STRUCTURE

• Introduction – A short introduction to big data (15 words)      
• Body – Role of impact of big data in businesses (120 words)
• Conclusion – Mention a short conclusion  (15 words)

ANSWER

Big data is a term that describes the large volume of data – both structured and unstructured beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.

The advantageous effect of employing Big Data for business depends on numerous factors, most importantly – on the business type. In general, it is used to promote products, develop better business strategies, reach customers, explore new markets and target audiences, optimize workflow, lower costs, and gain other competitive advantages on the market. The ultimate business impact of Big Data, regardless of the specific field of its implementation, is raising profits through data collection, processing, and utilizing analyzed information. However, it is important to understand that Big Data alone may be insufficient for obtaining sizeable long-lasting benefits and that it provides much better results in combination with AI, ML, cloud services, and other powerful Data Science solutions.

Retail, Marketing, and Advertising

• Retail companies are among the largest consumers of Big Data solutions as they deal with staggering amounts of data on a daily basis. Both offline distribution networks and online marketplaces collect and process large volumes of information in order to use it later for management, marketing, and other business intelligence activities.
• The sources of such information vary depending on the enterprise type and may be quite numerous and diverse. They include, among others, cookie files, especially those related to social media, POS terminals, customer surveys, and other means to collect information on user behavior.
• The most frequent task of Big Data solutions is linked to finding behavioral patterns that can be employed for many purposes. Understanding the habits and desires of customers is the primary concern of any business, and Big Data analytics is the only valid way to achieve precise results based on large sampling and extensive calculations instead of personal hunch and experience.
• Another common application of Big Data in business is forecasting performed using predictive analytics on the basis of large data sets. The bigger number and volume of data sets allow obtaining more precise results, configuring more parameters before the analysis, and even extending the topics and overall possibilities of forecasting. One of the most prominent examples of such automated prediction solutions is called MemeMachine, and it is employed specifically for advertising. In particular, it helps to increase the effectiveness of ad campaigns by providing valuable strategic advice based on input parameters and the assembled database of customer behavior patterns. As a result, this solution allows saving budget regardless of the advertising type and platform: Google Ads, AdMob, Brandvoice paid program, and so on.

Banking and Finance

• Financial institutions do whatever they could to minimize risks; that’s why they need to check a lot of information, in addition to other activities that involve data handling. Therefore, they employ Big Data solutions to collect extensive data about their clients that covers the financial history and other behavioral aspects. Analyzing this data allows financial institutions to decide whether they should give a credit to a particular client or to realize what kind of deals and services are required by customers.
• Moreover, the use of Big Data techniques allows banks to implement sophisticated risk management systems that offer fast risk calculation enhanced by artificial intelligence and machine learning. These complex systems provide analysis results shortly after they receive input data, so the client waiting time is reduced significantly. As a result, the clients learn the decisions regarding their loan applications almost immediately.
• Since the banking industry is characterized by high risks and even higher competitiveness, Big Data software provides invaluable advantages here. However, to use Big Data with maximum efficiency, a company should also invest in the infrastructure that provides an appropriate level of computational power, storage capacity, data throughput, and security.
• One illustrative example of using Big Data for the purposes of credit companies and banks is described in our case called “AI Loan Finance.” This solution retrieves and analyzes information on the client's credit history and security property in order to decide whether a person should receive a loan. The software uses several national-wide databases and may operate in a fully automated fashion after receiving input data.

Resource mining industry

• Many large companies that engage in mining and extracting natural resources, primarily gas, oil, or coal, benefit from implementing Big Data technologies in their businesses. Such industries involve a number of activities that require careful management with an extremely high precision level. The scope of resource extraction companies is usually very large, and they may include dozens of mines, rigs, and other platforms for collecting natural resources. Therefore, the operation of such companies results in immense data flows that require significant computational power in order to collect, process, and store such amounts of information.
• Mining companies often use Big Data to plan their expansion strategy in terms of searching for new producing areas, field development, and reservoir exploitation. Thus, resource mining is among the few industries that use large amounts of data for production forecasting. This research is crucial in this field as it saves significant amounts of time and money that would otherwise be wasted on drilling dead ground or excavating barren rock. With the help of AI and ML, raw data is compared with the existing databases to calculate the economic feasibility of developing the specific resource production region using geological data, fuel price fluctuations, weather forecasts, and other crucial factors.
• As another example, Shell implements Big Data solutions in combination with AI technologies and cloud services to analyze its immense machinery stock, predict its wear over time, and plan the maintenance and ordering of new parts and appliances. This approach allows maintaining the steady workflow, minimizing downtime caused by equipment fails, and optimizing the inventory of spare parts. All these benefits result in considerable savings of money and time, and improve productivity leading to better profits.
• At last, it should be noted that most mining enterprises also engage in retail and transportation activities that are described herein as separate industries. This way, most benefits described in relation to those industries are also relevant for resource mining.

Transportation and Logistics

• Like other enterprises mentioned in this article, transportation companies also deal with large volumes of information regarding vehicles, passengers, luggage, and cargo. The necessity to manage extensive data flows under tight time constraints imposed by transport schedules requires high-performance software solutions designed specifically for Big Data.
• The practical purpose of such solutions lies in tracking cargo delivery, monitoring fuel usage and supply, the technical condition of the company’s vehicle park, drivers’ health checkups and work schedule, as well as many other relevant factors. This way, companies can use Big Data software to prioritize safety in addition to the usual resource consumption optimization and effectiveness improvement.

Other business system management

• The examples presented above described the industries where the use of Big Data has already become a customary technique that increases business effectiveness. However, in addition to the aforementioned fields, Big Data may be effectively used in many other business areas. In fact, any enterprise that requires business insights and powerful resource monitoring options, as well as many other management features, will benefit from this technology.
• Due to the high applicability of Big Data solutions, the demand on them is gradually rising every year across various industries. Business analysts predict that the revenues from the global Big Data market will reach 70 billion dollars in 2020 and about 103 billion dollars in 2027.