Data Science And AI: In What Business Areas They Are Implemented?

The amount of data on the internet is growing exponentially. Data is everywhere, and it is a resource for business and marketing. Data science processes, analyses data, and answers business questions in retail, industry, banking, and other areas.

Business development requires testing hundreds of hypotheses and interrelated factors. Data science helps you analyse large amounts of information, extract useful knowledge from it, and take actions based on it to improve your business.

AI is directly related to data science. This value may lie, for example, in expanding the possibilities of forecasting, knowledge of patterns, and reasonable decision-making. In a narrower sense, AI is about algorithms and methodologies for processing information. Artificial intelligence operates on huge arrays, analyses incoming data, and develops adaptive solutions based on them.

Data science and AI by Grid Dynamics are used in various fields, including marketing and business.

Marketing

There is a lot of data in this area that needs to be processed and appropriate conclusions drawn. For an effective marketing strategy, it is important to understand the patterns of customer behaviour in order to increase the chances of attracting and retaining them. Machine learning algorithms are able to handle huge numbers of users and the information about them efficiently and quickly.

Data science will show how users behave on different platforms on the Internet: on websites, in applications, and in social networks. Based on the data, the company understands how to simplify the purchase process, what elements to add to make the product more attractive to the client, and what features to use to improve the service. Data science indicates successful and ineffective content on the company’s resources: which materials are read to the end, and which ones are scrolled through.

Finance

Data science and AI are used in this area. With their help, huge amounts of data are explored, on the basis of which you can make investment decisions, as well as protect the company from fraudsters.

For example, AI is able to predict the rise or fall of a particular stock based on data collected over a long period of time.

In the banking system, this tool can check creditworthiness based on the given parameters and provide the machine intelligence with the necessary information.

Retail

To create better customer experience models, many retailers turn to data scientists to help them run experiments, gather insights, segment customers, and find new ones. The algorithms created on the basis of the analysis performed determine the decisive factors: what reasons motivate customers to stay and increase their check or go to competitors. Moreover, algorithms help to determine the activities with the greatest effect in time and influence the client using tools with the best budget.

In the case of regular customers, the main task of analytics is to predict with high accuracy the right moment when a particular product will be needed by a particular person. To attract new customers, correct and effective marketing activities are important, the development of which requires econometric models that take into account many parameters. Such models make it possible to predict not only the contribution of each advertising channel to the final result, but also the influence of external factors: changing weather conditions or traffic congestion at a certain time.

Medicine

One of the most promising areas of application of data science in medicine is the diagnosis of oncological diseases. Experts in this field use a range of algorithms to develop solutions. For example, you can make diagnostics based on images of a tumour. In this case, data scientists will most likely use neural networks. For diagnostics based on the results of the analyses, one of the machine learning methods that is best suited for a specific task will be selected. There are also specific algorithms used, for example, to analyse DNA data obtained from single cells.

Production

Technologies are used to analyse production data, optimise supply chains and reduce costs. With the help of machine intelligence, patterns in equipment breakdowns are revealed, which makes it possible to prevent their failure. In addition, AI and data science provide an opportunity to make the supply chain more efficient and seamless.

The use of artificial intelligence is gradually becoming a necessity in all business sectors. The only question is who will introduce modern technologies first and get a quick result, and who will catch up at the very end in order to at least just stay on the market. Data Science has a significant impact on marketing and sales, and market analysts strongly recommend implementing artificial intelligence today.