingly come from their ranks and earn and keep their right to lead by staying connected to the nuts and bolts of their organization’s service offerings.
All of these people make choices every day that are informed by hypotheses about what service features have what impacts. In most organizations that I have spoken with in the last 15+ years they form up to 50% of the employee base. They have to have deep knowledge about the way their services operate and how people and things interact with them. (We call that “behavioral analytics.”)
Are these users non-technical? Are these users an undifferentiated mass of “business users?” Will one “magic metric” tell them exactly what to do without need for further exploration or critical thought? Can the relatively small number of data engineers in their organizations anticipate every question they may have? I think not.
In interacting with these users I have encountered a huge range of educational and career backgrounds. Many are Excel wizards. Many were trained as software or mechanical engineers. Many have science backgrounds. There are lots of accountants and economists. A few are even trained in creative fields. I’m one of these people, and the majority of people I have hired in my career fit this profile as well. (Personally my educational background is business/economics and art and with 20+ years in infrastructure and enterprise software in roles ranging from accounting and inventory planning through sales engineering and product management, I can find my way through some hairy functions just fine, thank you. I didn’t lose those skills when I got an exec title.)
What we have in common is an ability to reason logically, a desire to test hypotheses with data, and a preference for exploring data interactively through a GUI over writing code or query syntax. Many of us are reflexively mistrustful of summary reports if we can’t get a thorough understanding of the raw data and logic behind them. If fed a summary report, the good ones amongst us drill the people who prepared it to understand every aspect of what went into it to be sure that we know it’s real meaning and reliability.
Giving us shallow software is just lazy software engineering. It’s coding with contempt.
I’m at Interana because it is about “interactive analytics” – analytics that let people throughout every modern organization explore their own data intuitively and interactively. That takes more speed, scale and flexibility than older ways of delivering static reports or limited dimensions to “business users.”
I spent a day last week with an Interana customer where 100 users had had access to a previous system and only 3 ever ran ad hoc queries – the rest just glanced at the static dashboards. Within 6 months of implementing Interana, 200 users actively explore their data on an ad hoc basis every day. These users range from product management to marketing and QA to software engineering. Long term they expect 600 of their 1200 employees to be active ad hoc users. That is the shape of the enterprise of the future.
Would I be doing justice to Interana if as a product leader here I captured all of these people as a single persona of the “business user?”