Bill Schmarzo

Why Accept the Hype? Time to Transform How We Approach Emerging Technology

Time to Transform How We Approach Emerging Technology

It’s like a rite of passage – a new technology pops onto the scene and the “pundits,” analysts and venture capitalists (among others) immediately start to over-hype the technology:

It’ll stop world hunger

It’ll cure cancer

It’ll end poverty

It’ll help the Cubs win the World Series (okay, that one was true)

And the ultimate result? The technology is forced to traverse the infamous Gartner Hype Cycle, soaring to the Peak of Inflated and Totally Unrealistic Expectations and then traveling down the slippery slope into the Trough of Disillusionment (also known as the Trough of CLM: Career Limiting Moves), before finding its rightful footing and starts to deliver business value (see Figure 1).

Figure 1: Traversing the Gartner Hype Cycle

Note: Hats off to Gartner for institutionalizing the Hype Curve and using it as an effective way to show how screwed up IT is in adopting new technologies.

The path to the Plateau of Productivity is a long slug, and many careers and hundreds of millions of dollars will be wasted along the journey.  But it shouldn’t be this waybecause the ultimate cost is much higher than just the costs associated with the Peak of Inflated Expectations.  Let me explain.

Emerging Technologies Damned to the Hype Curve Quagmire

Figure 2 shows the 2018 Gartner Hype Cycle for Emerging Technologies, all promising technologies for which the vast majority will never reach their full potential.

Figure 2:  2018 Gartner Hype Cycle for Emerging Technologies

Lots of interesting technologies including Smart Dust (where’s that darn vacuum?), flying autonomous vehicles (UFO’s?), blockchain (…yet again), and quantum (leap) computing.  Some of these technologies will eventually navigate the Hype Curve to provide business value by solving strategic and meaningful business problems. But the path to get there is very expensive.

The green area underneath the “Peak of Inflated Expectations ” in Figure 3 reflects wasted investments in money, time and resources in trying to “monetize” or “commercialize” technologies for which there is not yet business justification.

Figure 3:  Wasted Financial and Human Costs of Over-hyped Technologies

While the exact amount of wasted financial and human capital in the green area varies widely across different technologies, suffice to say that lots of money and human resources are wasted with “technology solutions in search of a business problem.”

But probably the bigger cost for emerging technologies is the lost economic potential for emerging technologies that gets mis-positioned early in the Hype Cycle and never gains the level of organizational adoption necessary to drive material business and financial impact (see Figure 4).

Figure 4:  The Economic Cost of Mis-Cast and Poorly Focused Technologies

It’s the ultimate “Field of Dreams” moment with non-scientists cranking away on science experiments.  While there is value in organizations getting familiar with new technologies, the technology Proofs of Concepts should NOT be inflicted upon business stakeholders with over-fabricated expectations of grandeur.  Business users long ago stopped believing in the “Silver Bullet” solution – a simple and seemingly magical solution to a complicated problem.

How does our technology industry avoid these two costly mistakes?  What steps can we take at the introduction of an emerging technology to avoid having to traipse along the Hype Cycle to achieve economic value from that technology? Here’s my recipe.

1) Begin with an End in Mind

It’s really simple:  Understand your organization’s key Business Initiatives. Understand what’s important to the organization from a business, financial and/or customer perspective. Stephen Covey said it well: “Begin with an end in mind.”  While we may not understand the technology journey that we’ll experience trying to reach that end, the end point should not be a mystery.

Any emerging technology initiative – big data, artificial intelligence, IoT, blockchain, quantum computing, particle beams, invisibility rings – should start by understanding the organization’s business initiatives. Then conducting envisioning workshops with key stakeholders to identify, validate, vet, value and prioritize the use cases that emerging technology might support.

See the blog “The #1 IOT Challenge: Use Case Identification, Validation and Prioritization” for more details on the tools and techniques to help drive this envisioning process.

2) Understand the Technology Capabilities…But Within a Business Frame

While having a business goal is critical, it is important for IT to gain familiarization with how the technologies work, what might be required to support the technologies, and what sorts of problems can potentially be addressed with these emerging technologies.  Attend conferences, read articles, attend meet ups…but realize that you can’t always believe what you hear or read (as you read this blog).  So organizations are going to need to create their own sand boxes and labs where they can safely gain some hands-on experience with these technologies.

This is a great chance to leverage Design techniques like the Opportunity Report (See Figure 5).

Figure 5:  Opportunity Report

“You can’t get to the What and the Why by focusing on the How.”  So having a business frame (e.g., reduce unplanned operational downtime, improve customer retention, reduce hospital readmissions, improve 5 year graduation rates) around your technology exploration process will ensure that what you are exploring will ultimately have economic value to the organization.

3) Build out the Solution Architecture

Next, organizations must create an architecture that leverages big data, IoT, advanced analytics and application development to deliver “intelligent” applications (applications embedded with artificial intelligence that get smarter with every customer interaction) and “smart” entities (that leverage edge-to-core IOT analytics to create “continuously learning” entities such as cities, cars, airports, hospitals, utilities, schools). See Figure 6.

Figure 6: Hitachi Lumada IoT Platform Architecture

In order to deliver “smart” things and “intelligent” applications, organizations need to coordinate and align the following business and technology capabilities:

  • Build out the organization’s data and analytics capabilities for uncovering customer, product and operational insights that power the organization’s top priority business initiatives
  • Embed analytic insights and evidence-based recommendations into applications that enable customers, partners, and employees to learn from each customer interaction
  • Leverage IoT to enable near-real time operational and product performance optimization that further enhances business decision-making and extends more value to customers
  • Enhance and protect the organization’s data, analytic and application development assets with data management, security and authentication services

See the blog “Internet of Things: Getting from Connected to Smart” for more details on creating smart entities.

4) Use Design to Drive Organizational Alignment and Adoption

Finally, embrace Design as a way to drive organizational alignment and adoption.  While it is popular to think of Design and Design Thinking as techniques for uncovering customer, product and market requirements, these same tools can also drive organizational alignment and adoption with respect to where and how these emerging technologies can be best deployed to drive meaningful business value (see Figure 7).

Figure 7:  Leveraging Design to Drive Organizational Alignment and Adoption

What good is this new technology adoption approach if we don’t have the commitment of the organization to actually use the technologies to drive new sources of business and customer value?

Avoiding The Hype Curve Summary

As a society, we are wasting the economic potential of new technologies.  While it’s true that some of these technologies don’t deserve to make it out of the labs, we could certainly get more value, and at a greatly reduced cost, if we following these 4 simple steps:

  1. Begin with an End in Mind
  2. Understand the Technology Capabilities…But within a Business Frame
  3. Build out the Solution Architecture
  4. Use Design to Drive Organizational Alignment and Adoption

While technology werewolves may exist, the silver bullet to kill them does not.

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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