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What Industries Use Data Science?

WHAT INDUSTRIES USE DATA SCIENCE?

In simplest terms, data science is the study of data. However, it is a much more complex undertaking than that.

Data science uses many different tools and methods to analyze and interpret data. More specifically, data scientists use math, statistics, the scientific method, advanced analytics, artificial intelligence, and specialized programming to determine what information is held in vast amounts of data.

But data science isn’t just an undertaking in the scientific realm. Instead, many different industries use data science to reveal patterns in data, uncover insights about data, and use their newfound knowledge to make informed decisions.

Today’s world is filled with more information than can be managed without the proper tools. Data science provides those tools to virtually every industry.

Let’s first take a look at a deeper explanation of data science in general, and then explore some of the industries that are harnessing the power of data science.

What is Data Science?

As noted in the introduction, data science can be simply defined as the study of data.

But if we dig deeper, we find that data science involves many different processes, which makes sense given that it’s a multidisciplinary approach.

The overarching goal of data science is to develop an understanding of what the data is saying, and then extracting actionable and meaningful insights that can help inform decision-making.

Before data scientists can get to the analysis of data, though, there is much work to be done. Data has to be collected, stored, and organized, prepared for analysis and processing, and then it can be analyzed.

The preparation process can be a lengthy one. Data preparation might involve aggregating data. It might also involve cleansing the data. Data scientists can also manipulate the data in different ways, depending on the types of processing the data will undergo.

The analysis phase of data science typically involves the use of mathematical and statistical methods – like the use of algorithms – to reveal patterns in the data that might be informative to the end-user. Likewise, data analytics is increasingly performed with the help of AI models, which are all but necessary given the sheer amount of data that is created by organizations on a daily basis. Without AI, machine learning, and similar tools, the task of analyzing data would be far more complicated and time-intensive.

Of course, the whole point of analyzing data is to use it to inform the decision-making process. Oftentimes, this means using software to find patterns and make predictions, which are then validated through scientific testing.

Once the data has been analyzed and verified, data scientists use data visualization tools to make sense of the data for laypersons. For example, in a business setting, a data scientist might use graphs, charts, and other visual aids to disseminate their findings to managers or executives in the company.

This is a very brief overview of data science, but it should give you a working knowledge of what the process looks like.

Now let’s examine some of the applications of data science in various industries.

Healthcare

Data Science in HealthcareThe healthcare industry uses data science in order to take incredible amounts of raw data and have them converted into usable and manageable information.

For example, data science helps keep track of patient records, current scientific studies, and research findings, to name a few. Additionally, data science helps integrate data within medical insurance systems in order to provide the best care possible with the least amount of expenditure.

The key term here is “best possible care.” Obviously, the purpose of the healthcare system is to ensure that each patient receives the best possible care, and data science helps achieve that goal in a variety of ways.

For example, medical imaging is an important form of data science in healthcare. Computer programs can learn how to examine X-rays, MRIs, and other images to identify tumors, anomalies, and other issues that could pose a threat to a patient’s well-being. This, in turn, helps medical professionals make more accurate diagnoses that enable the proper care to be given.

In addition to assisting with diagnosis, data science helps in the treatment phase too. Enormous amounts of data have been collected about the human genome, and that data can be used to identify more specific treatments that can be used to address common health problems in specific individuals. That is, medical professionals might be able to use data science to identify targeted treatments for a patient’s illness based on their specific genomic data. Again, this leads to improved care and a better long-term diagnosis for the patient.

Drug development is another healthcare realm that is being heavily impacted by the use of data science.

Developing new drug treatments takes billions of dollars and often takes more than a decade before the drug is available on the market. But by using data science techniques, this process can be sped up because scientists can simulate how a drug might react in the human body. 

Of course, data science is also used in the daily operations of healthcare facilities. For example, predictive analytics can help hospital administrators ensure there are enough staff on hand to manage a typical day’s load of patients and that there are enough beds for said patients.

Data science can also help inform medical facilities how to streamline the operation of the emergency room, thereby ensuring that each emergency patient gets the critical care they need to survive.

Other uses of data science in the healthcare space include in maintaining electronic health records, streamlining billing, and coordinating with insurance companies.

Banking

Data Science in bankingAnother industry that has come to rely heavily on data science is banking and finance.

Think about it – millions upon millions of people entrust their finances to banking institutions. This means that banks have an incredible amount of data to maintain – customer information, account information, deposits, withdrawals, and transfers make up just a portion of the data that financial institutions collect. Data science makes the collection, organization, and maintenance of all that data a much simpler process.

