Book Summary: Lesson 5 From Disrupting Digital Business – Build For Insight Streams

Published on May 31, 2015 by R "Ray" Wang

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Get All 10 Lessons Learned From Disrupting Digital Business

As with the beginning of every revolution, those in the midst of it can feel it, sense it, and realize that something big is happening. Yet it’s hard to quantify the shift. The data isn’t clear. It’s hard to measure. Pace of change is accelerating. Old rules seem not to apply.

Sometimes when you are in the thick of it, it’s hard to describe what’s happening.  In the case of digital business, these models have progressed over the past 20 years.  However, non-traditional competitors have each exploited a few patterns with massive success. However, as the models evolved, winners realize there are more than a handful of patterns.

Lesson 1 – Transform Business Models And Engagement

Lesson 2 – Keep The Brand Promise

Lesson 3 – Sell The Smallest Unit You Can

Lesson 4 – Know That Data Is The Foundation Of Digital Business

Lesson 5 – Build For Insight Streams

Lesson 6 – Win With Network Economies

Lesson 7 – Humanize Digital With Digital Artisans

Lesson 8 – Democratize Distribution With P2P Networks

Lesson 9 – Deliver Intention Driven, Mass Personalization At Scale

Lesson 10 – Segment by Digital Proficiency Not Age

In fact, the impact is significant and now quantifiable with 52% of the Fortune 500 gone since 2000 and the average age of the S&P 500 company in 1960 is down from 60 years to a little more than 12 projected in 2020.  That is a 500% compression that has changed the market landscape forever in almost every industry.

Over the course of the next 10 weeks, I’ll be sharing one lesson per week.  For traditional businesses to succeed, they will have to apply all 10 lessons from Disrupting Digital Business in order to not only survive, but also relearn how to thrive.

Build For Insight Streams

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One of the biggest opportunities for monetizing digital business will come from insight streams.   These insights will come from both least likely sources and the most obvious.   For example, least likely sources include the amount of power consumer, water used, visitors into the building, foot traffic on the sidewalk, and density of the parking lot.   These sources may seem mundane and useless information to most of us, but large insight brokers will take that data to drive contextually relevant information.  Obvious sources include internal systems such as work force performance data, customer satisfaction, product quality stats, and other areas.   The goal here is to use this information to differentiate.   There are three models to build big data/insight business models.  I’m going to pull from my December 6th, 2012 post on Harvard Business Review here and share with you these models (Figure 1).

Figure 1. Digital Organizations Build Business Models Based On Insight

Big Data Business Models

  1. Differentiation of insight creates new experiences. For a decade or so now, we’ve seen technology and data bring new levels of personalization and relevance. Google’s AdSense delivers advertising that’s actually related to what users are looking for. Online retailers are able to offer — via FedEx, UPS, and even the U.S. Postal Service — up to the minute tracking of where your packages are. Map services from Google, Microsoft, Yahoo!, and now Apple provide information linked to where you are.  Big data offers opportunities for many more service offerings that will improve customer satisfaction and provide contextual relevance. Imagine package tracking that allows you to change the delivery address as you head from home to office. Or map-based services that link your fuel supply to availability of fueling stations. If you were low on fuel and your car spoke to your maps app, you could not only find the nearest open gas stations within a 10-mile radius, but also receive the price per gallon. I’d personally pay a few dollars a month for a contextual service that delivers the peace of mind of never running out of fuel on the road.
  2. Brokering augments the value of insight. Companies such as Bloomberg, Experian, Dun & Bradstreet already sell raw information, provide benchmarking services, and deliver analysis and insights with structured data sources. In a big data world, though, these propriety systems may struggle to keep up. Opportunities will arise for new forms of information brokering and new types of brokers that address new unstructured, often open data sources such as social media, chat streams, and video. Organizations will mash up data to create new revenue streams. The permutations of available data will explode, leading to sub-sub specialized streams that can tell you the number of left-handed Toyota drivers who drink four cups of coffee every day but are vegan and seek a car wash during their lunch break. New players will emerge to bring these insights together and repackage them to provide relevancy and context.  For example, retailers like Amazon could sell raw information on the hottest purchase categories. Additional data on weather patterns and payment volumes from other partners could help suppliers pinpoint demand signals even more closely. These new analysis and insight streams could be created and maintained by information brokers who could sort by age, location, interest, and other categories. With endless permutations, brokers’ business models would align by industries, geographies, and user roles.
  3. Delivery networks enable the monetization of insight. To be truly valuable, all this information has to be delivered into the hands of those who can use it, when they can use it. Content creators — the information providers and brokers — will seek placement and distribution in as many ways as possible.  This means, first, ample opportunities for the arms dealers — the suppliers of the technologies that make all this gathering and exchange of data possible. It also suggests a role for new marketplaces that facilitate the spot trading of insight, and deal room services that allow for private information brokering. The most intriguing opportunities, though, may be in the creation of delivery networks where information is aggregated, exchanged, and reconstituted into newer and cleaner insight streams. Similar to the cable TV model for content delivery, these delivery networks will be the essential funnel through which information-based offerings will find their markets and be monetized.  Few organizations will have the capital to create end-to-end content delivery networks that can go from cloud to devices. Today, Amazon, Apple, Bloomberg, Google, and Microsoft show such potential, as they own the distribution chain from cloud to device and some starter content. Telecom giants such as AT&T, Verizon, Comcast, and BT have an opportunity to also provide infrastructure, however, we haven’t seen significant movement to move beyond voice and data services. Big data could be their opportunity. Meanwhile, content creators — the information providers and brokers — will likely seek placement and distribution in as many delivery networks as possible. Content relevancy will emerge as a strategic competency in delivering offers in ad networks based on the context by role, relationship, product ownership, location, time, sentiment, and even intent. For example, large wireless carriers can map traffic flows down to the cell tower. Using this data, carriers could work with display advertisers to optimize advertising rates for the most popular routes on football game days based on digital foot traffic.

Homework

Start by thinking of the obvious and non-obvious data sources.   Put on your privacy hat and make sure you aren’t brokering any PII.  Armed with these data sources, start thinking about potential business models from these insights:

1.  Where you can create new brand promises with insight?  Take a look at the March 2015 article in Wired on the Disney Magic Band.  Note how they are using this device to create differentiated experiences.

2. What insights can you broker and trade?  Start with the questions you’d like to ask of your systems. Find out what insight sources are missing for you to make that decision.  Identify partners to trade with.

3. Find out if there are industry associations or consumer marketplaces where this data could be exchanged.  Determine what contextual services you may want to launch.

The result of this exercise is a list of potential projects to pilot.  Make sure you rank them based on brand promise delivered, value created, return on investment, and level of strategic differentiation.

The Complete 10 Lessons Learned From Disrupting Digital Business

For those attending the full keynotes and book tours, you’ll get the complete session and in many cases a copied of a signed booked.   For those following virtually, I’ve provided the slimmed down slide share deck for your use.

You now have the 10 lessons learned to disrupt digital business in your hands. You can take this information and change the world in front of you or choose to sit on the knowledge as the world passes you by and digital darwinism consumes your organization.

I trust you will do the right thing. And when you want some company, come join us as a client at Constellation Research where we’re not afraid of the future and the art of the possible.

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Your POV.

Are you ready to disrupt digital business?  Have you ordered the book?

Add your comments to the blog or reach me via email: R (at) ConstellationR (dot) com or R (at) SoftwareInsider (dot) org.

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