Why ignoring data science is business suicide

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Earlier this month, Business Insider reported that retail giant Walmart has more than 1,500 data scientists and 50,000 software engineers—and it is seeking to hire even more to advance its artificial intelligence technology. Given that Walmart is primarily a brick-and-mortar retail outlet, this news tells us much about the role data science and AI are playing in every sector.

As digital transformation fully takes over virtually every industry under the sun, leading companies all share one specific thing in common: they harness the power of data. Data utilization is no longer a luxury reserved for a handful of elite companies. Today, no matter the industry or size of your business, leveraging data is necessary to succeed.

Why data science is important

The value of data is finally being recognized in every aspect of a business. Data gives businesses the ability to make informed decisions, backed up by volumes of research. In turn, these decisions lead to a higher return on investment and boost a business’s standing. Gartner predicts that 90 percent of all corporate strategies will include data and information as valuable assets, and analytics as a core competency by 2022.

Today, the biggest tech companies in the world are already using data to better understand their customer base and aim their goals even higher. For instance, consider Google’s foray into artificial intelligence. Based on the premise that computers should adapt to human lifestyles, Google introduced automated Gmail responses as well as YouTube personal recommendations. All this is based on the vast amount of data Google collects, which includes preferred email responses and past viewing habits. Leveraging the power of data has allowed Google to see higher usage rates when it comes to Gmail, as well as an increase of 60 minutes per day per year in YouTube video viewership.

While Google is a tech giant and has access to massive databases, the power of leveraging data in any business is undeniable. With this in mind, it’s essential to integrate data science into your business sooner rather than later.

How data is used in businesses

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The power of data can be used in a variety of ways to benefit your business. For instance, insights gleaned from data can help in determining the best supply chain management practices. This includes everything from estimating the amount of inventory you might need and the best delivery methods to figuring out which products to recommend to your customers to increase the likelihood of a cross-sell.

In terms of sales, data can be one of the most valuable tools at your disposal. With multiple different channels of consumption, creating a customer journey map is essential to increase sales. Harnessing the power of data can make the research aspect of customer journey mapping much more efficient, and result in more accurate predictions. Data science can draw upon volumes of information so that you can effectively “get onto your customer’s head,” and in turn, cater to their needs.

Data is also useful in performing risk analysis. For those in the finance and banking sector, this capacity has proven to be very beneficial. From analyzing threats to predicting outcomes while taking current events into consideration, data science allows you to stay up to speed with any relevant developments and minimize risk. These are just a few of the ways data is being used in various aspects of business across industries. In reality, the possibilities are endless. Every day, businesses are coming up with creative ways to leverage data and improve their chances of success.

How to succeed with data

In an article about why companies need a data strategy, Dwayne Gefferie talks about the four steps any business should implement if they want to succeed with data. For starters, Gefferie recommends assigning or hiring a chief data officer. The responsibilities of the chief data officer should be data-centric, and the officer’s role will differ significantly from the chief information technology officer. The chief data officer should be one who is able to work with various departments to show them how to use data to their benefit, and generally adopt a data-driven way of thinking.

Secondly, Gefferie suggests aligning the business’s mission and goals with the “data value chain.” To quote Gefferie, “Every company goes through different stages in their Lifetime-Cycle, when transforming an organization into a data-driven one, the aspirations on the data value chain need to align with the organization’s primary needs.”

Thirdly, once the key aspirations have been established, a relevant overall strategy should be developed. The chief data officer should create a strategy that includes company-wide data governance and usage, as well as sub-strategies for specific departments.

Finally, to achieve the goals set out, the appropriate talent should be hired. Depending on the strategy, a business might need to hire professionals with a wide range of data skills. These include the ability to apply the correct data tools, identify key insights and create data-driven visualizations and reports. Rather than simply hiring data professionals to fill vacancies, Gefferie believes it is more prudent to first develop a data strategy that promotes business goals. Using this four-step approach, companies are more likely to succeed with data.

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. After all, to quote experts at Gartner, “Data and analytics will become the centerpiece of enterprise strategy, focus and investment.”


  1. Hey. I liked your article. A lot of useful information. Designing, developing and implementing data science solutions is an expensive pleasure. Therefore, even before the start of the project, it is worth assessing whether the investment is able to pay off. It is also necessary to weigh whether you really need all this machine learning. Machine learning is well suited for automating pipelined, routine processes, which in doing so lead to the accumulation of a significant amount of data. And keep in mind that the likelihood of misunderstanding between the customer and the contractor in data science projects is higher than when developing traditional systems. Good luck!

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