Data science is used to help prevent fraud, too. For example, machine learning can be used to detect fraud and devise ways to prevent it in the future. So, if someone gets your credit card number and goes on a spending spree, your bank’s fraud prevention program will identify the unusual purchases, initiate a red flag, and put a hold on the account to prevent further transactions from occurring.

Like healthcare institutions, banks must also maintain a vast amount of data about customers. By using data science tools like machine learning, banks can use that information to learn more about their clients, their clients’ behaviors, their preferences, and how they spend or save their money. In turn, financial institutions can use that information to provide more targeted services to their customers.

Law Enforcement

Data Science in law EnforcementLaw enforcement officials are always looking for ways to reduce crime. Likewise, law enforcement agencies are consistently devising new ways to protect individuals and organizations from criminal activity. Data science is an incredible tool for that very purpose.

One such example is the use of body cams. With more and more law enforcement officials equipped with body cams, there is an incredible amount of video footage that’s available for review. Data science can speed up that review process by using AI to analyze the footage. For example, a facial recognition program might be used to identify unknown subjects that have been captured on body cam video.

Law enforcement agencies also use data science to help detect and prevent crime. For example, a logistic regression might be used to help classify the likelihood that an offender will commit another crime in the future. As another example, neural networks might be used for speech recognition purposes or to help reconstruct audio transcripts from sources like victim interviews or 911 calls. 

In other words, there are many different ways in which law enforcement can use data science to streamline their work and improve their chances of preventing crime and capturing suspects.

Retail

Data Science in RetailRetail is one industry where data science provides incredible benefits. By taking raw data and breaking it down into different sub-sectors, the retail industry is able to see different trends almost as quickly as they emerge.

For example, at one time, retailers had to wait for daily or weekly reports of sales numbers to  determine which products or services were doing well and which ones were faltering. Today, though, data science enables retailers to analyze that information instantaneously, thereby allowing them to focus their marketing and sales efforts more appropriately.

As a result of this, retailers can improve their ability to provide their customers with appropriate products and services. What’s more, it enables retailers to more effectively target upselling opportunities and cross-selling opportunities based on specific customers’ purchasing habits.

Other areas in retail that benefit from data science include inventory management, fraud detection, personalized marketing, and even augmented reality, which allows customers to “see” items they are interested in purchasing in their own homes.

Real Estate

Data Science in Real EstateData science has proven invaluable to realtors and other professionals in the real estate business. It has allowed access to information for potential buyers and sellers that can both speed up the entire transaction process, as well as determine whether or not the finances are already in place to make a sale on a property.

In addition, data science pinpoints which neighborhoods are the most desirable to live in, as well as which areas are on the cusp of becoming desirable. Having that kind of information allows realtors to make more informed recommendations to their clients about potential properties to purchase. It also helps investors put their money in areas that maximize their return on their investment.

Another way in which data science is used in real estate is in aiding in the appraisal process. By using analytics tools that tap into big data, appraisers can make more accurate determinations of a property’s value. This, in turn, ensures that the property can support the asking price and that buyers can get appropriate financing to make the purchase.

In years past, real estate decision-making was often based on little more than first impressions or gut feelings, but now data science gives us the tools we need to analyze the information at our fingertips to make more informed decisions. Even something as simple as an online search for properties makes use of data science – it gives buyers the tools they need to filter properties based on availability, location, features, and so forth. Details like crime rates, school district ratings, and cost of living are also readily available thanks to data science.

The Challenges of Using Data Science

While it might seem that data science is the salvation for many businesses, the reality is that data science is not an end-all, be-all solution for every problem in every industry.

In fact, there remains a disconnect between data science and the businesses that use it, according to Forbes.

One of the reasons for this is that there is an unrealistic expectation from businesses as to how to use data science within the company framework. While the data science teams have access to a vast amount of important data, the difficult part is finding ways to take that data and make something useful to the business itself.

Of course, the increasing amounts of data that need to be collected, analyzed, verified, and reported on are only increasing by the day. This ensures that data science will continue to grow as a field, but it also adds to the complexity of using data science to achieve business-related goals.

As we’ve discussed, the industries using data science encompass nearly every walk of life. Even those industries not currently using data science on a regular basis often find themselves in need of using it more and more.

Technology has touched every aspect of our individual lives in some way or another, and data science is here to make the best use of the information that’s gathered. So, while today’s list of industries that use data science is quite long, in a few years’ time, the list will likely be much longer.

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