Bill Schmarzo

Avoiding the IOT ‘Twister’ Business Strategy

IOT ‘Twister’

Most organizations’ ‪ IOT Strategy look like a game of ‪ ‘Twister’ with progress across important IOT capabilities such as architecture, technology, data, ‪ analytics and governance; variables comprising a series of random investments and decisions.

There is something very different about the Internet of Things (IOT) craze versus other technology-induced bacchanalias. The IOT craze appears to be driven by the business stakeholders versus the Information Technology (IT) organization.

Could it be said then that it’s the tangible financial Return on Investment (ROI) that drives the IOT business strategy?

There are many IOT vendors who provide IOT “solutions” to address one particular operational need (e.g., predictive equipment maintenance, energy optimization, load balancing, network optimization) that in many cases provides a clear and tangible ROI.

This makes the purchase decision relatively easy for the business stakeholders because these decisions pay for themselves in 24 months or less.

Figure: IOT Applications by Industry

However many of these IOT solutions are “point solutions” – they don’t necessarily integrate into the existing corporate IT architecture and support organizations – which makes them easier for the business to buy and deploy because the “solution” doesn’t involve IT. And the standalone nature of these IOT solutions introduce all sorts of challenges for the CIO and IT organization, including:

  • Lack of integration into a master IOT architecture
  • Difficulty scaling across the entire organization
  • Difficulty operationalizing the point solution

Let’s look at each of these IT challenges in more detail.

IOT Challenge #1: Integration Challenge

Integration of IOT vendor point solutions into a more holistic IOT architecture is not trivial. Many of these point IOT solutions are offered on a cloud and a “cloud” is not a “cloud.” The interoperability between clouds is not the concern of the IOT point solution provider because that would just “slow down the sales cycle.”

Consequently, it is left to the IT organization to try to integrate this point IOT solution – after the fact – into the organization’s more holistic IOT architecture (see Figure 3).

Figure: IOT Reference Architecture

IT must avoid islands of IOT architectures that inhibit the ability to share the IOT data and resulting analytics across other business and operational use cases. And if we thought data silos were a challenge, wait until we have to address “architecture silos”!

IOT Challenge #2: Scalability Challenge

Business stakeholders make isolated IOT product decisions because of the compelling ROI from the perspective of that particular business unit. However, the IOT solution vendor is motivated to sell the solution to other business units and that’s when the scalability problems start because many IOT solutions don’t scale.

Scalability” refers to the ability to expand without running into obstacles that increase the per-unit costs of doing business, the ability to increase production inputs by a certain percentage, and get an equal percentage increase in output[1].

However, most organizations want more than just “linear scalability”; these organizations want to leverage “economies of scale” to drive down incremental or marginal costs.

Economies of scale arise when there is an inverse relationship between the quantity produced and per-unit fixed costs; i.e. the greater the quantity of goods produced, the lower the per-unit fixed cost because costs are spread over a larger number of goods. Economies of scale reduce variable costs per unit via operational efficiencies and synergies.[2] 

For example, if the first instance of something costs $100 to implement, then economics of scale means that the second instance is cheaper to implement ($50), and the third instance even cheaper ($25), and so on. Big organizations leverage economies of scale to accelerate their profitability, but if organizations cannot achieve economies of scale due to scalability problems, then the opposite is likely to happen – the overhead associated with each subsequent implementation increases in a debilitating, non-linear fashion.

IOT Challenge #3: Operationalization Difficulties

Operationalization is everything that happens after the initial implementation around the ongoing management of the IOT point solution. There is significant work that needs to be done after the initial IOT solution implementation to ensure that the IOT solution – and its supporting architecture, technology, data processing and analytics – effortlessly performs for the business unit in a production environment (see Figure 5).

 

Figure: IOT Operationalization Considerations

Summary: Delivering on the Promise of IOT Monetization

The result of purchasing IOT point solutions and its 3 IOT challenges is an IOT business strategy that looks like a game of “Twister,” with one hand at the Optimization stage for one IOT capability and other hand at the Monitoring stage and etc…

If a chain is only as strong as its weakest link, then one’s IOT business strategy is only as strong as its weakest developed IOT capability.

 

Figure: Haphazard IOT Business Strategy Development

Ultimately, the goal of any comprehensive IOT Business Strategy should be to couple the new sources of IOT data with advanced analytics to power the organization’s most important business and operational use cases (see the blog “Monetizing the Internet of Things (IOT)” for more details about driving IOT monetization).

By Bill Schmarzo

Bill Schmarzo

CTO, IoT and Analytics at Hitachi Vantara (aka “Dean of Big Data”)

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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