Nowadays, absolutely everything is based on data. It doesn’t really matter if you sell shoes, run a marketing agency or produce bicycle tires. Data is essential for you. You use it all the time. Data helps you in almost everything you do. From searching on Google, through hiring new workers up to creating financial reports. It really is all about data. Check our examples of data science applications!

2.5 quintillion bytes of data

No wonder that data science has just HAD to come into play. Its birth, development and common usage was obvious and predictable. The more data people are producing every year, the more complicated it is to embrace it. And that’s the role of the data science, and those who deal with it – data scientists. Talking about data – there really is quite a lot going on out there! Would you believe us, if we told you that over the past two years 90 percent of all data available in the world was generated? Or maybe it would sound more believable to you, to hear that back in 2018, people were producing 2.5 quintillion bytes of data… every day? Maybe a word of explanation. 1 quintillion is 1 followed by 18 zeros. That times 2.5. Each day! That’s huge amount of data that is in usage.

Those numbers are truly mind-boggling, absolutely unimaginable. If you acknowledge that, you understand the need for data science. Additionally, data science is present in our lives, right now, every second! Even though it is invisible, it’s always working in the background – for you! For instance, it’s the data science that suggests you Google results, while you are still typing your query. The data science that proposes you friends on Facebook. It’s the data science that proposes you, what you might want to watch on Netflix. The data science that proposes you job offers on LinkedIn. There are hundreds of usages.

data science examples real application

And now, we will trace some more examples of data science in order to realize, that it is not only present in our lives, but it is also hugely important! Let’s go on a trip!

Without data science, you lose!

According to McKinsey Global Institute (MGI) forecasts, companies that want to continue to be competitive, develop and scale their business should invest in Data Science consulting company & Machine Learning projects. For this reason, presidents and directors must be aware of that need and ready for a new reality that brings with it the need to manipulate data and solve business problems based on conclusions, not guesses. Below we will present 6 Data Science examples of real-life applications.

Data processing processes in order to obtain useful information and conclusions based on them help in strategic planning and prevent the incurring of previously unnoticeable costs in the organization. At this moment, you probably should ask: where can we use Data Science or Machine Learning? A one-word answer? Everywhere! The detection of patterns in the data is really in every field and industry. Sooner or later, any company wishing to compete on the market will face the need to digitize the organization. The greatest benefit for entrepreneurs will be speed, combined with information about strategic importance, which together can give an incredible opportunity to build a competitive advantage and be one step ahead of everyone.

How does data science usage help your business?

This topic is very wide, but let’s shorten it just to the essence. Firstly, with data science, you can make better decisions. Why? Because they are made not on someone’s opinion but on a much more reliable source. Yes, data! Thing is, only data science and machine learning systems can analyze millions of bytes of the given data within seconds.

It is a massive simplification, but more or less it works like that: you have a problem to solve -> you input that into the data science application -> the application analyzes all the data available -> you receive the best solution. Do you see the difference? You may have your instinct, but it can be unreliable. Data science and machine learning systems do not have this problem. They work without emotions and rush.

data science examples real application man problem solution think

Secondly, the usage of data science helps in rising sales. Machine learning systems can explore historic data, make comparisons and analysis of the market and, on that base, make recommendations of how, when, and where your product or service will sell best. What’s more data science can help you in improving accuracy in reaching your target audience.

Data analytical systems, such as Google Analytics, deliver you accurate data about who visited your website or e-commerce, when, from where what was he or she interested in, and many more. If you have been using Google Analytics already, you know how powerful a tool it is. It helps you to suit your target audience’s needs, and that is done by modifying your advertisements, your website’s layout, or even offers too!

Yes, using data science do may cause your company to implement some changes, because with the data-based solution probably you will see some new and unexpected possibilities. But the results of implementing the data-based strategy can also be unexpectedly good!

data science consulting

6 Data Science Examples

Enough of the introduction. Let’s see right now real-life applications of data science in several areas. Let’s focus on 6 data science examples of such applications. We will see how does data science helps in manufacturing, sales, retail, logistics, marketing, and banking. For more information take a look at data science consulting page.

1. Demand prediction for the manufacturing industry

The first example is the manufacturing industry. Many manufacturers depend on data science to create forecasts of product demand. It helps them in optimizing supply chains and delivering orders without risk of over/under-ordering. Data science can make a lot of savings for your company especially in supply chain optimization. It minimizes the risk that parts won’t be delivered and stocked on time. Data science in supply chain optimization takes into consideration many factors that can have an influence on the entire process, for example, shipping costs, weather, material availability, market scarcity, and many more.

What’s more, companies are able to analyze the needs and behavior of customers using data analysis. The results of this analysis are crucial in understanding what products enjoy the highest demand on the market – which in turn translates into an increase in the number of goods sold. The sales forecasting and optimization of the supply chain also bring high value. Companies with the help of forecasts and appropriately learned conclusions are able to optimally allocate resources and have better control over expenditure.

So to sum up this part – if you run a manufacturing company, data science will be useful for you, particularly in: forecasting the demand for a given product, optimizing the supply chain and minimizing the costs.

data science examples real application manufacturing man

2. Supply chain optimization in the logistics industry

While we talk about optimizing the supply chain, we go straight to the second example: it is also of huge importance in the logistics industry. Have you ever wondered how many packages are shipped every day? Well… it’s a challenge to get the exact number, but UPS and FedEx alone are shipping 32 million parcels each day [combined data, with 19M for UPS and 13M for FedEx]. So it’s obvious that the logistics industry simply drowns in data. Just think of that! Every day millions of pieces of data are processed! To name just customer data, GPS data, the number and types of items, carrier data, and delivery information!

