Big Data

5 Ways the Internet of Things Drives New $$$ Opportunities

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) April 9, 2014

4 9 14 Bill Boom Image 1BOOM!! That’s a sonic explosion caused by the explosive growth of data in the digital universe. The EMC Digital Universe Study – with research and analysis by IDC – highlights this data eruption with a few factoids:

  • From 2013 to 2020, the digital universe will grow by a factor of 10x—from 4.4 trillion gigabytes to 44 trillion.
  • Between 2013 and 2020 the division of the digital universe between mature and emerging markets (e.g., China) will switch—from 60% accounted for by mature markets to 60% of the data in the digital universe coming from emerging markets.
  • In 2013, two thirds of the digital universe bits were created or captured by consumers and workers, yet enterprises had responsibility for 85% of the digital universe.

But this explosion of data doesn’t really mean much if one can’t capture, integrate, and analyze the wealth of data to uncover new insight about your customers, products, markets, and operations.

“The Digital Universe can power business transformation by leveraging new sources of customer, product, market and operational data to optimize key business processes and uncover new monetization opportunities.”

The Internet of Things Yields New Business Applications

EMC and IDC see the Internet of Things (IoT) creating new opportunities for business in five main ways (see Figure 1):

  • New business models:  The IoT will help companies create new value streams for customers, institute processes that speed time to market, triage market performance, and respond rapidly to customer needs.
  • Real-time information on mission-critical systems:  With IoT, organizations can capture more data about their processes and products in a more timely fashion to create new revenue streams, improve operational efficiency, and increase customer loyalty.
  • Diversification of revenue streams:  The IoT can help companies create new services and new revenue streams on top of traditional products, e.g., vending machine vendors offering inventory management to those who supply the goods in the machine.
  • Global visibility:  The IoT will make it easier for enterprises to see across the business regardless of location, including tracking effectiveness and efficacy from one end of the supply chain to the other.
  • Efficient, intelligent operations:  Access to information from autonomous end points, as today’s smart grid already supplies to utility companies, will allow organizations to make on-the-fly decisions on pricing, logistics, sales, and support deployment, etc.
New ways Internet of Things will create new business opportunities

Figure 1: New Ways IoT Will Create New Business Opportunities

The real benefits of the Internet of Things will not, however, be realized until leading companies develop the next generation of applications that address specific business needs from this wealth of data. It is within the Internet of Things that we’ll see a new generation of applications, such as:

  • Predictive maintenance:  predict when and how a device will fail and what replacement and maintenance parts and service personnel skills will be required to preempt the failure
  • Loss prevention:  monitor device and network usage to flag unusual usage situations that may be indicators of revenue loss and theft
  • Asset utilization:  monitor and predict asset utilization under a number of different usage scenarios in order to improve asset, device, and node utilization
  • Inventory tracking:  monitor inventory levels and inventory assets to minimize loss and waste and improve inventory utilization
  • Disaster planning and recovery:  model different disaster scenarios and likely device and network usage requirements to proactively plan for disaster situations (e.g., hurricanes, ice storms, tornados, earthquakes, Zodiac Killers)
  • Downtime minimization:  leverage predictive maintenance and inventory tracking to identify high-probability downtime situations and ensure that the right maintenance and replacement parts are available, as well as the right skilled service personnel
  • Energy usage optimization:  optimize energy usage given current and historical device performance, historical and predicted energy costs, and device performance requirements
  • Device performance effectiveness:  monitor and optimize individual device performance/throughput based upon historical performance, given certain workloads and environmental conditions and coupled with a detailed profile of the performance behaviors of that device or node
  • Network performance management:  monitor and manage/fine-tune the performance of a network of devices given current load, required performance requirements (service level agreements), and forecasted performance requirements
  • Capacity utilization:  reallocate device resources and jobs to optimize network and device performance given the history of device interactions and current and forecasted performance requirements
  • Capacity planning:  predictive and prescriptive analytics that model product and device usage and in real time, make resource allocations and automate the provisioning of new capabilities (turning on and off capacity as dictated by the predictive models) in order to ensure the required capacity at the optimal price
  • Demand forecasting:  leverage device behavioral models, actual usage patterns and trends, and external factors (weather, traffic, events) to forecast longer-term network configurations and product and network build out
  • Pricing optimization:  understand device usage patterns, coupled with demand forecasting, to optimize device and network pricing—lowering pricing when demand is low and increasing pricing when demand is higher; almost like surge pricing, but hopefully without the same customer satisfaction issues
  • Yield management:  optimizing device and network usage to extract the most value out of the overall network capacity and capabilities
  • Loading balancing optimization:  balancing usage load in light of forecasted demand to ensure that all nodes are being utilized equally and to avoid performance bottlenecks

The Third Platform is the Monetization Platform

Game-changing digital universe monetization opportunities will appear once organizations understand how to leverage the third platform to deliver new analytic insight to customers and front-line employees. Organizations are trying to move from descriptive analytics to predictive and prescriptive analytics. And more and more, the results of digital universe analysis will manifest themselves in actionable analytics (e.g., recommendations) on the mobile devices used by both front-line employees and consumed by customers (see Figure 2).

The third platform is driving monetization realization

Figure 2: The Third Platform is Driving Monetization Realization

Think of John Deere and its ability to deploy new software to tractors in the field—software that can help tractors drive straight across plowed fields. Think of EMI Music, which drives marketing of artists based on data from a database of a million music-listener surveys and information from Spotify music streams. Think of mobile phone companies that can leverage their customers’ apps usage behaviors and Global Positioning System (GPS) data to delivery new location-based services. Think of energy producers who can analyze wind turbine sensors, error codes, and vibration data to improve network uptime and minimize revenue loss via predictive maintenance.

And all of these audiences—farmers, musicians, customers, technicians, engineers, store managers, nurses, teachers, and more—are finding that the optimal way to consume these insights is via their mobile devices.

Ready, Set…Set…Set…

Few organizations are at the top of analytics maturity

While the technology challenges in capturing, managing, backing up, securing, and analyzing these huge volumes of real-time data are significant, the biggest challenges are, first and foremost, organizational. Here are three steps all organizations must contemplate to prepare for these monetization opportunities:

  • Create a C-level position in charge of identifying and overseeing new digital business opportunities:  whether it involves creating a new position (e.g., Chief Digital Officer), or an enhancing an existing one (e.g., upgrading the CIO’s current responsibilities), the executive would be responsible for identifying and pursuing new analysis-driven revenue streams.
  • Develop and continuously revise an executive-team understanding of the new digital landscape for your enterprise:  who are the new (and potential) digital competitors? How are you going to cooperate with others in your industry to anticipate and thwart digital disruption? What are the short- and long-term steps you must take to ensure a smooth and timely digital transformation?
  • Design and execute a plan for accelerated investments in digital enterprise technologies and skills:  re-allocate resources across the business based on key business initiative, invest in promising data collection and analysis areas, and identify the gaps in talent and skills required for success in the era of the Third Platform.

Increased investment in human capital and the new skills required today is the first order of business for all organizations. However, organizations need to invest the time to make sure where and how to start; to drive collaborate between IT and key business executives to understand or envision the realm of what’s possible with respect to these new sources of data and advanced analytics. And organizations need to integrate and/or expand their user experience expertise and capabilities in order to exploit the move to the third platform.

Remember: organizations don’t need a Big Data strategy to exploit the digital universe; organizations need a business plan that integrates the data and capabilities enabled by Big Data and the digital universe.

Bill Schmarzo

About Bill Schmarzo


CTO, Dell EMC Services (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”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. 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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