Applications of Big Data Technology in the Activities of Modern Enterprises

Big data, representing huge volumes of heterogeneous rapidly arriving digital information that cannot be processed by traditional methods, nevertheless allows us to identify patterns between events that cannot be found by humans. With a properly constructed query, excellent results can be obtained to optimize any area of activity. This is largely the reason why big data has become the most valuable economic resource over the past decade, which can be traced to changes in the list of the world’s most expensive companies by market capitalization. Today, information is another important national strategic resource in addition to the three main resources: land, air and water. Countries around the world are gradually realizing the advent of the era of big data and are creating a Big Data industry. The importance of big data in maintaining the competitiveness of enterprises is not limited to the areas of generation, storage, management, analysis and their use, as processing and classification tools may be of particular importance here, in order to form an idea of future trends in management decision-making.

The purpose of the study to identify the advantages and limitations in the activities of enterprises in the use of Big Data technologies, with a special emphasis on assessing the extent of their impact on innovation management and economic efficiency of the enterprises under consideration.

Research material and methods

The growth of the big data industry has accelerated the process of industrial modernization and structural change in the economy. With the advent of the big data era, some industries have gradually shifted their attention to a development model that combines traditional industries and big data industries. In traditional industries, the use of big data techniques to explore new needs and research and development of new materials can both reduce R&D costs and improve the accuracy of research and development of new products.

Regardless of industry specifics, every company has two areas for applying big data analytics technologies, internal and external interactions.

Within the framework of external interaction research, of interest is the accumulated customer experience, namely understanding customers through social network analysis, their social status, age, preferences, etc., information about regions, market segments, satisfaction with a product or service, ways of promotion, as well as ways of contact, etc. External interactions can also include everything that is related to the business model and structure of the business and its interaction with the outside world, such as suppliers, partners and sales channels.

The study of internal interaction is aimed at studying and optimizing company’s operational processes, the purpose of which is to increase productivity not only of equipment, but also of employees, as well as rational use of resources. It is worth noting that the main competitive advantage enterprises will be able to obtain, not so much at the expense of data collection, as at the expense of the ability to quickly extract useful information from the total sheer volume of big data generated.

Let’s consider the advantages of applying big data in the management of an enterprise:

• First, it helps improve decision-making. The big data platform has a real-time data resource collection function and can extract key information based on fast processing and analysis of massive data that can better meet the immediate needs of enterprises.
• Second, it promotes more and more diverse decision-making tools.
• Third, it enhances the credibility and quality of the decisions made, because they rely on a large statistical base of source information, which greatly strengthens the credibility of decision-making schemes.
• Fourth, Big Data technologies also guide a company’s operational strategy.

Assessing the areas of influence of Big Data technologies on management decision-making, the following should be emphasized:

1) Impact on the management decision-making environment.

Big data environment based on cloud computing has a great impact on the process of collecting information for enterprise decision-making, making plans, controlling their execution and evaluation of plans, which has led to significant changes in the decision-making environment. At the same time, enterprise management solutions in terms of big data demonstrate clear data-driven functions, that is, data-driven business development, providing proactive and reliable guidance for business improvement and innovation.

2) Impact on management decision makers.

The advancement and application of big data is completely undermining the traditional empirical decision-making model, and the bulk of the decision-making process has expanded from top management to direct. Decision makers, can flexibly use technologies such as machine learning, statistical analysis and distributed processing to extract valuable data from massive data.

3) Influencing the management decision-making process Organization.

The involvement of all employees in the decision-making process leads to a redistribution of decision-making authority of the enterprise, and the change in decision-making authority will ultimately affect the organizational structure of corporate governance and the culture of decision-making. In a big-data decision-making environment, the main problem the organizational structure must solve is how to allocate decision-making authority and choose the right decision-making method.

4) Impact on management decision-making technology.

In the context of big data, data is mainly in the form of data streams. It is necessary to use intelligent analysis technology to explore the potential relationship between data fragments and get the real information. Therefore, enterprises need to accelerate technological innovation and use the latest technology to serve the management decision-making process.

Speaking of the impact of big data on enterprise economic performance, there is no doubt that in many areas, the application of Big Data technologies can help increase productivity, create additional value and expand revenue streams. Because big data has the potential to improve efficiency and effectiveness, companies can not only produce more products at a lower cost, but also add value to products and services.

However, when examining the extent to which big data affects a company’s level of economic efficiency, it’s important that management decisions are maximized to achieve a few imperative benchmarks. First, Big Data technologies are a way to understand the customer by examining all their preferences. In addition, today’s customers are very different from the old ones. The rise of big data allows them to research products, understand the volume of consumption and explore their customer benefits before they buy them. Using big data, interactions between manufacturers and consumers can be personalized, thereby producing consumer-driven products and providing customer-centric services. And based on the data, it is possible to find social and business forms suitable for enterprise development environments, use this data to analyze and understand user and customer attitudes toward products, and accurately discover and interpret many new user needs and behaviors.

