Companies across a wide range of business sectors collect myriads of data in the modern day. Yet, some of it is still underutilized or not managed properly. While more and more organizations are embracing data-driven strategies, there is still a lot of room for improvement.
“Organizations often apply data-driven approaches—from predictive systems to AI-driven automation—sporadically throughout the organization, leaving value on the table and creating inefficiencies. Many business problems still get solved through traditional approaches and take months or years to resolve.”
— McKinsey
With so much potential still hidden, companies are turning to data science to uncover insights within the digital information they already collect. Naturally, this fuels market growth of the technology. In fact, the data science platform market is set to reach $322.9 billion in 2026.
However, some might still be plagued with the question, what exactly is data science used for? How can it help my specific business? Well, that’s what we’ll cover today. So, let’s get right into it.
The Value of Data Science in Business
Like we said, there’s no shortage of data in the world of business today, which is why knowing how to leverage it has become a top priority for company leaders. Data science helps you do just that. It combines the processes of building, cleaning, and structuring datasets to analyze the digital information within them and extract meaningful insights.
As such, the technology allows organizations to gain a multitude of benefits. From streamlining internal processes to a better customer understanding, there are plenty of rewards to reap from data science-powered intelligent solutions.
If you’d like to know some examples of what data science can deliver, here are the top ones:
- Predictions
- Recommendations
- Pattern and anomaly detection
- Actionable insights
- Automation
- Segmentation
As you can imagine, these capabilities can be applied to all kinds of business functions. After all, nearly every department of an organization deals with data that could be leveraged for better strategic insights.
So, while the use cases of data science might be similar across business areas, there are some that are specific to certain industries. That’s what we’ll talk about next.
BI for Business
Find out the secrets of how business intelligence boosts operations and what BI tools and practices drive data analysis.
Top Data Science Use Cases Across Industries
The software development evolution has led to breakthroughs in a variety of business sectors, and data science is often at the core of these advances.
Healthcare
The healthcare sector deals with a lot of data in various formats. Medical images, laboratory results, information from hospital management systems, insights from medical devices, all have to be explored and analyzed.
As such, managing that data is not only beneficial for process optimization and revenue growth but can actually improve diagnosis and quite literally save people’s lives.
For instance, data mining tools combined with artificial intelligence and machine learning algorithms can detect anomalies within test results at early stages and predict the likelihood of breast cancer, diabetes, or other adverse medical conditions. Thus, allowing doctors to take swift preventative action and deliver better patient care.
Learn more about AI in Healthcare
Another example of data science being used in the medical sector is within genetics and genomics. As you may know, the goal of this area is to gain a better understanding of DNA impact on the wellbeing of patients.
Hence, it’s imperative for a variety of data to be analyzed so that relevant insights can be extracted for the best treatment plan to be created. Speedy identification of genetic predisposition to certain types of disease can be game-changing for the industry.
All of these are just some examples of data science use cases that are gaining popularity in healthcare. Clinical decision-making, remote patient monitoring, and even drug development can all benefit from these tools as well.
Read up on the development of a Remote Patient Monitoring Solution for Urology
Finance and Banking
The financial services industry has an extensive range of processes that require immense time and effort. For instance, analyzing stock market trends, using high frequency and algorithmic trading, as well as assessing customer risk.
Take a look at Intelligent Trading Platform Development Process
As you can imagine, this makes the finance field prime for data science implementation, and it’s no surprise that there’s a multitude of use cases in this sector for us to discuss. For starters, data science tools can help investors get real-time stock market insights. This is crucial in an industry where every minute, if not second, things change as stock prices go up or down.
Having up-to-date information allows for smarter decision-making and thus for a reduction in errors. Not to mention, by incorporating predictive analytics, finance teams can even gain access to stock price forecasts that are based on the latest information.
Discover more examples of Financial Intelligence Tools
In banking, one of the top data science use cases lies within loan risk assessment. To determine whether a customer can be given a particular loan, bank employees have to first figure out if the person in question will be able to pay it back.
To do so, financial experts take into account the customer’s credit history, existing assets, and the like. As you can imagine, this can take quite a bit of time.
With data science, you can merge digital information from multiple sources swiftly to get clear insights via intelligent dashboards. Hence, speeding up the entire process and getting back to your clients in a more efficient manner.
Insurance
Insurtech is nothing new for companies operating within the insurance sector. Every day, organizations have to sift through customer information during quoting, underwriting, or claims processing. With so much relying on top-notch analysis, it’s easy to see why data science use cases are so prevalent in insurance.
