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BIG DATA AS THE MAIN PLATFORM FOR ENHANCING A COMPANY'S VALUE
Sarbasheva Asiyat
master student in Financial University, under the Government of Russian Federation ИСПОЛЬЗОВАНИЕ БОЛЬШИХ ДАННЫХ ДЛЯ СОЗДАНИЯ СТОИМОСТИ КОМПАНИИ
Сарбашева Асият, студентка магистратуры Финансового университета при Правительстве Российской Федерации
ABSTRACT
This article summarizes theoretical and practical aspects of Big data used for enhancing company's value. Big data becomes popular due to new techniques of its' collection and methods for the analysis. Big data presents one of the most complete database for revenue growth due to understanding of the customer's profile, cost cuts due to the effective cost management, and predictive analytics due to good information population. This article summarizes main implications of Big data by economic sectors.
Big data doesn't drive value itself, but being accurate and properly analyzed it provides opportunity to implement efficient solutions in terms of both cost cutting and optimization or revenue growth, thus enhancing company's value.
АННТОТАЦИЯ
В данной статье описаны теоретические и практические аспекты использования «больших данных» для создания стоимости компании. «Большие данные» становятся все популярнее благодаря появлению новых методов сбора и анализа. «Большие данные» могут служить наиболее полным источником информации для увеличения выручки компании, в частности благодаря изучению потребителей, уменьшению расходов компании, в частности благодаря определению «узких мест» и неэффективных затрат, а также могут служить подробной базой для прогнозного анализа. В статье обобщены основные методы использования «больших данных» в компаниях разных секторов экономики.
Сами по себе «большие данные» не представляют ценности, но при правильном методе анализа они создают возможность для принятия эффективных решений, создающих стоимость компании.
Keywords: big data, company's value, shareholder's value.
Ключевые слова: большие данные, стоимость компании, акционерная стоимость.
1. Big data definition and characteristics Nowadays companies store and process petabytes of information: transactions, client data, web traffic, call recordings, publications on social networks, emails, journals equipment, sensor readings, etc. In order to obtain value from the data it's required to be analyzed properly, frequently and timely, but in the first place the data should be available in real time.
The concept of Big data is the series of approaches that allow to work with large amount of data that are difficult or
impossible to control by common means. They have different structure and significant rate of replenishment, for example unstructured data (metadata, tweets, and other social media posts) and multi-structured data (data from web applications or social networks, such as web log data) [2].
Big data is collected from traditional and digital sources within and outside the company, and therefore creates the most complete data for the following analysis. Big data is significant source for predictive analysis.
Big data provides new opportunities to the business, such as most complete information on customers and their behavior; and enables the business to develop products and marketing campaigns, monitor competitors' actions and based on the information received reconsider running growth playbook and long-term business development.
Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, manage, curate, and process data within a tolerable elapsed time.
Big data can be described by the five Vs characteristics, which are set out in the chart below. The main features are described as follows. [4]
Chart 1. Big data characteristics
■ Volume - the size of the data which determines the value and potential of the data under consideration.
■ Variety - diversity of factors' categories.
■ Variability - the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.
■ Velocity - the speed of generation of data, or how fast the data is generated and processed to meet the demands and the challenges which lie ahead in the path of growth and development
■ Veracity - the quality of the data in terms of accuracy. Most data has the property of complexity that means
that it should be linked, connected and correlated in order to be able to grasp the information that is supposed to be conveyed by these data.
Big data analytics consists of six Cs which stand for Connection (sensor and networks), Cloud (computing and data on demand), Cyber (model and memory), Content/Context (meaning and correlation), Community (sharing and collaboration), and Customization (personalization and value). [4]
2. Applied technologies and data collection
In order to provide useful insight and gain correct content, data has to be processed with advanced tools (analytics and algorithms) to generate meaningful information. The information generation algorithm has to be capable of detecting and addressing invisible issues such as machine degradation, component wear, etc.
Big data requires exceptional technologies to efficiently process large quantities of data within tolerable elapsed times. A McKinsey report suggests suitable technologies include A/B testing, crowdsourcing, data fusion and integration, genetic algorithms, machine learning, natural language processing, signal processing, simulation, time series analysis and visualization [6]. Multidimensional big data can
also be represented as tensors, which can be more efficiently handled by tensor-based computation, such as multilinear subspace learning [5]. Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data mining, distributed file systems, distributed databases, cloud based infrastructure (applications, storage and computing resources) and the Internet [6].
The traditional data derived from product transaction information, financial records and interaction channels, such as the call center and point-of-sale can't be excluded. All of that is big data even though it may be dwarfed by the volume of digital data that's now growing at an exponential rate.
Data sets grow in size in part because they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers, and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s [3]. as of 2012, every day 2.5 exabytes of data were created [1]. The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.
3. Big data analysis
For the adequate analysis the proper data is required. Based on the accurate information and with the use of correct techniques value-adding solutions can be derived. Thereby big data provides good population that becomes essential part for further analysis.
As the techniques various methods can be used: SWOT, PEST, BCG Matrix, Porter's Five Fources, Balanced Scorecard, Six Sigma, Lean Management, etc.
Chart below sets out main steps of the analysis: from data collecting to implementation of efficient solutions with the following evaluation of changes' impact on the business.
Chart 2. Big data analysis steps
4. Big data implications
Big data is widely used in industry (IBM, GE, Walmart), internet (eBay, Amazon, Facebook), science and research (NASA (NCCS), SDSS, MIT) and government (USA, India, United Kingdom).
Main implication grouped by economic sector are provided below.
Generally big data is used for customer analysis, portfolio analysis and measurement framework.