Secondly, with the help of big data technology, companies can collect and analyze resource extraction, specific conditions and reserve allocation needed in the enterprise mode to form an enterprise-level resource allocation map, similar to an “electronic map”. The different benefits will be point-to-point data and graphical mapping, so that plant managers can view their own plants more intuitively and make better use of various existing and potential resources. Without big data, it will be difficult to find correlations between behaviors that were once thought to be completely unrelated.

Third, Big Data technologies can be used to plan production technologies. Big data not only changes the way data is aggregated, but also affects the production and delivery of enterprise products and services. By using data to plan production architectures and processes, they can not only help them discover methods of combining values that are not known in traditional data, but also provide appropriate, customized solutions to detailed combining problems for enterprises. The function of big data virtualization greatly reduces enterprise business risks, allows the enterprise to give appropriate deterministic answers before production or service starts, and allows production and service orientation.

Fourth, through the correlation analysis of big data, according to the overlap and coincidence of data from different brand markets, the direction of the company’s method will become intuitive and easy to identify, and will be more confident in brand promotion, choice of location and strategic approach. In addition, big data technology can contribute to the intelligent operation of enterprises. Intelligent enterprise operations management mainly aims at dynamic communication between the enterprise and users, and provides users with more dynamic and acceptable quality services based on user feedback. With the help of Internet channels, modern enterprises can better communicate with users, users can use the Internet to use more equipment resources and get better services through timely feedback of their experience. On the one hand, it guarantees the efficiency of enterprise performance management; on the other hand, it also provides a powerful guarantee for user experience management and user consumption prediction.

Fifth, through the calculation of big data, social information data, customer interaction data, the enterprise can conduct horizontal design and segmentation of brand information. Business intelligence software Yixin BI tools and retail experience can also help companies better understand the process of increasing sales and eliminating unnecessary costs.

Sixth, big data creates differentiated benefits that are mostly reflected at the strategic level of the business model. Big data can help companies improve their strategic decision-making capabilities. Through data analytics, companies can quickly formulate strategic plans that align with the market. By streamlining processes to improve cost efficiency, big data can allow companies to obtain market and customer information more intuitively and quickly, and market research and customer demand studies can be faster and more efficient.

However, when talking about the undeniable benefits of applying Big Data technologies to modern businesses, we should not forget about the existing limitations of applying these technologies in today’s environment. Today, all industries and sectors are exposed to and using Big Data to varying degrees. However, many businesses or organizations that implement Big Data are not successful. There are still many limitations and challenges in applying big data within the enterprise. According to the study, there are common problems with failed big data implementations. The most typical and serious problems are the following.

The problem of in-house data processing. Today, most companies can only process structured data, and structured data makes up only 15% of total data, and technology for processing more than 85% of semi-structured and unstructured data is inadequate. Mature, improved data processing and analysis technologies are a challenge for enterprises. Newly generated data around the world is increasing by 40% annually. The total volume of global information may be doubling every two years. The increase in utilization rate is less than 5%, and 90% of existing digital content is unstructured.

The shape of big data is important in determining the tools to process and make decisions about visualizing the face of a decision. Moreover, much of the information about a company is stored in multiple databases, with data difficult to share and correlate between different business modules. Achieving correlation and integration of data information between business platforms, is also a major challenge facing businesses. Business intelligence is a major technology in the era of big data, but it is not widespread and is only used in industries closely related to IT (finance, telecommunications, networks, e-commerce, etc.).

In addition, in the era of big data, companies are faced with huge volumes of data, the protection of which becomes extremely difficult. This data includes not only business secrets, but also private confidentiality. Some unscrupulous “hackers” use it to damage the interests of businesses. Enterprises are dealing with information security issues, which is another major problem.

Also a limitation associated with the problems of data processing within the enterprise can be attributed to the rather high cost of solutions, which are often accompanied by a lack of quick results. Enterprises, especially mid-sized businesses, do not adhere to the market development strategy and limit their budget expenditures on information technology. Big data tools are compute-intensive and expensive to purchase, install and use. Business owners want to see a return on investment in the short term, but such systems as big data and their application is a long-term process, and it is impossible to say for sure that the use of such technologies can produce the expected result quickly. The use of big data technologies refers to innovative projects, and as we know they are difficult to assess the effectiveness of investment and a guaranteed result, so not all companies are eager to implement them in their operational processes. However if we talk about companies close to the public sector, things are easier there, due to the general focus of the state on the development of such technologies and the allocation of budgets there is based on a slightly different understanding of the process.

The problem of data storage formation within the enterprise. The most important challenge for enterprises when launching big data technologies is data fragmentation. In many enterprises, especially large ones, data is often located in different departments, respectively stored in different repositories, and the data processing technology in different departments may also be different, which leads to the inability of the company to access its own data. If businesses can’t take advantage of that data in a timely manner, its value is lost.

