We all know the age-old phrase: curiosity killed the cat. But when it comes to data science, curiosity can make your business come alive. In fact, the right mix of curiosity and data science could be your key differentiator in digital transformation.
Let me explain. In this new age of big data and data automation, lots of companies are using data to find patterns, create algorithms, and generally become smarter about their customers and their companies. But the best companies—the ones who will truly move the needle when it comes to customer experience and industry disruption—are the ones who question their data—look at it from all sides—consider what other stories it might be telling. The winning companies will be the ones that give data science its own place—and its own freedom—in the digital business domain.
Curiosity and Data Science: How to Implement it in Your Company
If you’re like many businesses today, you have your data scientists lumped together somewhere under IT or the marketing department, and on the surface, both of those teams make sensical data managers. But what makes more sense is to give data science an umbrella in its own right. Why? When data science is managed by one team, the data gets used for one purpose—to support the marketing team’s goals or to make IT more efficient. The better decision is to allow data science to live on its own, with curious thought leaders who take a wholistic, unbiased look at patterns and keep the overall business goals in mind.
If you’re like many leaders, you might find it hard to imagine data science living on its own. The following are a few ways—and a few reasons—to give it a try.
Sometimes the pattern goes deeper than we realize. Often in data science, we’re quick to jump on the first pattern that seems to fit our business goal. We settle for the simple algorithm, rather than digging deeper, where a whole new treasure trove of customer information could lie. When curiosity and data science comingle, your data scientist is able to look for deeper patterns, anomalies in the data, and bigger reasons behind new customer behavior. After all, in the age of machine learning, hypothesis are generally quick and easy to explore. Your data scientist won’t go down a rabbit hole unnecessarily—but they may unearth some great data by looking outside the predetermined lines.
A skill set is different than a quality trait. Know the difference between a skill and a quality. When you hire a data scientist, don’t just look for someone who is good for mining data and developing code or algorithms. Look for someone who lives for asking questions. Look for the “yeah, but …” person. The person who loves to solve mysteries—and likes to do it efficiently. Look for the person who didn’t just keep a marketing or customer service team running efficiently with data, but who helped take the company to a whole new level.
Culture makes a difference. It’s a weird time in digital transformation. Some companies are pulling far ahead while others are still managing siloes filled with legacy-era technology. It’s easy to understand that one’s company today may not be amenable to giving data scientists so much freedom. The truth is, you need a culture of curiosity if you want curiosity and data science to thrive. It makes no difference if your data scientist finds an amazing new pattern if your leadership won’t hear of it or isn’t willing to act on the insight. Likewise, if they have a hunch for a new theory or pattern but fear reprisal for exploring it if it isn’t correct—they’ll never take chances to find those patterns to begin with. That’s why it’s imperative that data science is given the freedom to access and manipulate data at will—and is given a seat at the table when it comes to finding news to meet company goals.
Not every company is ready for curiosity and data science. In the future, however, it will become a necessity to give data science the space it needs to do the job it was meant to perform—to improve your relationship with your customers and set your company on fire in digital transformation.
The original version of this article was first published on Futurum.
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