Oil may have been the world’s most precious resource throughout the 20th century, but there’s little doubt its crown has been stolen by data.
Simply put, data is everywhere, yet we still want more of it. In fact, the volume of data created, captured, copied, and consumed worldwide increased 30-fold between 2010 and 2020, and is expected to hit an astonishing 181 zettabytes by 2025:
To put that in context, a single zettabyte is equivalent to 30 billion 4K movies, 60 billion videogames, or 7.5 trillion MP3 songs.
That’s a lot of data!
So it’s no surprise that employment of data scientists is set to increase by 22% –– much higher than average –– between 2020 and 2030, according to the US Bureau of Labor Statistics.
What Is Data Science?
Let’s start with some quick definitions.
Data science is the process by which datasets are built, cleaned, and structured.
That way, big data can be analyzed and used to enhance business decision-making.
As such, the field of data science differs from data analytics. As their job title suggests, data analysts are responsible for analyzing and interpreting the data you’ve gathered.
Data scientists are also different from data engineers, who develop, build, test, and maintain data architectures.
5 Real-World Business Applications of Data Science
1. Improving Sales Performance
No organization can succeed without a high-performing sales team.
From researching prospects to doing cold outreach to nurturing warm leads, sales reps are responsible for bringing in the revenue you need to grow your business.
As such, their time is extremely valuable. Yet surprisingly, almost two-thirds of the average rep’s time is spent on non-revenue-generating activities like attending meetings and updating CRM systems.
Workplace analytics platforms and data science skills can help to maximize the remaining time to ensure you extract the best possible performance from your sales function.
For instance, effective building, cleaning, and structuring of prospect data can help you understand the types of prospects who are most likely to convert –– the industries in which they’re based, the companies they work for, their roles and responsibilities, and their business goals.
That way, your sales team can spend more of their precious time nurturing leads that stand the best chance of becoming paying customers down the line (and, by extension, spend less time on prospects who aren’t likely to buy from you right now).
Likewise, data science can help you understand which products or services to pitch to certain prospects, or how much a certain deal is likely to be worth over the life of the account –– and therefore how much of a discount you can afford to offer that would-be customer.
If you’re not using data to inform these sorts of business-critical sales decisions, you’re effectively operating on nothing more than guesswork.
2. Predicting Patients’ Future Healthcare Needs
Ever wondered how data is changing healthcare?
In reality, it’s changing the industry in myriad ways –– too many to go into in a single section of this article. But undoubtedly one of the most significant applications of data science in healthcare is predictive analytics.
In a nutshell, predictive analytics involves analyzing historic patient data to identify trends and use them to understand future healthcare requirements.
That offers numerous benefits to healthcare organizations, such as the ability to:
- Predict when a patient is likely to encounter a given health issue, thereby enabling healthcare professionals to suggest preventive measures
- Review a range of symptoms and associate them with specific outcomes
- Improve efficiency throughout the supply chain by forecasting future demand for specific medications or procedures
The addition of all those factors results in one thing: better patient care.
Importantly, predictive analytics isn’t an example of technology that might help patients in a decade or two; it’s providing measurable benefits right now.
According to research from the Society of Actuaries, 60% of healthcare executives say their companies have already adopted predictive analytics. And of those that have, 42% have seen improved patient satisfaction levels since they did so, while 39% say they’ve saved money.
3. Understanding Your Audience
As we’ve already seen, businesses have more data at their fingertips than ever before.
Between 2020 and 2022, enterprise data is expected to grow at an annual rate of more than 42%, according to research from Seagate.
However, data isn’t automatically useful. If it was, we wouldn’t need data science at all. In reality, only about one-third of all the data generated by enterprises is actually being used, meaning the majority of corporate data is simply eating up space on hard drives and servers.
Just think how much better you could understand the people who make up your audience if you started tapping into all that unused data.
A data scientist, in tandem with more effective data integration and data recovery, can make it happen. Data and tech professionals can work together to unlock a wealth of information about your customers, including their:
- Demographic makeup
- Purchasing habits
- Personal and professional goals
You might collect data on a customer every time they interact with one of your social posts, open a marketing email, visit your website, or add an item to their shopping cart.
Aggregating all that data allows you to predict their future behavior and apply it to entire segments of your audience.
For example, you might notice that customers tend to buy within seven days of liking a product-related post on Instagram.
Or maybe that customers who abandon their shopping cart are more likely to come back and convert if you send them a cart abandonment email within 30 minutes.
These techniques can help you enhance the customer experience, boost loyalty levels, and improve conversion rates, which is clearly good for business.
4. Enhancing Security Measures
Cybersecurity Ventures expects global cybercrime costs to reach $10.5 trillion annually by 2025.
With so much money at stake, it’s simply good business sense to improve your security measures –– and, once again, data science can help you do it.
For example, finance institutions are using machine learning to scrutinize unusual financial behavior in an effort to identify potentially fraudulent transactions.
Simply put, the vast quantities of data involved mean that these algorithms are able to detect fraud faster and more accurately than humans ever could.
Of course, it’s not just about the banking sector. Companies in lots of other areas can benefit.
Whatever type of organization you work for, data science and machine learning can help to safeguard sensitive information through encryption.
And more generally, improved knowledge of data and privacy measures can mitigate the risk of customer information being misused or mishandled.
5. Analyzing Future Trends in Your Market
Data science effectively grants you the power to predict the future. The more you collect, clean, and structure, the more effectively you can identify customer behavior and market-based trends. It’s impossible to overstate the value of this sort of business intelligence.
There are any number of data points that can indicate the types of products your audience might want to buy a month, a year, or even a decade down the line. Relevant data types include:
- Search engine queries
- Celebrity and influencer activity
- Consumer attitudes and intent
- Current purchase data
The growing popularity of electric cars is a good example of how data indicates the direction of travel within a market.
According to research from EY, 41% of motorists who plan to buy a new car intend to choose an electric vehicle, with 66% of those planning to buy an electric car within the next year. Significantly, that an 11% year-on-year upturn in electric vehicle interest.
On a basic level, previous buying activity demonstrates this trend in action, with global electric vehicle sales rising from fewer than 600,000 in 2015 to more than three million in 2020:
Staying abreast of trends and results within your market allows you to make better-informed decisions that help you beat the competition.
Data has no real, intrinsic value. Without effective cleaning and segmentation, it’s just a load of letters or numbers, with little meaning or context.
But in the hands of a data scientist, it becomes arguably the most powerful business tool you can possibly access, providing invaluable insights on your market and audience.
About the author
Phil is a freelance writer specializing in all things digital marketing. He worked agency-side in the UK for 10 years, before leaving when he realized he could basically do this job from anywhere. Beyond marketing, he loves travel, his cat, and wearing a vest under an open shirt. Connect with him on LinkedIn.