Over the past decade, the use of data science in business has grown by leaps and bounds. Respected analysts at companies like Google, Yahoo and IBM predict that data scientist will be the hottest job in the coming decade. Clearly, data science is the next big thing for businesses looking to stay one step ahead of the competition. Unfortunately, there’s quite a bit of confusion regarding the definition of data science and how it differs from business analytics. As you’ll see, understanding data science and its many applications will be the key to survival for many businesses moving forward.
What Is Data Science, Anyway?
While the use of data science in business has been around for many years, the contemporary definition of the term is still a bit vague. One way to describe data science is a methodical approach to data analysis that seeks to coax insights from Big Data. The idea is to let data speak for itself rather than coming up with a thesis and testing it out. This approach to data analysis can yield impressive results since it largely eliminates the problem of confirmation bias that can lead traditional business analysts down the wrong paths.
How Data Science Works
In a nutshell, data science is kind of like statistical analysis on steroids. Data scientists collect information, run it through various algorithms and use the results to make business strategy recommendations. Data scientists and data science applications often rely on so-called “machine learning” to guide their efforts. Programs written in languages like Python, R, SAS and SQL manipulate databases like Hadoop and Teradata to produce the intelligence that businesses of all sizes need to streamline their operations.
Why Data Science Is Exploding
When you get right down to it, data science is basically just a fancy term for statistical analysis. Modern statistical analysis relies on an unbelievable amount of data to produce relevant insights. Thanks to the proliferation of mobile devices that capture all sorts of data related to consumer behavior, data scientists have a lot of information to work with. So-called Big Data is the reason why using data science in business to streamline companies is such a hot draw nowadays. As data collection increases exponentially, the power and accuracy of data science will increase with it.
How Businesses Are Using Data Science
Whether you’re a solo entrepreneur or a massive corporation, success in business comes down to serving the customer better than the competition. The use of data science in business allows outfits of all sizes to refine everything from web search to product recommendations to delivery logistics and much, much more. By analyzing data gathered from consumer behavior, businesses can tweak their products and service offerings to better appeal to key demographics. Ultimately, an intelligent application of data science to a company’s business model can be the difference between consistent growth and bankruptcy.
The Benefits of Data Science for Businesses
Ultimately, the best way to use data science to improve a business depends on the company in question. However, there are a few universal benefits of using data science in business that apply to all enterprises including:
Customer Acquisition and Retention
If you’ve run any kind of business for longer than a week, you know how hard it can be to turn prospective buyers into loyal customers. Data science allows businesses to devise the best online and offline tactics for attracting consumers. Algorithms created by data scientists can quickly fine-tune the marketing efforts of any company and allow them to avoid costly and ineffective advertising blunders. What’s more, the kind of in-depth analysis that’s made possible by data science enables companies to reduce customer turn-over or “churn” that negatively impacts the bottom line.
Superior Customer Experiences
Another highly compelling benefit of using data science in business is the ability to improve the buying experience for customers. Many businesses use data science techniques to up-sell consumers when they’re shopping online. If you can figure out what consumers want before they even know they want it, you’ll make their lives easier and boost revenue at the same time. Data science can also point businesses in the right direction when it comes to offering the right buying incentives at the right times.
Greater ROI Across the Board
Last but not least, data science can help a business to reduce its overhead and thereby boost profits in the process. For instance, data science is often leveraged to predict peaks and valleys in demand and adjust inventory levels accordingly. Many companies use the insights they’ve acquired from data analysis to automate low-level tasks and optimize their shipping procedures. For businesses in low-margin industries, every penny saved adds up quickly and can deliver a significant edge over competitors that aren’t using Big Data analysis.
Data Science Success Stories in the Business World
Nowadays, nearly every major corporation in the world is using data science to improve their business model. For instance, AirBnB has made extensive use of data science from the start to optimize their search capabilities and hiring policies. Facebook is famous for using data science to manipulate user experiences and customize the trending news that they see in their feeds. Pharmaceutical giant Bristol-Myers Squibb has been using data science to speed up their clinical trials for years.
The Future of Data Science in Business
In the coming years, you can expect to see more and more companies relying on data science rather than business analysis to guide their strategies and tactics. You’ll also see more SMBs investing an increasing amount of money into data science solutions as they become more commonplace and affordable. Businesses of all sizes will need to adapt to increasingly competitive local and global markets if they wish to survive and thrive. While it’s impossible to predict where the rise of Big Data analysis will lead, it’s safe to say that the use of data science in business is here to stay.
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