5 Reasons Why Data Science Is Important

Introduction:

What is the importance of data science? Today, information is everywhere. Organizations must be able to harness data and use it to make informed decisions regarding products, marketing, customer outreach, and business growth. Data science is at the heart of all those activities.

Isaac Newton discovered the law of conservation of energy. This law states that energy can never be created or destroyed. It can only change state. The same is true for data – it can evolve from one form into something else through data science and data analysis. The importance of this process can be seen in machine learning, deep learning, artificial intelligence, big data science, and more.

Contrary to what you might think, data science is not new. It’s been around in one form or another since there was a need to understand things. 

Take the Ishango bone, for instance. Discovered in the heart of the Democratic Republic of Congo, it is thought to be roughly 20,000 years old. It’s theorized to be a tally stick and is regarded as the oldest mathematical tool created by humans.

Fast forward several millennia to the 1600s. John Graunt was an English statistician and demographer who became the first person to use data analysis based on medical birth and death information.

Data science has always been and will always be critical to human beings; it’s how we understand the data around us and how we prepare data for use. Why do we need data preparation? We prepare data to use for better models. Raw data is dirty. Data preparation minimizes errors, enhances data quality, and generates more business insights. The steps involved in data preparation include the following:

• Data Discretization
• Data Cleaning
• Data Integration
• Data Transformation
• Data Reduction

Today, data science is the foundation of everything from Google’s search algorithm to the census and everything in between. Below, we’ll explore five reasons underlying the importance of data science, including:

• Data growth
• Continue improvement in data quality and efficiency
• Enhancing customer service, providing faster delivery, and keeping costs low
• Data-driven research
• Focus on analysis and interpretation rather than modeling

Understanding Data Growth

Why will humanity always need data science? Two words: data growth. According to Statista, “the total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly.” It reached 64.2 zettabytes in 2020 and is expected to grow exponentially, reaching 181 zettabytes in 2025, just five years later.

What led to this explosion and predictions of astronomical growth? One of the most critical events was the reduction in the price and size of storage and transistors. Computers once took up entire rooms and could only handle basic mathematical equations. Today, we carry computers more powerful than those that powered the Space Shuttle in our pockets. That doesn’t touch on the capabilities of more extensive devices, like your laptop or desktop.

According to Forbes, those advances led to the creation of around 2.5 quintillion bytes of data every day. The technological explosion that allowed the rise of e-commerce, smartphones, and biometrics has also increased our ability to access large amounts of data quickly. The result of this evolution is that the physical and digital worlds have aligned very quickly.

Continuous Improvement of Data Quality and Efficiency

The second of the five reasons for data science’s importance is the ongoing improvement in data quality and the efficiency of applying that information to adapt the data world and drive changes into the physical world on that basis. That drives changes in the physical world as companies develop new services and products through data discovery insights.

For instance, consider healthcare analytics, a field that didn’t exist just a few decades ago. The data analysis importance in this area can be seen in several key capabilities:

• Patient Outcomes: Healthcare analytics allows doctors, nurses, surgeons, and others to help deliver better patient outcomes. These can include surgical procedures to whether a particular patient is well-suited for specific prescription medication.
• Service Delivery: Data science techniques can allow administrators, healthcare providers, and decision-makers to unearth patterns and trends and then make changes that enable higher quality services to be delivered with shorter wait times.
• Profitability: Through data mining and discovery, a data engineer can help determine what services, specialties, and focus areas offer the best profitability while also serving the needs of the facility’s patient base, creating a win-win.

These are just some ways healthcare analytics is changing the world of medicine, and it’s all powered by data science. Of course, improved data quality and efficiency also benefit the world in other ways, including real-time information encryption and the opportunity to automate many decisions by closing knowledge gaps.

Enhancing Customer Service, Providing Faster Delivery, and Keeping Costs Low

Customer service and product delivery have traditionally been challenging hurdles. However, data science has dramatically transformed these processes while keeping costs affordable. For instance, suppose a customer contacts a company for support.

Thanks to data mining, cleaning, and transformation, customer service reps can look back at every touchpoint that customer has had with the organization, from website orders to email and phone interactions. This gives the customer service rep an accurate picture of the customer’s history with the organization and an idea of their pain points and frustrations. It’s then a simple matter to speak directly to those challenges and offer solutions that address real-world problems.

The same is true in product delivery. The importance of data science can be seen in how analytics has transformed the logistics industry. It’s no longer about entrusting a customer’s order to a shipping partner. Organizations (and their shipping partners) can now analyze transit routes to reduce time and costs, compare carrier options, and even provide live updates for the company and the customer during the shipping process.

The data analysis importance can be seen in how the service industry adds customized user experiences by taking an example of the hotel industry; the hotelier gatherers data from email communications, website communications, phone conversations, and other touchpoints to offer:

• Customized room recommendations based on group size and makeup
• Customized amenities/services during their stay, such as spa coupons or restaurant discounts
• Customized suggestions for activities and attractions
• Customized Meals according to family size

Those benefits aren’t confined to hotels, either. Data science has implications for logistics, banking, transportation, and many other sectors. In short, data discovery insights help ensure that customers across many industries enjoy a customized, personalized experience that was impossible just a few years ago.

Data-Driven Education and Research

Data science (Data Scientist – Rolai) is both a formal learning path and a defining factor in education delivery. For instance, it’s now possible to deliver educational options based on the learner’s past experiences, talents, traits, and goals rather than having a one-size-fits-all syllabus. Of course, the rise of data science has also expanded the career options available.

It’s important to understand that data in its raw form is largely unusable. Data must be transformed to glean insights from it. Analysis and interpretation are used to achieve that goal. Combined with the wide availability of and easy access to data, these methods help achieve important goals for organizations.

One of those goals is real-time observation. In the past, data analysis was only possible with by analyzing historical data – you could only access and study information from the past. Today, real-time observation offers increased agility and flexibility.

Automated learning is another benefit of machine learning algorithms. Through specialized techniques, skilled data scientists can create algorithms that learn as they process information, helping to alleviate the need for human programming.

Finally, data analysis and interpretation offer better visualizations, improved understanding, and the ability to tell important stories through information.

Conclusion:

Data has been with us for a very long time, dating back tens of thousands of years. And with the rise of digital technology, the importance of data science has increased dramatically. Like a fine wine, it only gets better with age. Today, it offers a way to innovate and create new things, deliver better outcomes, and enhance business processes and customer experiences. 

By this point, you should understand the critical role that data science plays in today’s world. That role continues to evolve by the second. It can combine machine learning algorithms with critical thinking to offer essential insights into everything from your customer experience to ways to evolve your product or service offerings while outcompeting other organizations and driving down costs.