Why decision making without data science can be catastrophicBy Jon Taylor on January 8, 2021
“What do you want to be when you grow up?”
It’s a question that we were all asked at some point in our lives, most likely during our school days. Professional footballer, astronaut and Hollywood actor were probably some of the most common responses given in my class, while ‘rich’ also popped up occasionally.
It turns out, though, that these were all wrong answers. That’s because it’s data scientists that now find themselves as some of the most sought-after talent in modern day business, with demand for US data science roles predicted to grow by 31% over the next decade. Once described as ‘the sexiest job in the 21st century,’ those working in the field are playing an integral role in helping some of the world’s leading organizations to extract gold from vast amounts of messy, siloed data. And when I say gold, I mean insight that leads to smarter business decisions.
Ultimately, ignoring data science in today’s rapidly changing business environment is business suicide. The digital economy is here, and data science is an integral component of this transformation that cannot be overlooked.
Data science for business decision making is exactly what it says on the tin – it’s all about putting your company’s data at the heart of your decision making processes. Gone are the days of teams having to rely purely on gut feel and intuition to make the call that they think might be right. The proliferation of data in the everyday business, coming in all shapes and sizes from all kinds of sources, means that you can comb through the numbers, make smart predictions, run multiple scenarios and ensure you’re taking the best possible course of action to achieve a specific business goal.
To put it simply, data needs to be at the center of every business’ key decisions if they’re to succeed in the modern era. Research from the University of Pennsylvania claims that companies with pervasive data access and skills – coupled with a culture that empowers data-driven decision making – are improving enterprise value by up to 5%. But for the majority of companies, this is easier said than done. They have the data, yes – collecting that is the easy part! – but it’s what they do with that data and how they leverage it to its full potential that matters. That’s why data scientists are so crucial.
The role of data science in business decision making
I chatted to three of Peak’s data science team leads to learn more about the role data scientists have to play in smarter business decision making.
From left to right: Peak data science team leads Sorcha Gilroy, Amy Sharif and Tom Hassall.
No more stabs in the dark
“Data scientists can look objectively at what the data is saying and allow businesses to make decisions off that, rather than just relying purely on gut feel or simply doing things how they’ve always been done,” says Sorcha Gilroy.
“Similarly, data scientists can build models that continue to update based on changes in circumstance without manual input. Without this, you’d need to rely on stakeholders changing their mind about how things work.”
More consistent decisions
Tom Hassall added: “Decisions are often made by several different people in different ways in the same business. For example, two different retail merchandisers might make their buying decisions based on completely different factors. A good data scientist can work with the merchandisers to define how buying decisions are made and write software to put more data behind them.
“This makes decisions better (by using the best tactics from both merchandisers), more consistent (both merchandisers will now make the same decision), and easier – the merchandisers will have more data at their fingertips, and won’t have to spend as much time on things like data entry. It’s also possible to show which tactics are working by testing them using data.”
Automate the boring stuff
“I’d also add that data scientists can help to automate decisions, particularly the ones that a team has little to no time to look into themselves,” says Amy Sharif.
“In the merchandising example, if you have thousands of different items/SKUs/products to look through – the chances are you want to spend your time focusing on the ones that really count (e.g. your best sellers), but there’s still a whole tail end of products that still contribute a lot to the bottom line when you add them all together. I also think that working with data scientists helps you to work in a more data-driven way – it helps to prove or disprove certain hypotheses and, as Sorcha has said, it removes bias in decision making.”
However, simply hiring data science talent in your business is not enough; it’s imperative that you’re utilizing their expertise in the most effective way, too. Despite widespread industry agreement that ‘data is the new oil’ – that particular phrase was first coined way back in 2006! – many organizations make the mistake of not using data science resource to full effect. Yes, they bring in a handful of data scientists or work with consultants, but they keep them at arms length when it comes to the decision making process.
Focus on implementation
“One thing I’ve heard from friends working as data scientists in big companies is that they’re often doing some bit of work for a particular department, but that it often doesn’t really get put properly into production – so it floats around as more a bit of insight and doesn’t end up being implemented into a business system,” says Sorcha.
“Probably in this case they’re not close enough to decision making to be able to work with the stakeholders to build something that will actually improve their processes.”
More than just reporting
Amy adds: “Data scientists within an organization tend to produce reports, a demo app, or some kind of proof of concept (POC) – it’s rare when we interview people that they’ve truly put something into production. Sometimes they will produce regular reports to act as ‘information’ to inform a decision, but as this isn’t automated, it doesn’t really save time with the actual process itself.”
“Yeah, for a lot of businesses, data science teams are used to make POCs, test ideas and do data analysis and reporting. They are removed from the decision making process, which means that many of them feel frustrated that they can’t make an impact,” adds Tom.
By introducing data science for business decision making into your organization, you can eliminate the guesswork and make smarter, better and faster decisions. With the expertise of an experienced data science team, armed with best-in-class technology and systems, you can finally uncover the true value of data worth its weight in gold. With the big data analytics market set to surge by a further 30% by 2022, those who fail to execute this effectively this year run the risk of being left in the dust.