Edward Hadley on February 3, 2016
“Everybody has access to the same information. We just know how to analyze it better.”
So says Bobby Axelrod, hedge fund manager and lead character in the new Showtime series Billions.
Axelrod’s comments embody what has been the secret sauce of hedge funds for decades: the ability to aggregate vast amounts of information and make better decisions quicker than the rest of the market using proprietary algorithms. In fact, I would go so far as to say that the best hedge funds aren’t in the investment business. They’re in the algorithm business.
Your business should be too—if it wants to survive and thrive in the digital era.
We’ve talked in the past about how every company must think and act like a software company. That is still true. New business models and competitive differentiation are being built on the back of custom software that’s delivered rapidly and changed constantly in response to new business needs and market requirements.
Increasingly, though, the heart of this software is algorithms. Just a few years ago, everyone was saying that data is a company’s greatest asset. What’s more important today is how businesses redefine themselves by turning their data, processes and innate knowledge of their markets into proprietary algorithms .
Uber is the easy example. They’re not in the taxi/limo business; they connect people who need rides with drivers willing to drive them. Their business algorithm is mobile mapping, connecting, billing, rating, rinse and repeat.
But, we’re bored with that analogy. So let’s talk light bulbs. Let’s say you manufacture boring, easy to commoditize light bulbs for the horticultural industry. Low-cost, offshore competition is eating your lunch.
One of our customers faced this very real threat, and chose to rethink their business. They realized they were not a light bulb manufacturer. They enable greenhouses to grow better plants, faster. Their business algorithm became greenhouse optimization as a service.
With Mendix, they created an application to collect sensor data on light, temperature, soil, weather and more. Machine learning services and the resulting predictions optimize the photosynthesis of the plants along with energy consumption and greenhouse maintenance. And yes, our customer sells plenty of light bulbs too. The app predicts the life expectancy of each bulb and proactively notifies maintenance workers to replace it before any disruption occurs.
Back in 2010, Google CEO Eric Schmidt famously said, “Every two days, we create as much information as we did from the dawn of civilization up until 2003.”
While that’s a LOT of information, we are only just getting starting. The emerging Internet of Things is accelerating the data deluge, as billions and billions of devices and sensors come online. Cars. Washing machines. Greenhouses. Soap dispensers. Jet engines. Anything you can possibly think of is connected and generating a steady stream of data. The imperative is to extract meaning from this data and use your knowledge and experience to build new business models and revenue streams.
The challenge, however, is that it’s not humanly possible to interpret all of this data. Instead, enterprises are increasingly adopting machine-learning: the science of getting computers to act without being explicitly programmed. Machine learning services enable developers to create machine-learning models using visualization tools and wizards. These models, or algorithms, can then be leveraged to reveal unexpected correlations and new insights that improve existing services or create entirely new ones.
That’s just the first step. What’s equally important is how businesses consume these algorithms and make them available at the right time and through the right channel to their employees, partners and customers. For that reason, we’re seeing a rise of ‘smart apps’ that combine contextual awareness with algorithms to deliver intelligent, predictive recommendations. Instead of the user telling the app what to do, smart apps will tell the user what to do when, in order to optimize business outcomes.
The intelligent greenhouse app discussed above is just one of many examples. For instance, imagine an iPad app able to identify a patient based on a sensor in their hospital wristband, and then instantly synthesize a wealth of patient data (e.g. symptoms, vitals, medical history, allergies, etc.) to offer treatment suggestions. Or imagine a retail environment where an app identifies high-value customers through in-store beacons and provides associates with tailored product recommendations based on the customer’s purchase history and propensity to buy models.
Thus, the next evolution of becoming a software company is leveraging machine learning and predictive analytics in conjunction with rapid application development to deliver smart apps faster and more flexibly. Bringing it back to Showtime’s Billions, success hinges upon your ability to analyze data like a hedge fund; at the same time, it requires the ability to rapidly build and iterate software like a tech company
The show may be fiction but this world is fast becoming a reality.
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