Global race in data science decision-making

Global race in data science decision-making

On your mark, get set, go 2017!

While most folks in the US intuitively understand the pull from abroad, it might surprise them to hear this: I’m finding many overseas companies way ahead of their US counterparts when using data science to improve decision making. It’s real. It’s noticeable. Speaking as an American, it’s even a bit scary.

I’ve been guilty of assuming the US always leads technology innovation, data science and analytics. The US has Silicon Valley, Stanford, MIT, Harvard, the National Labs, Amazon, Google, Facebook… and that’s just the tip of the iceberg. There’s no denying US leadership in many areas. Yet while US innovation often defines what’s “new school,” the way most US companies make strategic decisions is hardcore “old school.”race-is-on

Sure, US companies tend to look at data when making strategic decisions. They rely on market research, technical reports, internal expertise and consultants. These inputs are usually treated as considerations, with the final decision coming from an executive’s judgment about which data to care about, which to ignore, and how the pieces fit together. This is how it’s usually done for “strategic” decisions such as what products to invest in, what to divest, what markets to enter, what to restructure or outsource, which partnerships to form. When it comes to strategic decisions, US companies usually view data as subservient to human judgment.

Meanwhile, I’ve noticed an uptick in overseas companies with different relationships between data science and human judgment. Data science has become integral to their decision making processes, even in strategic domains, and even (in some instances) overriding human judgment. And… for a lot of companies it’s creating better outcomes.

My hunch about this shift was recently confirmed, in spades, by a terrific study by PWC. In PWC’s Global Data and Analytics Survey of 2016, more than 2,100 executives were interviewed to discuss how decisions are made, and how this needs to improve by 2020.

The study found three things I’d like to highlight:

  1. China, Korea, Germany, the Netherlands and Asia-Pacific are leading the world in their use of machine algorithms (data science) to guide decisions rather than human judgment. The US also ranked below Nigeria and the UK.
  2. Insurance, industrial products, technology and banking are leading industries in the use of data-science-driven decision making. Asset management – to the horror of anyone who’s entrusted their life savings to an asset manager – ranked dead last.
  3. Nearly two-thirds (61%) of executives wanted their companies to rely on data science more, and intuition less.[i]

In other words, if you’re a US-based industrial products company that mostly relies on human judgment and doesn’t really use data science for strategic decisions, you should be freaking out right now. It’s entirely possible that your foreign rivals make better, faster, smarter decisions every week than your organization is capable of in a year.

It’s 2017… is your business still making decisions like it’s 1917?

Businesses know their strategic decisions need to become more data-driven, fast, sophisticated and reliable. That’s why the world’s best companies are in an arms race for the best data science. It’s redefining how executives, companies, industries and even countries make decisions. Whether you’re in the US or anywhere else, the starting pistol went off a while ago. Make sure you’re in the race.

 

[i]PWC’s Global Data and Analytics Survey 2016, http://www.pwc.com/us/en/advisory-services/data-possibilities/big-decision-survey.html

This Post Has 3 Comments

  1. The challenge of course is reinforcement. If you believe you are the best it reinforces your confidence in your own intuition. It got you there (supposedly) but if you are looking for an edge then making better decisions and eliminating cognitive bias is a no-brainer.

  2. Interesting article. do you have data that ties these changes to better business results? it reads a little like the old Japanese businesses can do no wrong articles from the 80s.

  3. Malaysia CPG manufacturing we use NLP and big data to automate sales forecasts and plan manufacturing capacity. Totally automated. No planners anymore as of last year and are 26% more efficient plus lower cost since no planners.

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