• Data Science
  • Derek Russell
  • DEC 13, 2017

Attention Business Leaders: How Will You Be Disrupted?

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With all the talk on “the street” about disruption in the various markets, from the auto industry to retail, it can be easy to act reactively and change too fast too soon rather then to be strategic and do what is best for your line of business (Amazon popped up on 10% of US earnings calls as of late July according to Reuters). But exactly what is disruption and how do managers strategize and run projects to counter it? Beyond being bounced across the lecture halls of the world’s top business schools, and in the board rooms of organizations — what does disruption actually mean, what are the implications, and how can a business leader get in front of its acceleration?

We’ve all seen it, whether you’ve gotten your groove on with Spotify to play your favorite playlist, used facial detection on your smart phone to capture that perfect picture of your daughter spilling mashed peas onto the dining room carpet, or enjoyed that priceless room overlooking the ocean on your anniversary (thank you Airbnb). Dare I mention what Amazon is doing to many a handful of industries? Ask Blue Apron, whose disappointing IPO, the worst of 2017, could be credited to the recent announcement of Amazon’s meal-kit business. We see and utilize these disruptive businesses everyday, and welcome the changes in services and products to make our lives better, but we sometimes forget that there were a great number of working variations of these offerings far before the digital revolution.

Disruption will happen to each and every one of our organizations at an increasing rate, as depicted by the average company lifespans in the S&P 500 in the chart below, but it is how we prepare business models to better utilize technology, human capital, and education to democratize data — to put the power of disruption in the hand of the incumbent, and to allow a better understanding of who the customer is and what is most valuable to their experience and needs.


(How do organizations prepare themselves for change, and how can the change be fast and methodical enough to allow for longer age?)

Productivity: Optimizing the learn vector

In Sam Harris’s “Waking Up” podcast, his “Landscape of the Mind” episode pursues a conversation with Founder of WIRED magazine’s Kevin Kelly, and they discuss the opportunity for human intelligence and how this applies to learning in an increasingly changing technological landscape, “the only literacy that should be taught in school…is that you can learn how to learn…this will be the only true meta-skill you will need in the future, if you want knowledge, you ask a machine” One moral of the conversation: we are utilizing technology more, and older populations will have harder times with technology than do the recent technology natives, but these younger folks will suffer eventually from disconnects as they are introduced to technologies of the future (think millennials raised around mobile phones to new generations growing up with augmented reality). Organizations must now develop “a curriculum of learning how to learn”.Technology is exponentially increasing in intensity far faster than humans can learn how to wield them, and the only antidote to this is a hyper growth mindset of interacting with technology and utilizing it at fast as possible. How can we help employees and teams within organizations learn “better” and faster to keep up?

I recently graduated from a full-time MBA, and was hired by Microsoft to be a trusted advisor for our customers, to help them modernize their data estate and digitally transform. The learning curve has been incredibly steep, and to be successful, I knew that I could not learn in the same ways I have in the past (analogue learning processes, adapting in a siloed environment, waiting to be “taught” by the organization in a formal program). Things are moving far too quickly, and my customers need partners that coach them in modernization and walk the walk. Instead of relying on these old ways of learning in a new role, I have been relying on data, technology, and people.

I am utilizing insights driven by our internal visualization dashboards, Power BI, to better understand how our customers are interacting with our products and how we can better serve them. LinkedIn has been a life saver, searching for practitioners within the organization that can speak to certain focus areas, and quickly connecting with them in virtual environments or using Cortana powered “chat-bots” to schedule meetings. What has been most helpful; connecting to Microsoft’s internal product rallies via cloud, and ingesting videos and keynotes while on flights or on the treadmill at the gym, applying this to my customers business challenges, and sending out digital resources to customers in an easily accessible portal with Linkedin’s PointdriveGetting better at accessing insights quickly, digitally, and with a portfolio of various tools and environments has optimized my learning vector and has allowed me to make better decisions in guiding my customers with effective solutions, backed by insights to help them modernize.

It doesn’t end here, other synergistic tools that multiply productivity can include machine learning models, which can run autonomously on top of any repository of data, gather more data through self teaching, and create additional insights that lead to better decision making for managers. Delivering intelligence has never been needed at such a steep rate, if we cannot learn, educate, and deploy fast, then our customers must look elsewhere. Keeping employees connected, creative, and collaborating with ease enhances productivity, and enhances learning, allowing businesses more time to focus on their proprietary functions. The faster people can get better at learning, the faster they can distribute value with the appropriate pace of change within the organization.

Cloud: Big Data is a challenge, not a solution


(CFO’s measure the impacts of cloud on their line of business)

Then there is the cloud. While still, many people are confused as to what this darn thing is. In Michael Friedman’s book, “Thank You for Waiting”, he gives some data from the Business Insider on the current understanding of cloud technology, roughly 51% of respondents (including millennials) believed that stormy weather would impact the cloud. This is what the cloud is in plain language; an owner of a large computer server estate (think of a business that owns a warehouse full of computers that aren’t being used) decides one day to monetize the extra storage and compute space that wasn’t being used by these extra computers — poof, the cloud is born. Along with the cloud comes this nebulous term of “Big Data” and how it is the solution to everything. Yet, the abundance of data around us is compounding with time, from what we put in our lattes to how long we view online content. A flight in a Boeing 747 can produce 500 gigabytes of data while a one minute ride in an autonomous car can produce nearly the same. Big data is now a challenge that we must approach methodically — how we ingest it, store it, and the manor from which we organize and turn it into insightful decision making is the solution. It’s not that easy. In the last 2 years, we’ve collected 90% of the worlds total data over the history of human kind. Doing something with the ever increasing ingestion of data is what businesses are struggling to strategize around.


The bigger question for business is: “how can I use the cloud to impact my bottom line and generate new revenue streams?”

Besides scalability, security, accessibility from multiple devices and geolocations globally…etc, you are now able to put your data into one central location and analyze it. Here is where we input A.I. and Machine Learning. What is Machine Learning? Data is fed to a machine, allowing the machine to build guessing frameworks with the data. As you feed more data into the machine’s frameworks, it gets better and better at predicting future outcome with this evolving framework, it can infinitely exceed at building a better prediction capability in this fashion. The machine can now automate decisions and tasks, or help humans make decisions and execute tasks, using this data based learning curriculum. Sound too futuristic? Now imagine you were Rolls Royce, one of the worlds biggest producers of jet engines (and a valued Microsoft customer), and you wanted to know when your engines would fail based on telemetry that sensors were feeding you from engines all around the world. In Roll’s Royce’s case, they are paid only when their engines are running, you can call this “pay as you go” or “engines aaS”. IoT sensors send signals about the health of the engine to the cloud, and with some machine learning and computing, predictive maintenance algorithms allow these engines to be flagged for maintenance far before anything actually goes wrong, allowing maintenance crews to wait at relevant airports for the plane to land, with the right tools in hand to get the engine to full health without displacing run time. This is a true story:

These technologies are not science fiction, and they are not just meant for organizations with deep pockets. They are real solutions that are helping thousands of businesses all over the world today fight the power of disruption with digital transformation. In aggregate the message is clear, how you will be disrupted depends on how long it takes for your organization to develop optimal learning and productivity environments, the embrace of scale cloud computing and processing, and the utilization of AI and Machine Learning to make better predictions and more intelligence based actions.

Let’s continue the conversation, share our ideas and visions, help educate each other, and reflect on how we can better embrace the digital transformation and create a better tomorrow.

The Harvard Innovation Lab

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The Harvard Innovation Lab


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