Data science is the backbone of modern business. When you look at any of the most successful companies in the world today, you can see example after example of data science implemented to its fullest extent.
But what if you’re not a multi-billion-dollar company—can you still utilize the data science to get ahead? We’re happy to inform you that you can use data science and change the game. Read on to discover important implementations of data science in business.
What Is Data Science?
Data science is a lot like all other types of science—scientists create datasets to form hypotheses and test them through experimentation. In addition, scientists perform tests to confirm the quality of their data and put it into a cohesive structure for later analysis.
Data science uses more than scientific principles—it also uses math. Algorithms are essential in a data scientist’s toolkit to facilitate proper collection and analysis, especially with large datasets.
The best algorithms can notice patterns that human eyes wouldn’t usually see while also making data gathering and analyzing more efficient.
Data Science vs. Data Analytics
There are a lot of similarities and connections between data science and data analytics. In truth, you shouldn’t have one without the other. To make a long story short, data scientists learn new ways to model and interpret data using algorithms and predictions. On the other hand, analysts view datasets to find trends that help businesses make more calculated decisions.
1. Customer Insights
Customer insights can tell you nearly everything about the people buying your products or services. You can learn the demographics, habits, and even the goals of your patrons so that you can more effectively market to them in the future.
Perhaps your algorithms highlight that a person consistently visits your online store, puts an item in their cart, and leaves it. When your algorithms catch this, you might have your system send out an email to remind that customer about the item in their cart or even to offer a small incentive to come back.
When that tactic proves successful, you’ve learned something about your customer: they wanted the item the whole time, but something held them back from making the initial purchase. With a little more work in customer insights, perhaps you can determine what kept them from pulling the trigger so you can address that inadequacy for the future.
2. Produce Better Goods and Services
You may think your product is as good as it can get, but customer reviews often tell you otherwise. Of course, your data science platform may need to sift through quite a few reviews before it finds genuinely helpful suggestions, but there is no shame in adapting your products to suit your customers’ needs.
The companies that consistently improve their goods in a free market are often the most successful.
3. Effectively Manage Your Business
Data science is about more than customer-facing factors. You can also leverage data science to perfect your internal management structures. Just as your customers are rich with information about their spending habits, your business is rich with information about its processes.
With the right algorithms, your data scientists can sniff out patterns that indicate what is working and what needs work.
4. Predict the Future
While data science can’t actually predict the future, it can paint an accurate picture of the outcomes of certain decisions. Even without data science, businesses have spent years trying to perfect the art of prediction. When will markets crash, and when will they boom? Is the Internet bubble ever-expanding, or will it pop?
With data science, those predictions can rely more heavily on hard data than ever before. While gut feelings and intuition will always be a part of the business world, data science can hone instinct to drastically reduce risk.
5. Assess and Double-Check Your Decisions
Once you make a decision, the process isn’t over. It’s essential to assess your choices and track how the decisions affect your company’s performance. Even when a choice leads to a negative repercussion, it’s not all bad. You’ll gain valuable information, and your data science platform will have more evidence to understand what went wrong.
6. Detect Anomalies
Datasets are imperfect. When dealing with petabytes of information, it’s impossible to ensure that all of it is flawless. However, data science is excellent at scouring datasets to find anomalies and isolate them.
Fraud detection, for example, is a process made far easier through the implementation of data science. When fraudulent activity occurs, it’s essential to address the issue quickly—data science platforms can detect problems within minutes and alert you so you can take the proper steps.
How Data Science Works in Practice
To analyze the benefits of data science in a real-world environment, all you have to do is look at Walmart. While Walmart may have more pieces of data to work with than your company, what they do with the data may well be the same.
For instance, if you have a physical location, especially one with checkout lanes, you may be able to take a page out of Walmart’s book. Walmart uses its data analytics platform to keep track of the times of day when checkout lanes become overcrowded. This allows the platform to suggest staffing more cashiers during certain hours on certain days.
In this way, a simple staffing change can reduce traffic at the front of your store while also providing a better experience for your customers.
Additionally, Walmart uses data to keep track of the purchasing patterns of its store patrons. Perhaps pizza ingredients sell better on Saturdays, while pickles sell best on Wednesdays. This information can ensure enough items are in stock at the correct times so you don’t miss out on sales.
Now that you know these important implementations of data science in business and real-life examples take your company’s data game to the next level with your own data analytics platform. Training your people and getting the most out of the platform will take some time, but the investment is worth it.