Published on Techcrunch.com
We’ve reached a point technologically where consumers are no longer impressed by access to data, as data availability, even our own, is increasingly the norm. Instead, the usability of that data is what’s driving demand for new products and services. Even the term “Big Data” is slowly being replaced by “Business Intelligence” as data is increasingly becoming commoditized.
This is where Design Thinking becomes so powerful. What insights can we extract and how do we present those insights to the user? The difficulty with that question is that it requires restraint and focus.
IDEO’s President and CEO described that practice best, stating, “Design thinking is a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.”
What are the requirements for business success and how does data help to enable that success? Most big data startups are simply interpreting and presenting data to end users. However, the ability to do that effectively is something that many startups struggle to achieve.
Luckily, as part of my job I’m afforded the opportunity to interview hundreds of incredible startups per year; many in the “Big Data” space. In doing so, it becomes apparent which practices work best and which are still struggling to answer the following questions.
Who is The User?
One of the questions I always ask is “Who within the org would use this product?” Is this coming out of the marketing department budget or the sales department budget? There may be scenarios where multiple departments could utilize this tool… This is usually a trap. Building for everyone’s needs dilutes the impact of your tool and will muddle the insights you are trying to present.
Getting this question right will affect how you sell the product and more importantly how you design the product. This is one of the reasons why EMR software is so painful; they try to sell across specialty even though the clinical process for a general practitioner is far different than an ophthalmologist or neurologist. Yet many of the largest players have designed their products to serve everyone and as a result, most doctors had to be bribed with federal funding and penalties in order to make the switch.
One of our portfolio companies, Askuity, a fast-growing start-up in the retail analyticsmarket, is a great example of a company that built their product from the ground up around their core users. For just a single client they may analyze up to 16K SKU’s for over 6K stores worldwide. There is no doubt that this data asset could be harnessed across multiple departments and user groups but restraint and focus helped them capitalize on a single user who was desperate for insights.
"Some of the best advice we got early on from senior people in the industry was to 'keep it simple' if we really wanted our target user group - national retail account managers for product companies - to be engaged users of our platform," notes Eric Green, CEO and co-founder of Askuity.
"And when a 30 year veteran retail sales manager tells us that our application is his homepage in his browser, that's a good sign that we're on the right path."
How Should the Data Be Presented?
I’ve found that the biggest mistake on the data side is trying to present too much information. Most startups in the space will find that they will eventually need to scale back to a few need-to-know insights with deep dive capability. Essentially, identify 2-3 need-to-know insights, and make that the focus of the product.
The goal of the product is to be consistently used by all users, not just power users, and the only way to accomplish this is to make it as simple as possible to discover the relevant insights. Reduce clicks and noise.
Another one of our portfolio companies, Content Analytics, is a great example of a startup successfully reimagining the user experience. They were capturing so much data that clients could have easily become overwhelmed. Simplifying the presentation of complex information was critical. David Feinleib, CEO of Content Analytics put it as follows:
“We work with brand managers and site merchants. These clients are busy managing the business and don't have time to dig through huge quantities of data. We present them with visual e-commerce dashboards that provide actionable insights rather than a pile of data. That allows them to prioritize and improve quickly.”
Understand Your Competition
Rather than thinking about competing products, think about competing processes. If you are selling into the marketing department, what is their current process for accomplishing the task you are serving? Your goal should be to make that process faster and more efficient. Additional features are great but if a potential client can do X faster with their current process, your offering of Y & Z doesn’t matter. You may win early but it will be difficult to last.
Slack has done a remarkable job of accomplishing this. They are in a super competitive space of workplace collaboration. They did not win because of features; they won because of ease and simplicity.
Data is a commodity, just because the dataset is big, does not mean the feature-set needs to be big. Identify your user and show them what they need to know faster and more efficiently than anyone else.