This is a challenge for data science! Optimization algorithms are able to shorten the delivery time and select the optimal route for the vehicles – thus reducing operating costs and speeding up the work! Transport service providers are also able to predict the demand for services with high accuracy by combining historical data with information on consumer profiles and macroeconomic indicators.

Data science can also optimize the warehouse sector. It saves time, space, and resources while reducing errors in managing the warehouse. The data science mechanisms constantly search for the most efficient solutions for storing products and managing a warehouse.

And well, it’s just a matter of time when self-driving trucks will be introduced to the market. You don’t believe that? It has already begun! For example, Tesla is already testing its autopilot system, and Google has its own automated car, called Waymo. In the not too distant future, AV [Autonomous Vehicle] trucks will be part of our landscape.

data science examples real application logistics shipment

3. Customer analytics in the retail industry

In the retailer’s business, the most valuable information is included in receipts. Millions of transactions flowing from all stores combined together give a broad look at customer profiles. Quick processing of millions of receipts and conclusions drawn on this basis help to arrange products in the shop correctly and to compose appropriate baskets of goods – which, according to the Forrester research company, in 80% of cases results in increased sales and higher quality of customer service.

Hundreds of millions of receipts issued every day lead to huge amounts of data, absolutely impossible for one human or even whole team of humans to master. You simply NEED the data science for that, there’s no other option. But how can it help?

To start with – the data science apps can manage promotions and discounts actually in real-time. That could help in selling out old products or creating interest in the new products. Another example – the data science can scan the whole social media network in order to forecast what products will be in-demand in the near future and promote exactly the same products to the market.
And yet another example – the data science can analyze the online cart for every single customer and display recommendations based on that exact customer’s preferences! Also in real-time! This is called upselling (when you offer more expensive products instead of the one chosen by the customer) or cross-selling (when you offer an additional product to his current cart content) and both these techniques would be simply impossible without data science. That’s one of the most common and important usage of data science in the e-commerce world.

real application retail ecommerce

4. Recommendation systems in marketing & advertising

We mentioned that a bit in the last point, but that doesn’t exhaust the topic. For marketers, it is very valuable to analyze user behavior on their websites. Based on this information, we can determine what are the tastes and preferences of the customers, what kind of knowledge or help they seek, what are they interested in, what do they want to buy and how much do they want to pay for it.

Customer Journey analysis allows you to create more and more perfect recommendation systems which on the basis of this information indicate in time-specific products that customers are willing to buy. Implementation of such systems helps stores to be closer to the customer and thus drive their business. Some help with that offers the Google Analytics system, which actually is neither more nor less the data science application. It shows, quite accurately, your customers’ behavior on your website, and thus – what you should do or change in order to get more customers interested in your offer.

5. Credit scoring for financial institutions (one of the most popular Data Science examples Application)

The banking sector is our next stop. And one of the biggest problems of this sector is NPL – Non-Performing Loans. These are the loans that haven’t been settled for at least 90 days. After that period loans become NPL – “endangered”. And the problem is very serious. As EBF (The European Banking Federation) informs, on average, 3.74% of all worldwide loans are NPLs.

Of course, every banking company is trying to avoid this problem. And this is where data science comes into play because it can help in lowering that number considerably. Based on an analysis of the given customer’s banking history, data science mechanisms can estimate loan debtor’s creditworthiness and predict which loans can in the future become NPLs. So one of the usages of data science in the banking industry is risk management. Even now banks are using machine learning algorithms to analyze past spending behavior.

Here’s another example – the data science, and especially machine learning applications can analyze the influence of the financial trends and situation on the market on the consumers’ behavior, by learning from their historical data.

To sum up this part, in the finance industry the key is the analysis of banking transactions and based on the insights supporting credit decisions. Fraud detection is also very important, which allows financial institutions to identify the most suspicious operations and pass them for a deeper analysis. The developed algorithms detect illegal transactions that would be very difficult to detect for employees manually. And finally, data science can predict how changes on the market will affect customer’s reactions and decisions.

financial institutions

6. Sales analytics

Sales – exactly what every company is about. Sales representatives have a very tough job. Hundreds of phone calls, meetings, follow-ups, offers, and presentations. Always in a rush. Every day. But data science usage can help them as well! Consider two data science examples.

The data science algorithms can help sales representatives in deciding between products or services eligible to suggest to the potential client. Or they can indicate what discount would be reasonable. So the sales rep saves a lot of time. Time that could be dedicated to arranging new leads! And what’s even better, all of that can be done in real-time, for example, while the representative is negotiating with the client on the phone! This is what data science is about – fast, accurate and irreplaceable support.

Moreover, data science can indicate on which prospects sales representatives should focus on, which prospects have the biggest chance to close the deal. Or propose the set of actions to take that should improve the conversion rate. There’s plenty of options.

We analyzed 6 examples of the data science applications, but in fact, in almost every business, the analysis of sales data is extremely important – which as an example allows you to anticipate trends or profile your clients. The conclusions obtained from this type of data analysis allow making decisions that have a huge impact on company sales. At the same time, the analysis helps to keep up the pace with the dynamically changing market and to concentrate on the leads that have the biggest chance to become the company’s customers.

sales analytics

Haven’t you found particular Data Science examples you are looking for?

There’s so much more to the data science! There are tons of examples of data science applications! With this article, we barely scratched the surface! Check our data science Case Studies and read our other articles to see how data science could be implemented. If you see the great potential for using Data Science & Machine Learning solutions in your business, see the article that will help you effectively implement Data Science and AI solutions. And remember – we are always here for you. Just drop us a line or give us a call! We will gladly talk with you about implementing data science into your business!

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