Is data science important? It’s a term that’s talked about a lot but often misunderstood. Because it’s a buzzword it’s easy to dismiss; but data science is important. Behind the term lies very specific set of activities – and skills – that businesses can leverage to their advantage. Data science allows businesses to use the data at their disposal, whether that’s customer data, financial data or otherwise, in an intelligent manner. It’s results should be a key driver of growth.
However, although it’s not wrong to see data science as a real game changer for business, that doesn’t mean it’s easy to do well.
In fact, it’s pretty easy to do data science badly. A number of reports suggest that a large proportion of analytics projects fail to deliver results. That means a huge number of organizations are doing data science wrong. Key to these failures is a misunderstanding of how to properly utilize data science. You see it so many times – buzzwords like data science are often like hammers. They make all your problems look like nails. And not properly understanding the business problems you’re trying to solve is where things go wrong.
What is data science?
But what is data science exactly? Quite simply, it’s about using data to solve problems. The scope of these problems is huge. Here are a few ways data science can be used:
- Improving customer retention by finding out what the triggers of churn might be
- Improving internal product development processes by looking at points where faults are most likely to happen
- Targeting customers with the right sales messages at the right time
- Informing product development by looking at how people use your products
- Analyzing customer sentiment on social media
- Financial modeling
As you can see data science is a field that can impact every department. From marketing to product management to finance, data science isn’t just a buzzword, it’s a shift in mindset about how we work.
Data science is about solving business problems
To anyone still asking is data science important, the answer is actually quite straightforward. It’s important because it solves business problems. Once you – and management – recognise that fact, you’re on the right track. Too often businesses want machine learning, big data projects without thinking about what they’re really trying to do. If you want your data scientists to be successful, present them with the problems – let them create the solutions. They won’t want to be told to simply build a machine learning project. It’s crucial to know what the end goal is.
Peter Drucker once said “in God we trust… everyone else must bring data”. But data science didn’t really exist then – if it did it could be much simpler: trust your data scientists.