Race car driver Dale Earnhardt once quipped “the winner ain’t the one with the fastest car, it’s the one who refuses to lose”.
It’s a telling metaphor for the state of play in Big Data. A day doesn’t go by where there isn’t some screaming Big Data headline, how it will fundamentally transform healthcare, banking, retail, or any other industry you care to name.
Every C-level exec globally I meet says Big Data is a top priority for their business; they then proceed to rattle off the technology stack they are using, quickly highlight some use cases they are working on and typically throw in a few trendy three-letter acronyms to show me they are on top of it (and look for a reaction from me whether I am buzzword compliant or not).
Frankly, most Big Data projects multinationals talk about feel tired. They usually remind me of old school Business Intelligence (BI) initiatives from a decade ago, but with a sexed up bigger/faster/cheaper angle. In other words, more of the same.
When C-level execs wax poetic about their current Big Data initiatives, I politely ask one question:
Can I meet the Data Sciences team?
Can I meet the Data Sciences team driving the deep thinking around here?
Most Big Data initiatives too often focus on the car and not the driver. What if the driver (the Data Scientists) took precedent over the car? (the technology stack) What does that look like?
Everyone knows about Google Shopping Express right? It’s a little experiment Google is doing in retail; same day delivery from a number of local retailers. They are doing this little pilot of sorts right in their backyard in the heart of Silicon Valley. Whoever does the most successful experiments wins, right? Currently, Google Shopping Express is a free limited trial service, they’ll deliver items to your home same day, often within a few hours. Retailers like Target, Walgreens, Whole Foods, Costco and REI are participating. It’s actually quite a low-tech operation based on an innocuous little chat I had the other day with an unsuspecting Google employee I bumped into in a participating retailer. Place order online, order is routed via a mobile phone (presumably Android!) and a Google employee assigned inside participating retailer picks/packs items then carries them outside to a waiting delivery vehicle. No big infrastructure investment (warehousing, logistics, etc). Like I said, low-tech (for now) Google currently doesn’t even aggregate orders. Place three separate orders on the same day to the same retailer and it’s likely three different delivery vehicles will turn up at your residence. Purchase a $2 pair of nail clippers and Google Shopping Express will deliver them to your home (same day), currently no delivery charge. Really? Yep.
What’s going on?
Google is doing a classic experiment where the key insights are in the data derived from the experiment itself. My head explodes on the volume, velocity, variety and veracity of the data Google are seeing daily.
Google isn’t a search company anymore. Think of it as one giant computer science department. If Google can mathematically understand how an industry really works – in this instance retail – and then abstract that complexity into software, it has the opportunity to capture the most profitable customer segments, and by definition leaving more unprofitable customer segments to traditional incumbents.
As software is eating the world, the information content of every industry is increasing. It’s your math against the disruptors math. Traditional retailers more and more will face disruptors like Google (and others) who are cleverly using Data Science as the next frontier of competition.
Whoever pushes the math envelope the furthest and gets the greatest insights from data, will ultimately win.
So who exactly is your Data Sciences team? How many PhD’s and quant jockeys do you employ exclusively focused on the most vexing challenges and opportunities as defined by the gigabytes (petabytes?) of data flying by your company every second? Where is your multidisciplinary team full of experts in chaos theory, machine learning, R, data analysis and data visualization? The kind of team that sits around at lunch and debates heteroscedasticity, multicollinearity and the nuances of cutting edge Markov models. Has your company recently recruited away any top Data Science talent from Google, Facebook, Netflix or Twitter lately? You ought to consider it being a goal for your company. (Seriously) You’ve undoubtedly read it’s a war for top Data Science talent. It’s a war you need to be in, and win. Drill down on how these Silicon Valley companies do Big Data differently. Ultimately, competitive battles will boil down to ‘your math’ versus ‘their math’, and competition might not be the traditional incumbent competitor you always thought it was.
Oh, and it’s not just retail executives that ought to be alarmed. Many industries are being digitized at an ever-increasing rate; financial services, media, advertising, entertainment, automotive/transport, healthcare and education to name a few.
It’s not about the car, it’s about the driver.
A world-class driver with a very average car will beat a very average driver with a world-class car every time.