Table of Contents
- 1 Why Big Data Is so Important?
- 2 What Is the Main Source of Big Data?
- 3 What Is an Example of Big Data?
- 4 How Do You Analyze Big Data?
- 5 How is AI Used with Big Data?
- 6 How Does Big Data Benefit a Business?
- 7 Conclusion
Today, a business that does not use data in its strategies is very much at risk of being crowded out by its more advanced competitors and deprives itself of the ability to be aware of the current market situation, trends, consumer preferences and even events that are just about to occur with a high degree of probability.
In turn, big data provides an ideal opportunity to understand the real picture of what is happening. If earlier business strategies were developed almost blindly, basing themselves on the common sense of the owner of the company, today the data provide very good help for making error-free decisions. In this article, the SPD Group talks about the concept and benefits of big data and its impact on business in 2020. And to understand it comprehensively, let’s start with the most basic questions and answers to them.
Big data and its business impacts are manifested in the fact that it allows businesses to make more strategic decisions, adapt to user preferences, customize their products and offers, improve user experience, and more closely meet their expectations. And profit growth is a global result and the goal of all these actions.
The two main sources of big data are business and users. Absolutely any action of any company or person on Earth can be considered as a source of big data if it has value and useful applicability for business. In simple words, any visit to the site, purchase of goods in a supermarket, hotel reservation can be considered as a source of big data.
If we talk about real examples, then it is possible to list a million of them, and each example will be specific to the industry in which a certain event occurred.
For example, if we look at the healthcare industry, then an example of big data is indicators of the body of the owner of a wearable device. If we look at retail, then an example of big data is information about the products that users buy most often. If we take the tourism sector, the big data is information about which countries are visited by tourists most often and in what season of the year. And this list can be limitless.
We have already said that big data has great potential for business, but they do not make sense without analysis. Without analysis, big data is simply arrays of information that cannot have any business value until they are systematized, classified, analyzed, and certain patterns are revealed. The general big data analysis algorithm is as follows.
- Identify the business problem you want to solve, for example, understand user preferences.
- Systematize the data and select only that part that is directly related to the problem.
- Identify common patterns.
- Visualize data.
- Conclude on this basis.
There is a justified opinion that big data is too big without the use of artificial intelligence. And this is actually so. To explain how AI works with data, let’s get back to our algorithm. In practice, only the first and last stages are a zone of human responsibility. Everything else is done using AI systems that are initially trained to recognize data, classify, structure and analyze them, and make predictions based on previous historical information.
Collecting big data makes no sense without the ability to analyze it, and this can only be done with the help of AI, whose ability to process information is far superior to humans.
Below we have listed several industries and described in detail how big data affects each of them and what opportunities they open up.
The possibilities of AI in conjunction with big data are endless for retail. For example, it is possible to develop personalized offers for customers, immediately recognize the client that entered the physical point of sale, and even assume his intentions regarding the purchase by analyzing his facial expressions.
Plus, big data made visual search possible – when a user having a photo of a thing that he likes, he can immediately find the site where this thing is for sale and the physical point of sale closest to him. Big data also allows window dressing to be made more intelligently and even improve the layout goods in the store based on user preferences.
For this industry, big data and AI also offer many additional opportunities. Here are the most basic and promising examples.
- Improving production efficiency through analysis of customer preferences
- The development of new approaches to food packaging, which should become more environmentally friendly under the influence of changes in the worldview of customers
- Quality control of manufactured products through recognition systems and matching of each product unit with an ideal sample
- Identification of new consumer preferences through analysis of big data, for example, as Pepsi Cola did, by installing self-service machines so that users can create individual drinks
- Improving the organization of food delivery to the points of sale taking into account weather and traffic data
- Reduced food waste by producing better products, faster delivery, and maximum compliance with user preferences
- More reasonable menu and purchase planning for restaurants and cafes by analyzing the most popular orders and reducing food waste from these institutions as well.
One of the most promising uses of big data in healthcare is the ability to recognize diseases in their early stages. The system reads the vital signs of the owner of the wearable device, compares them with its knowledge of how certain diseases begin. These diseases can be specific to this user in terms of his age, indicators and medical history, so smart systems immediately signal potential risks. Moreover, such a preventive system is even more effective than clinical tests, which sometimes can not catch the warning signs at the earliest stages.
As for financial services, here AI and big data have two main applications – these are also personalized offers and improved security, plus the fight against financial fraud.
As for the first opportunity, using big data, financial companies can find out the current intentions of users – for example, it became known that he intends to buy a car, as he looked through advertisements on the corresponding sites. In turn, the company, taking into account also the financial capabilities of the user, can offer him the most favorable loan for the purchase of a car.
Speaking of financial security and fraud prevention, here AI systems analyze huge flows of current and historical information, identify legitimate and suspicious behavioral patterns, recognize the cardholder’s face when buying offline and in real-time can draw conclusions about the legality of each financial transaction. And also signal about possible violations.
AI and big data development are about to revolutionize education – it will no longer be the same because of the introduction of these technologies. As in all other industries, the first thing AI does in conjunction with big data is personalizing experience. For education, this means the possibility of developing personal educational plans and strategies. Also, voice assistants begin to gradually fulfill the functions of teachers, saving them from routine work.
Plus, AI systems help educators solve organizational issues by optimizing the procurement strategy for teaching materials, organizing exam results, developing advertising campaigns for colleges and universities, and hiring new teachers.
So, as you can see, the main goal of big data and AI for a business is to increase its profitability by providing higher-quality services and the production of more sought-after goods. Plus, big data makes users’ lives safer and more convenient, and this is a definite plus for the reputation of companies that help their customers achieve these goals.
About the Author
Helen Kovalenko is IT Project Manager at SPD Group AI development company. She is working in a Data Science Team over the NLP, Computer Vision, and Fraud Detection solutions, and gladly shares her researches and developments in her blog posts.
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