Take a look at how we built Onboarding and Underwriting Software
The first data science example worth mentioning is in improving the underwriting process. With the help of predictive analytics in particular, insurers can quickly evaluate risk that comes from providing insurance coverage to a certain customer. Thus, helping personalize the insurance offer while protecting your business from potentially significant losses.
Additionally, data science can prove invaluable in helping insurers prevent fraudulent activity. As you know, insurance fraud yields enormous losses for companies within the sector every year. As such, reducing the likelihood of fulfilling false claims is a top priority for insurance brokers and agents.
Data science tools use statistical models and predictive modeling techniques to monitor current activity and compare it against previous fraudulent events. As a result, these solutions identify similarities between the two and can alert employees if something suspicious is going on.
As you can see, there are several data science use cases in this sector, and they are just as applicable in automotive insurance as in property, life, or health. So, no matter which area of the industry you specialize in, think about implementing data science software to help improve your organizational performance.
Manufacturing
The modern manufacturing industry is increasingly embracing automation, robotics, and the use of data science tools. Industrial Internet of Things (IIoT) has ushered in a new era of performance management, streamlined operations, and enhanced efficiency. Data science and other intelligent tools are at the heart of all this.
Find out How to Apply Business Intelligence in Manufacturing
In smart manufacturing automation, one of the major data science use cases lies in asset management. As you know, the machinery manufacturers own and operate is immensely valuable. It’s what produces the products, which means that any untimely failure can lead to significant financial losses.
However, by combining IoT devices with data science technology, assets can be monitored in real-time to provide status updates on their health, utilization, and availability. Moreover, intelligent algorithms can learn from past machine failures and identify the warning signs so that you can schedule preventative maintenance. Thus, helping avoid downtime altogether.
Learn more about IoT in Manufacturing
Similarly, you can also leverage computer vision with thorough data analytics to detect bottlenecks in production processes and even identify product defects. As you can imagine, this can speed up your operations while also keeping customers happy since they can always be sure that with you — they’ll be getting a top-notch product.
Supply Chain and Logistics
Data science use cases are also highly prevalent in the areas of supply chain, logistics, and transportation. Just like all the other industry examples we’ve discussed above, these too are filled with a multitude of daily moving parts that require monitoring and insights extraction.
For instance, inventory management is at the core of how well a supply chain functions. Yet, to ensure all the processes run without a hitch, it’s imperative to keep track of stock levels on a regular basis.
Once again, connected devices and data science technology can come to the rescue and not only deliver regular inventory reports but also forecast which items are about to run out. As long as you’re alerted in advance, everything can be replenished in due time to avoid unnecessary delays.
In terms of logistics and transportation, data analysis can be game-changing in two ways. First, by gathering data about traffic trends and previous journeys, the technology can deliver ways to optimize routes to help decrease fuel usage and increase efficiency.
Uncover how IIoT Improves Sustainability in Business
Secondly, with the aid of data science, you can be sure that transporting a product that requires strict condition monitoring won’t be problematic. This is due to the fact that information from vehicles and fleets can be directly transported to your systems and surveilled in real-time. Moreover, speedy alerts can be sent out if ever things go awry so that your staff and the driver can be notified and appropriate action taken.
Retail
Finally, the last data science use case we’ll discuss today is in the retail sector. In this industry, knowing your customers and delivering the right product at the right time is of utmost importance. Preferences are often dictated by trends and can change quickly, so staying on top of this is crucial for long-term success.
With data science, customer information across a variety of channels can be leveraged to create highly accurate segments, predict changes in behavior, and even deliver personalized product recommendations.
By centralizing the necessary data in a customer relationship management platform and enhancing it with data science tools, you’ll be able to anticipate changes in customer preferences. Thus, catering to them in due time to maximize revenue.
Another benefit of analyzing multichannel data is the ability to optimize your prices. Specifically, you can apply some pricing flexibility depending on customer location, buying history, season, or even competitor prices.
With this kind of real-time optimization, retailers can boost profitability and make sure that they’re capitalizing on every sales opportunity.
Project Estimates
Watch our webinar to learn about the practical ways to evaluate your software project estimates.
Reap the Rewards of Data Science
As we have seen today, there are multiple ways for data science to be used in business and across various industries. From improving internal processes to arming you with helpful insights and forecasts, there are plenty of rewards to reap from this technology.
If you’re intrigued about the potential use cases outlined above or are curious about your unique business situation — don’t hesitate to reach out to our team. We offer extensive data science services and will be happy to guide you on your software development journey.
Get the conversation started!
About the author
