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Data science can give you the insights you need to create new services and transform your business. But, if you don’t understand your data, you risk making bad business decisions or worse, automating them for years to come. Here’s a few things you should know to get started.
By now you’ve probably heard somebody at an industry conference proclaim that data is the new oil.
As the digital economy begins to take shape with people, organisations and machines increasingly interconnected, a rich new stream of data is being generated. Sticking with the automotive theme, the oil (in this case data) is only valuable if your business has a reliable engine.
That’s where data science comes into play. Skilled professionals are needed to put this data to work. When done properly it creates new products and services, transforming business models and entire industries.
The term data scientist was coined by DJ Patil and Jeff Hammerbacher – then at LinkedIn and Facebook, respectively – in 2008. A decade later there’s still a widespread lack of basic understanding among business executives. With the age of automation upon us, there is an increasing pressure on senior executives to understand how to make sense of the data which is fuelling emerging technologies such as machine learning and artificial intelligence. If executives don’t understand how these technologies work, there is a risk of making erroneous business decisions or worse, automating erroneous decisions for years to come. To bridge this knowledge gap, we need to get back to basics. Here’s why:
1. Garbage in, garbage out
This is a commonly used term in computer science. Put another way, the quality of output is determined by the quality of the input. So, inputting inaccurate data is likely to shape poor business decisions. In 1999, NASA suffered a major setback when a miscalculation led to a $US327 million loss. Due to complications stemming from human error, the Mars Orbiter encountered Mars at a lower than expected altitude and disintegrated due to atmospheric stresses. This may be an extreme example but it’s a cautionary tale for businesses increasingly reliant on data-driven insights.
2. Size doesn’t matter
Bigger isn’t always better. How your business uses data is much more important than how much it collects. Even if you have advanced infrastructure and produce mountains of data, it’s useless unless the right questions are being asked. Asking questions is at the core of what scientists do. It’s also important to recognise that data isn’t always usable in its current state. Preparing it is often a tedious process. Data scientists dislike massaging data rather than mining or modelling it.
3. Correlation and causation
Not all business executives are equipped with a background in statistics, so it’s tempting to assume that one correlated variable causes another. Getting bogged down in an endless search for correlation and causation will only prevent your business from reaping the benefits of data science.
In today’s digital age, data science matters. If harnessed correctly, it can power incredible business value. However, executives must acknowledge that data science is an art form and, like any art, it takes time to perfect. Many big data projects will fail to deliver against expectations but starting out with clear business objectives will improve your chance of success. There’s no time like the present. Know what problem you’re trying to solve and stay focused.
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