It’s the dullest of truisms to say, “Big Data means big bucks.” But it’s also true, and not so trite, to say that Big Data isn’t for everyone. Sure, forecasters say the Big Data analytics market is going berserk, but small and mid-sized businesses may not yet be positioned to effectively leverage Big Data and the technologies needed to understand it. Some market watchers have doubts about the wisdom of using big-data technologies when you’re working with small data.

For smaller outfits, particularly startups, raising money in Big Data can be difficult and the market is already crowded. And because data are not business outcomes but merely tools for achieving outcomes, they are critical to the “Build-Measure-Learn” approach that underpins the Lean-Startup strategy.

Alistair Croll, analyst and co-author of Lean Analytics, reminds us that “analytics is a conversation with your market about your product.” As a proponent of the Lean method, he’s talking about the critical function that data analytics play in hypothesis testing, customer discovery, rapid iteration, and pivoting that are central to the strategy. It may look like Big Data on a smaller scale, but the thrust is the same: harvesting meaningful information from data that results in business value.

To the average founder, entrepreneur or small investor, the Big Data analytics market appears to be beyond reach. But opportunities in data analytics abound. Customer adoption of artificial intelligence is increasing daily. We’re no longer talking about early adopters. Think of Amazon’s Alexa: only two years old, and it’s practically ubiquitous. Imagine where it will go once it’s  interconnected with smartphone technology.

AI represents a maturing set of technologies that is now making sense to the average consumer.

What AI and Lean Startup share in common is the instructional value of data. This point was driven home by Slava Solonitsyn, investor in hardware, IoT, and interactive digital-media startups, at this year’s Startup Launchpad, Asia’s largest hardware trade show, in Hong Kong.

Parsing some of the differences between the Asian and American markets, Solonitsyn observed that consumer-hardware startups in the U.S. are sorely challenged, it’s such a crowded market. Meanwhile, software and AI are growing by leaps and bounds. Smart entrepreneurs should note, he says, that success lies in SaaS plus hardware plus data. He gives several examples, including these:

  • Ring video doorbells: Combining IoT (connected devices) with video surveillance (computer vision) and subscriber services (mobile app, cloud access, recurring revenue, customer engagement), Ring is finding enormous success.
  • Peloton indoor fitness bike: Peloton brings together high-quality hardware, a subscription model of service and a software layer that provides both live and on-demand coaching for those who are serious about fitness.
  • Naked 3D Fitness Tracker: It may look like a mirror, but Naked is a sophisticated sensing device that provides instant feedback on the progress of one’s fitness regimen. It collects data that can enable C2M (consumer-to-manufacturer) transactions such as buying clothing that is custom-fitted exclusively to you.

Data-enabled devices allow businesses to embrace AI while garnering a great amount of data from the clickstream or voice interface to better understand the customer’s needs and whether the business model should persist or pivot. For example, initially collecting data using a low-priced minimum viable product subsidized with crowd-sourced funds (e.g. Kickstarter, Indiegogo, etc), will permit the business to iterate, perhaps yield the time and resources to refine the algorithm(s) on which the AI depends, and to focus on what Solonitsyn calls one’s “beachhead market,” the evangelists who may want to help co-develop the product. Later comes the launch of a more robust retail version. Key to this approach is the subscription model based in software embedded into the product, drawing recurring revenue from engaged, repeat customers.

Data aren’t useful if they are too big to manage. Oftentimes, lean data can yield bigger results.

Solonitsyn’s advice to smaller businesses includes using existing platforms for key functionality rather than to re-invent them. There’s no need, for example, to create one’s own platform for cloud storage, cybersecurity for connected devices, or AI processing for computer-vision surveillance. All of that already exists and their performance is proven.

Expert mentoring and an investment infrastructure are key components, of course, providing direction, resources and momentum, regardless of whether your enterprise relies on industrial-scale Big Data or on lean data, “an Occam’s razor approach to data capture and analysis.”

You’ll find such mentoring and infrastructure at tekMountain, one of the nation’s emerging innovation and entrepreneurial centers.

This blog was produced by the tekMountain Team of Sean Ahlum, Amanda Sipes, Zach Cioffi and Beth Roddy with lead writer Bill DiNome.

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