Is Your Data Sleeping?
I just returned after delivering a cool keynote on Data Science in Athens, Greece to many IT and Business Managers. It was great to have interacted personally with both engineers and managers of companies across Europe
One question a manager asked me in private: “We are a large company and have hundreds of thousands of clients across the globe. There is so much data but how can I derive insights and eventually make some meaningful projects that can help drive sales and improve our margins?”
I was thinking in my head, “Wow, only if there was a good direction setting from leadership, some simple data science skills and one MVP (minimum viable project), I think they could drive so much value already!”
We discussed how he could get started on day 1 and exchanged visiting cards.
AI Economy Will See Cannibalization Within Data Players Too
Data is being harvested by companies right in front of your eyes.
You watch the news and you go about your work.
Chances are that you're heading to work at an employer that may not exist in the next 5 years.
You may be thinking to yourself, “Well, I work for a hotel booking company, and we're a company doing awesome stuff with data. No one can touch us”
You're right, this doesn't happen overnight but being complacent could be fatal to your company's future survivability.
But it is coming to your data company too!
What Do I Mean By That?
Don't worry I won't give your Harvard or some management consulting lectures rife with complex jargon and frameworks on how reverse innovation, self-cannibalisation will help you.
Instead, let's just watch some plain facts — and some I had also shared as well in my keynote.
Google has silently rolled out its Google Hotel Search service and sure enough you've seen Google advising you -- probably more efficiently than your local booking website on your travel plan such as flight, hotel and more.
For instance, in planning my upcoming trip to Sao Paolo, I just search and have this complete advice at my fingertips:
Okay, so this is not surprising because you're thinking if you're a local booking.com or other company: 'We have even more sophisticated interface than this, and we serve x amount of clients and x millions of this and y millions of that per second!”
But you know what you're missing?
The AI Multiplier!
Machine Learning Data — Google has 100x or perhaps 1000x more than you
My last post was about how AI is transforming the industries at scale. The widely popular 1-minute video was reported in the press where News.com reported an article as “Gone in 60 seconds!“
This is all possible with Google, Amazon and other companies are increasingly racing to create their leadership position with uber-personalized services because they own 100 times or perhaps even 1000 times more data that you have.
Still Not Convinced?
Look at Tesla.
It has already collected data from its vehicles for autonomous driving of over 1 billion miles, according to Teslanomics.co. Now, compare that to the nearest competitor Waymo, which only has reported 13 million autopilot miles so far.
So, Tesla will not only speed past all other traditional companies like Volkswagen, Daimler but also it tech competitors as it will continue to possess more data as more cars and more new models will go into the market.
With already 500K cars on the road today, and as Elon Musk added yesterday during his Model Y unveiling that they may have 1M cars by 2021, this data collection will grow at an enormous rate to which only Tesla will have access to, develop great services and continue to wow its customers while everyone -- even fast movers, playing catch up.
This is the same way manufacturers, retailers, telecom and other players need to look at.
How to Make My Data Work for Me?
The only path to such massive growth is to live and breathe data inside your company.
Getting mature leadership that actively supports and stands for this data culture will be able to sustain its AI dream. Measuring up objectively where you stand and what you need to do will help you define an actionable case for AI transformation, else you will at best succeed only in limited silos inside your company.
Having a false belief that you're an AI company will not only be untrue but ultimately fatal!
Giving your AI team a proper governance structure and a departmental or structural home will only accelerate the feeling of belongingness towards your company goals for these data scientists!
Running data-driven, machine learning and statistical projects is the only way to actually test whether your maturity, as well as skills, are at par with the industry benchmark. This is also a great opportunity to test drive the cultural aspects of your company. A lot of your own managers are posting their roles on LinkedIn as “Global Head of AI” but are they really doing anything? To help these managers also feel part of this revolution, adopt an inclusive approach. This will help them succeed inside your own company, which is what you actually want.
We have been seeing services such as Google Jobs being released a few years ago and a lot of other services that are slowly coming together to create a unified ecosystem where data across all sorts of customer journey will be harnessed at great speed.
Don't lose hope as you have tons of data, you just need to harvest it the same way as these companies.
Don't pass around the buck either as there may be nobody to take it over from you anyways. Hone these talented data scientists, so they become the powerful AI locomotive you desire for your firm.
Get started today!
Tarry Singh, columnist, is CEO, Founder and AI Neuroscience Researcher of AI startup https://deepkapha.ai. deepkapha.ai focuses on the following three pillars: 1) breakthrough AI research that intends to knit the world of neuroscience, models and frameworks around deep learning for the future, 2) build AI Solutions for corporate customers and train engineering teams to holistically build AI solutions with hands-on, market-relevant advanced AI projects, and 3) AI Philanthropy initiative “givebackAI" to train the world that cannot afford an expensive education. We have already trained over 10.000 learners worldwide and expect to have trained* about 50.000 by mid 2019.