With Industry 4.0 being hailed as the age of optimization, businesses are becoming hyper-focused on connecting the data sources needed to fuel this data optimization. In the race to win with AI and automation, companies are turning to infrastructure, integration, and data cleansing to jumpstart their IoT journeys.
This all seems kosher, right? We need a lot of data to power AI and automation, so it makes sense to connect disparate sources and devices at the start of IoT implementation. But by taking this integration-first approach, is IT missing the bigger picture?
Is IT having Big Data déjà vu?
I won’t argue that integration isn’t paramount to getting the most out of device data. But with their attention focused on the technical challenges, many companies are overlooking the business needs behind IoT.
Forbes reported that a Cisco survey found that “companies consider 76% of their IoT initiatives failures, and a majority said that IoT initiatives looked good on paper, but turned out to be more complex than expected.”
The investment -- both in time and money -- in integration is overshadowing the true business value IoT can enable. As IT executives hone in on how to collect and clean data, business executives are getting frustrated. In fact, according to the same Cisco survey, business execs are 57% less likely to find IoT initiatives successful than their IT counterparts.
What we’re seeing is Big Data deja vu. Or maybe it’s Big Data 2.0? Whatever we call it, we’re in a similar dilemma. When we hit the initial tipping point with Big Data in the early 2010s, we started seeing budgets shift from IT to Marketing, and ultimately to SaaS. The root of this shift is simple; SaaS companies could solve business problems faster than internal IT teams. And we’re hitting a similar tipping point now.
As IT works to solve the architecture challenges of IoT, businesses are getting impatient. But what’s different with Big Data 2.0, is that SaaS isn’t exactly positioned to dig out IT this time. With IoT and Big Data in Industry 4.0, companies are connecting more data sources than ever before -- and they’re doing it with different tech stacks, different architectures, and different infrastructures. There isn’t a one-size-fits-all solution to the technical challenges of IoT in 2019. But there is a singular solution for solving the IoT business challenge: start with the business need.
Build a Business Strategy, not an IoT Strategy
You’ll rarely hear me recommend starting any IT or Martech project without a strategy. With a background in data-driven marketing and product management, I like clear, goals-driven roadmaps. So it’s probably no surprise I’m suggesting the key to unlocking IoT success is having a business strategy. While I’ve long been a proponent of starting with a thorough strategy phase, the need now is even greater with the emergence of AI, Business Intelligence (BI), and other smart technologies, especially if you’re looking to IoT to improve your operations and drive change.
And if you don’t want to take my word for it, let’s look at what the market is saying. NetworkWorld reports that the number one problem companies face with IoT is “not understanding the data.” And Forbes reports the top challenge as companies failing to “present a compelling return on investment.”
Do you know what these top publications aren’t citing as the top cause for IoT failure? Data connectivity. While the web of IoT tools, integration, and data cleansing is complex, it’s not the inhibitor of IoT success. So why are so many businesses starting with the technical challenge over the business challenge?
The answers to this can vary greatly. I’ve heard customers cut out strategy due to tight budgets and timelines or simply because they believe it’s an overhyped, unnecessary expense. Whatever their justification may be, one thing’s for sure, with the huge amount of data being generated today, simply aggregating data isn’t enough to drive business value. To derive real value from IoT data, companies need to deliberately identify what problems they’re trying to solve and what data they need in their solutions.
Robert Plant, Associate Professor at Miami Business School, and Cherie Topham, a machine learning algorithm programmer, unpacked the core tenets of IoT strategy and success in a 2018 article for Forbes:
- Curation: Knowing what data matters
- Control: Making data dance
- Connectivity: Bringing data together
- Capital: Using data to discover savings
Plant and Topham argue that starting with Curation, or business strategy, is vital to achieving IoT success. Businesses need to be clear about the questions they need data to answer, so they can narrow in on the correct data to collect. This shift from collecting “all the data” to specific business-impact-driving data has multiple benefits:
- Small data collection: Focusing first on the business problem, enables businesses to isolate specific data sources to collect and connect. This can simplify data integration and cleansing and even cut back on timelines.
- Clarity on what to do with data: Starting with a narrower data set and a specific business problem helps companies avoid data overload. The IoT tool landscape is vast. From hardware to BI platforms and CRM, driving action with IoT data takes much more than just data. Starting with a business problem simplifies honing in on what data you need, what insights the data should produce, and what actions it should trigger. Overall, kicking off with a business strategy makes it easier to design your IoT infrastructure because you know what end your working toward.
When companies start with a business or customer need, they give themselves the focus needed to streamline data integration and IoT security and present ROI faster. If companies can show value for a specific business need, they’re more likely to get buy-in on more data collection and future IoT solutions.
Don’t Be Afraid to Start Small
Focus on demonstrating value before connecting all possible sources. Data sources will continue to evolve. The need to connect new sources and retrofit old devices isn’t going anywhere, so start where you can show value. Small wins in the short-term will give you the proof needed to develop a long-term IoT program.
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