The problem of “sluggish” enterprise management systems. Currently, only a few high-tech enterprises place a high value on the use of big data in decision-making. Most business leaders don’t realize the value of big data. Some business leaders believe that big data is just data entry and collation, and its use cannot directly benefit an enterprise. However, it is known that the more data a business has and the more effectively it integrates with each other, the more competitive the business is.

Although some companies collect and analyze data, their managers still follow the traditional management model and follow too closely the cause-and-effect relationships. In the era of big data, we’re not chasing causality, we’re correlating. In massive data, as long as the factors that have more to do with improving corporate profits are dug out, it can provide strategic support for managing corporate decisions to some degree. This requires corporate executives to have a deep understanding, which creates a new challenge for the thinking style of management decision makers.

The problem of considering the impact of big data on the quality and timeliness of enterprise management decisions. Making managerial decisions in the enterprise is becoming increasingly complex, it is difficult to analyze the value of information related to decision-making, which to a certain extent determines the level of development of system competencies of decision-makers. At the same time it should be noted that, for example, there is still a deficit of specialists in the Russian market, there is no formed professional community, which would perform the function of informing the market from inside. That is why many companies train their professional staff independently, but these measures are still not enough. It should be taken into account that the speed of corporate decision-making is not as fast as market changes. In addition, enterprises face the problem of diversification of decision-making subjects. In this case, enterprises need to establish a hierarchical decision management system to improve the scientific level of management.

The problem of providing data storage. Today’s big data is information about the phenomena under study from different sources, different standards, large volumes of data, multiple structural forms and real-time requirements. These challenges undoubtedly increase the complexity of data collection and integration, especially in terms of data pre-processing and filtering. If the filtering is too fine, it is easy to filter out useful information, and the level of detail in the screening is too coarse, and the desired analysis effect cannot be achieved. Problems in data security and privacy issues arise from a lack of one hundred percent customer confidence in big data technology in the area of data privacy and personal information. In general, it is fueled by the lack of full-fledged legal and regulatory regulation in the area of big data.

Forming directions for improving the processes of using big data technology, the following should be emphasized:

First, it is necessary to improve the security of big data collection, use and storage. The issue of protecting data privacy caused by enterprise management is becoming more and more important. Verizon’s 2015 data breach survey report shows that more than half of the top 500 companies have been hit by “hacking” attacks. In response to this problem, enterprises must build big data silos, monitor information security in real time, and streamline decision-making procedures. The national government should also improve the legal and regulatory framework for the existence of Big Data and increase penalties for misconduct with it.

Second, professional talent needs to be developed. In the era of Big Data, the problem of talent shortage is gradually becoming prominent. In academic studies, McAfeeA and others have pointed out that talent is an important factor influencing corporate management decisions. In this regard, enterprises can choose highly qualified professionals for continuing education through internal training; government agencies should encourage colleges to focus on training talent in this field; colleges and universities should also change the traditional education model and focus on innovation and practical linkages in the curriculum system to ensure that there are enough qualified professionals.

Third, the data model must be unified and systematized. For example, all data are stored in one database. Big data analysis is very different from traditional data analysis. Big data platforms and analysis will be used to digitize the fragmented market, and then customer data will quickly build decision data so that companies can track and respond quickly to changes in the market environment. Creating a single data model can help companies integrate different businesses and form an actionable circle of operations.

Fourth, an open data sharing system must be created. Future big data companies must have a common mission. Enterprise data is often limited, and it often requires someone to share it to enrich their form of data. This requires businesses not only to be open-minded, but also to be able to share data.

Fifth, it is necessary to view big data as a strategic resource. Data is like oil, and it is inexhaustible oil, placed in a cornucopia if stored. Businesses with a strategic vision can judge the value of data in the future and are willing to spend some cost to store some potentially valuable data.

Sixth, a system to support and encourage the use of big data needs to be developed and implemented at the state level. Data processing technology has always been seen as an important factor influencing the widespread use of big data. Without stable and secure data processing technology, it will not be able to exploit the enormous commercial value it contains. Therefore, it is worth paying attention to the development of appropriate technologies for data analysis and processing. The government should actively encourage related technologies and pay attention to research and development of data processing technologies in universities, enterprises and research organizations.

Conclusion

The technological changes taking place within the global digital transformation, which are taking place on a global scale, bring a huge number of opportunities into the activities of companies and people’s lives. The use of big data is one part of the digital transformation. The world is changing and will never be the same again. The only question is how quickly the process or business model in which a company exists will become obsolete. In the course of this process, the boundaries of industries are being erased and what used to be the company’s advantage may lose significance altogether, and instead of existing ones, completely new areas will emerge that were previously unnoticed; it is these shadow growth areas that the company can identify through the use of big data technologies.