An organization’s ability to manage big data—the level at which they’ve invested in and have implemented big data architectures and analytics—is critically important to the realization of IoT value.
However, most companies are taking a largely ad hoc approach to big data today, according to aSeptember 2016 survey of 306 business leaders conducted by Harvard Business Review AnalyticServices. Nearly half of respondents said they pursue big data initiatives on a per-project basis,with just 18 percent saying they have an enterprise big data strategy and approach.
It’s not surprising then that most companies are taking a makeshift approach to their early IoT initiatives. Six out of 10 respondents said they have pursued some IoT point solutions or proofs of concept but have yet to develop an IoT strategy. Those that have that infrastructure in place may not have the data scientists on staff to make use of it. Many don’t have the master data they need to generate meaningful insights from IoT. Others have yet to build a business case around the IoT. And many companies don’t know where or how to start.
Nonetheless, business leaders have high hopes for IoT technology. Their expectations for the business benefits of IoT implementation are significant. They’re looking for the integration of IoT devices and data not simply to cut costs or increase efficiency but also to transform the business in some way. Some of the biggest business drivers behind the current usage of IoT are improving customer experience, creating new products and services, transforming business models, and increasing revenue, according to the survey.
The integration of IoT technology, data, and intelligence into the enterprise has the potential to create exponential rather than linear change. Those companies that aren’t investing in IoT will fall behind. The business cases for IoT will vary by industry. In some situations, the value will be obvious. We’ve had data streaming out of jet engines forever, and it’s clear that getting smarter about using that data could lead to improvements not just in operational efficiency and costs but improving the customer experience or creating new services.
In most industries, the threat of disruption is a powerful driver for IoT investments. There’s nothing like an imminent threat to clarify the thinking. So companies are starting to figure it out. But the initial activity is very fragmented. One company I’m working with has more than a dozen IoT projects that started organically, and they’re still popping up. They’re now realizing they will need to rationalize those and create a more scalable IoT environment.”Respondents to the survey said their organizations are implementing IoT for both internal operations and customer-facing solutions.
Today’s IoT initiatives are most likely to be found in the areas of customer experience (49 percent),operations (39 percent), customer service (38 percent), IT (32 percent), sales (28 percent), and logistics (27 percent). And companies are beginning to implement some specific programs based on IoT data and insight in the areas of customer relationship transformation, employee productivity improvements, asset tracking, and marketing programs.
A NEW DATA PARADIGM
Many of the initial use cases of IoT are focused on incremental improvements. “It’s the low-hanging fruit,” says Pareekh Jain, research vice president at HfS Research. “But those small projects will not yield transformation.”In part, that’s because companies have never dealt with anything comparable to IoT before. “It’s a different kind of data than we’re used to collecting and organizing—and there’s a lot of it. The volume and velocity are bigger and faster than we’re used to. It makes us have to think about hosting it, sorting it, aggregating it, archiving it, and integrating it in ways we’ve never done before.
And if we’re going to have hundreds of thousands of devices spitting out this data, we have to figure out how to do those things in a way that’s not going to slow the whole network down.”Survey respondents were clearly aware that big data in general and IoT specifically will require a new approach to networking. More than three-quarters of them (78 percent) said that new networking capabilities and technologies are very or somewhat important to their big data strategies.
Massive, fast-moving, and broadly sourced IoT data will create new network challenges. Nearly half of respondents (49 percent) said they are either exploring, investing in, or already have edge computing capabilities to conduct analytics at the source of the data.
THE UNIQUE CHALLENGES OF IoT
The organizational and technical challenges presented by IoT are substantial. Many enterprises simply don’t know where to start.More than any other factor, a lack of big data skills and capabilities is preventing organizations from using or acting on more of the big data that may be generated by IoT systems, according to the survey.
A big part of it is finding people experienced in collecting and managing the environments for processing this kind of data. Then on the analytics side, you have to find people who know how to aggregate and analyze this data.” Some companies are able to find experienced hires to lead the charge, he says, but many are partnering with vendors and service providers to bring in experienced talent and IoT-specific thinking.Another significant organizational challenge is changing existing business processes to incorporateIoT-driven insight.
If companies are to advance beyond collecting insight from stand-alone IoT initiatives to integrating IoT intelligence seamlessly into the organization, the business process change will be significant. You have to recognize that IoT is going to dramatically change processes and plan for the redesign and change management that goes along with that, Those process changes can impact customers as well.Another major issue: one-third of companies have yet to identify what problems to solve with IoT. Without contextual relevance and appreciation of business value, all you have is a lot of data. You have to move beyond just pushing data into a data lake and analyzing what you can to actually building a structured governance process around how you are going to ingest, analyze, and put context around this IoT data to get the most relevant outcomes.