How to Use Data Science to Improve Your Business Operations

The chances are you’ve already read an article today about how AI can improve your business. The not-inconsiderable hype around AI has led to many bold claims, not least around improving business operational efficiency. A recent report by MIT Sloan Management Review, in association with Boston Consulting Group, has actually defined this hype gap:

“Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage. But only about one in five companies has incorporated AI in some offerings or processes.”

– “Reshaping Business With Artificial Intelligence” – MIT Sloan Review 2017

The issue is that this hype is obscuring the considerable benefits that data science can bring to your business operations. Without getting sucked into the arguments around terminology, we are currently in the age of “Narrow” or “Weak” AI, which means humans are still firmly involved in coding the intelligence in AI. This means that the opportunities to sustain a competitive advantage are available, but with the correct application of data scientists, rather than generic “AI”, with your business data. Specifically, the adoption of data science can be used to significantly enhance internal operational practices within a business, as well as improve customer knowledge and drive sales. However, for many business leaders, investment in this approach can seem complex and confusing.

Data Science and Risk Management

Take risk management, for example. Operational and business challenges around risk tend to use large volumes of data to predict a small number of high-risk events, e.g. under what circumstances a jet engine will fail, a drug will exhibit adverse effects, an investment will go sour. They cannot afford failed experiments. They need to know the likelihood of one event leading to another. Events on which serious money, and in some cases lives depend.

AI, as it currently stands, is not advanced enough to learn the complex correlations between events to determine risk. You need strong data skills, the industry knowledge to understand what the data means, and scientifically robust test patterns and models. If you really want to benefit from AI, you need a solution suited to your specific problem, and then have the right people in place to train it correctly.

How Businesses Can Take Action on AI

If a business is to fully benefit from Narrow AI integration, a strategy must be developed to meet the specific needs of the business. We’ve written about this before, but essentially it comes down to people or platforms.

One option is to buy an AI solution – often referred to as the “Black Box” solution – which will take your data and identify patterns within it. However, for most businesses, the investment in this sort of technology is the equivalent of using a sledgehammer to crack a nut. Most business operations will be too complex to automate, and the patterns or recommendations that pop out the other end will not be presented in the context of business insight – they usually require a lot more work to understand if they are relevant, and what further action needs to be taken.

Alternatively, businesses may benefit more quickly and significantly by investing in data science expertise – either in-house data scientists or external consultancies – and building the tools themselves. A company that tailors their tech to their own needs is able to build bespoke “AI tools” that acknowledge specific data and context for the business, rather than being limited by a one-size-fits-all solution. You’ll be able to tackle individual strategic issues on a more cost-effective per-project basis. Plus, you’ll have resources in-house to not only develop the tech but recommend potential business applications of the insight. And, you get to keep control of all of your data.

Whether data science is employed to increase profit margins and sales, improve the security of financial transactions, heighten data protection efforts, minimise human employee work hours, generate accurate forecasts, optimise the analysis of historical records or develop more reliable and effective future business plans; companies in every sector have the opportunity to achieve substantial returns and competitive advantage on even the smallest data science project.

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