In one of our previous blog posts, we talked about the most common mistakes in data science for business. One of the crucial ones was the wrong approach. We told you that every data scientist has to think about benefits first. As in every other business area, there’s no place for art for art’s sake. That’s why, when talking about data science for business, you have to concentrate primarily on problems this discipline can solve. And this is exactly what we are going to talk about in this article.

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With data science for business, companies can improve their work and the results they get. Data science helps you understand data better. This understanding gives you the possibility to fix everything that doesn’t work correctly and improve even more these aspects that already prove effective.

In fact, data science for business can easily solve the vast majority of problems and issues modern organizations struggle with. That’s why we can say data science is such a massive milestone in every company’s development. Let’s take a look at some practical aspects of data science for business.

Data science for business: Improves work

For starters, data science help companies work in a better, more effective way. Let’s take a look at some examples:

WITH DATA SCIENCE, YOU CAN SPOT TRENDS AND CORRELATIONS IN DATA

Companies that deal with big data on a daily basis simply need data science for business. There is no other equally effective method that would allow you to analyze and categorize all these massive volumes of information. With data science, companies can analyze data they possess more thoroughly.

As a result, they can draw more accurate and useful conclusions and spot trends and correlations. When your company is utilizing data science for business, you can make more informed business decisions.

Data science for business, analysis, work

FUTURE PREDICTIONS

This is also wonderfully helpful. The data science tools and applications, frequently combined with machine learning models, can help companies predict the future. How is that possible? Thanks to the analysis of historical data.

The more information your company processes (or has access to), the more accurate predictive models are possible. In many instances, you can also use data that comes from the market itself, not just your company. Just think of the scenario where your company makes decisions not based on the owner’s hunch but all the previous data and market research. Wouldn’t that be outstanding? And yes, it’s possible! In a way, data science works a bit like a crystal ball. Only way more reliable.

Data science for business, analysis, charts, predictions

COMMUNICATION TOOLS

Another exciting example of data science for business in action are modern communication tools, especially chatbots and voice bots. The way they can improve work is obvious–these applications can automate communication between your employees and customers. Many studies show that modern AI-fueled chatbots can easily replace humans concerning straightforward queries and repetitive tasks.

Naturally, data science is not the only technology that’s necessary to make this happen. Chatbots and voice bots are typically a combination of data science, machine learning, and artificial intelligence. But still, data science is indispensable.

chatbots, hello

Data science for business: Optimizes work

Although this application is similar, we decided to put it in a different category. Work optimization is a multifaceted aspect of data science. Consider some examples:

DIGITAL TWINS

Perhaps, it’s the most outstanding example of how data science optimizes work. In fact, some time ago, we published a separate article about digital twin examples, you ought to read it! How can digital twins optimize work? As you probably know, they are all about creating an exact digital copy of the device or tool. The digital twin technology allows you to get priceless insights about improving operations, increasing efficiency, reducing costs, or discovering issues or glitches before they happen. And all of that without the real-world consequences!

Digital Twin Case Study

Today, digital twins are more and more commonly used in:

  • Troubleshooting and remote repairs
  • Training new employees
  • On-site inspections (here, computer vision plays the key role)
  • Optimizing parameters (see the aforementioned article and read how digital twin technology is used in Formula 1!)

MANAGING SYSTEMS, DEVICES, AND INFRASTRUCTURE

This application is eagerly used by companies operating in various branches of the industry. Just think of hundreds of industrial facilities: Factories, plants, refineries, and oil platforms. All these facilities have to continually monitor tens of multiple sensors and measurements, 24/7, 365 days a year. On many occasions, that requires site visits. Such inspections are time-consuming and costly. Thanks to data science, they can be reduced to the minimum necessary. Data science, combined with IoT, allows industrial companies to monitor all these parameters without conducting so many in-person inspections.

managind systems in data science for business

INTERNET OF THINGS (IoT)

Nowadays, almost everything can be connected to the Internet. That’s possible thanks to a technology called the Internet of Things. IoT is based on installing multiple sensors and detectors that measure various parameters 24/7 in places where your products/devices are stored, used, transported, and shipped. For thousands of companies, this solution amazingly optimizes work. Just think of the retail companies. More and more often, customers require accurate information about the products they purchase. They want to know that the products they buy are 100% safe, and they were produced in compliance with applicable standards. IoT can provide such assurance.

Data science for business: Supports digital transformation

As we told you earlier, data science for business is a massive milestone in every company’s development. This refers to digital transformation and marketing, as well. Actually, there is no modern online marketing without data science!

CLOUD COMPUTING

In early 2020, we mentioned cloud computing as one of the essential big data trends for this year. It wasn’t just an educated guess. Cloud offers enhanced security and stability, helps cut costs, and gives companies much greater flexibility. Moreover, many data science tools and applications are cloud-based so that you can use them no matter where you are or how extensive your team is.

cloud computing

MARKETING AUTOMATION

This is especially important when it comes to performance marketing. The main idea behind marketing automation is to conduct efficient marketing campaigns without human assistance. In This way, data science for business also helps you improve work. Marketing automation tools, mailing tools, and CRMs–all these applications are designed to help companies with digital transformation, but, more importantly, to improve their everyday work. Thanks to marketing automation, you can:

  • Save time: Thanks to various automation tools, you don’t have to, for instance, waste time sending emails to your customers.
  • Save money: To some extent, marketing automation allows you to reduce workforce

Marketing automation could never be possible without data science. Starting with Google Analytics and ending on tailor-made B2B online platforms, every single online marketing tool operates on data. And data science is what makes them usable.

marketing

Data science for business: The essence

Data science for business can be a very broadly understood term. Today, technologies and disciplines like data science, AI, machine learning, and business intelligence are strictly interconnected and cannot exist without one another. However, we have to state that, in its essence, data science is all about five fundamental applications:

  • Identifying trends and patterns in large datasets: We’ve already talked about that. Data science (or data analytics, if you will) is the best and most effective way to analyze large datasets in order to draw useful conclusions from them.
  • Identifying anomalies: Anomalies are frequent occurrences in various industries and sectors. Especially banking and e-commerce companies should be interested in identifying anomalies. For instance, if a bank detects some unusual activity on their client’s account, they can take necessary steps to prevent, for example, money laundering.
  • Predicting the likelihood of future events: Predictive analytics can take into account many elements and datasets to present you with an accurate prediction about future event or course of events.
  • Showing how things are connected to one another: In the article about data science mistakes, we warned you about mixing correlation and causation. However, detecting both these occurrences can be immensely helpful in your company’s development.
  • Categorizing individual data points: We also refer to this application when we talk about classification. Classification is all about identifying to which of a set of categories a new observation belongs, on the basis of a training dataset containing observations (or instances) whose category membership is known. Classification is essential, for instance, in spam detection.

To sum up, data science helps you work more efficiently and, in many instances, quicker as well. Thanks to data science, your company can understand data and draw useful business-wise conclusions, which, in turn, leads to more accurate and informed decisions.

If you are interested in applying data analytics in your company–drop us a line today! Addepto is experienced in various aspects of data science. We will gladly help you devise the perfect solution that will meet your needs and expectations.

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