1. Predictive maintenance: increased production by reducing equipment downtime (achieving the production plan and sustainable development of the company), more accurate planning of repairs, and decrease of stock.
2. Situation center: immediate response to events with the action plan in all areas (staff, ecology, production), strategic and operational planning, monitoring and evaluation of the impact of external influences on the management objects, automotive control over settings, accumulation of information-analytical systems management experience.
3. Fraud prevention: identification of non-authorized transactions and deviations, social environment analysis.
A. Implications in Retail
1. Demand forecasting: including business factors in forecasting models, measuring the impact of business factors (including the expected changes in retail prices) and events / activities on forecasts, analysis of the range to identify gaps and opportunities, optimization of plans in order to improve the efficiency product promotion, developing order pipeline and inventory process.
2. Inventory management, including accelerating of detailed information collection on the goods and stocks from 15 days to 5 minutes, accurate and rapid identification aged and obsolete inventory, good returns from customers and promotional campaign planning in real time.
3. Optimization of warehousing, including streamlining of operational cycle.
B. Implications in Telecommunications
1. TMT data storage, including integration with key market systems (such as SAS).
2. Customer loyalty management through customer's profile identification: customer segmentation, evaluation preferences and calculation of return for each group; analysis of records of customer calls; targeting and planning of marketing campaigns.
3. Merge of billing systems using intelligent procedures and technical support for M&A.
C. Implications in Oil & Gas
1. Retail sales, including demand forecasting, brand-analysis, macroeconomics impact, potential growth of sales od substitutes.
2. Economic efficiency of oil fields, including determination of optimal development program analysis and identification of suboptimal areas of development.
3. Digital Oil Field: monitoring of drilling process, tracking of the schedule of capital construction, analysis of the current situation in the well based on the historical data, identification of incidents, corporate technological knowledge base.
D. Implications in Financial sector
1. Credibility analysis (underwriting and scoring): modeling script passing the application of the borrower, which records deviations from the credit rules and calculated credit limit; building effective scorecards and automatic determination of the significant factors and selection of the optimum of scoring points.
2. Liquidity management (BASEL III tightened requirements): measurement of liquidity ratio in real time.
3. Calculation of capital adequacy ratio.
E. Implications in Government sector
1. Social and Economic development (SED) forecasting: currently SED forecasting is made by indexation method due to lack of relevant data.
2. Centralized data storage implementation on "Electronic Budget" of the Federal Ministry of Finance and Treasury of Russian Federation: currently the system contains dozens of relational databases and integrated with each other systems without unified analytical data storage system.
3. Identification threat to national security: identification of schemes and incidents related to unauthorized viewing by members of the object information stored in the internal database including: id relations be-
tween an individual and a list of companies on the criterion of an individual belonging to the list of founders or executives of the company; authorized list by legal persons viewed RPF officers. 5. Concluding remarks
Big data is a set of techniques and technologies that require new forms of integration to uncover large hidden values from large datasets that are diverse, complex, and of a massive scale.
Big data doesn't drive value itself, but being accurate and properly analyzed it provides opportunity to implement efficient solutions in terms of both cost cutting and optimization or revenue growth, thus enhancing company's value.
In Russia, Big data has not being spread widely. However, some industry sectors, such as retail, telecommunications, banking and government, show interest in Big data development and implementation.
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АНАЛИЗ И ПЛАНИРОВАНИЕ ТРАНСПОРТИРОВКИ МАТЕРИАЛЬНО-ТЕХНИЧЕСКИХ
РЕСУРСОВ ИП САВОНОВ Ю.Н.
Савонов Александр Юрьевич
студент 3 курса, Амурский Государственный Университет, г. Благовещенск
Ким Ольга Вечеславна
студент 3 курса, Амурский Государственный Университет, г. Благовещенск
ANALYSIS AND TRANSPORTATION PLANNING LOGISTICAL RESOURCES IE SAVONOV Y.N. Savonov Alexandr, 3rd year student Amur State University, Blagoveshchensk Kim Olga, 3rd year student, Amur State University, Blagoveshchensk
АННОТАЦИЯ
Цель: поиск пути снижения издержек при транспортировке материально-технических ресурсов в условиях предприятия ИП Савононов Ю.Н. Метод: аналитический. Результат: принято решение перейти на более экономичный вид транспортировки. Вывод: при использовании аналитического метода исследования проанализированы способы транспортировки грузов по тарифным планам предприятй, занимающиеся транспортировкой грузов из города Москва до города Благовещенск и выбран наиболее эффективный способ для снижения экономических издержек при транспортировке.
ABSTRACT
Objective: To search for ways to reduce the costs of transporting the material and technical resources in an enterprise IP Savononov Y.N. Method: Analysis.
Result: The decision to switch to a more economical form of transportation. Conclusion: using the analytical method studies analyzed ways of transportation tariff plans enterprises engaged in the transportation of goods from Moscow to the city of Blagoveshchensk and choose the most effective way to reduce the economic costs of transport.
Ключевые слова: логистические издержки; транспортировка материально-технических ресурсов.
Keywords: logistics costs; transportation of material and technical resources.
Транспорт - это отрасль материального производства, осуществляющая перевозки людей и грузов. Значительная часть логистических операций на пути движения материального потока от первичного источника сырья до конечного потребления осуществляется с применением различных транспортных средств. Затраты на выполнение этих операций составляют до 50% от суммы общих затрат на логистику[1]. Логистические издержки (затраты) - это
сумма всех затрат, связанных с выполнением логистических операций: размещение заказов на поставку продукции, закупка, складирование поступающей продукции, внутрипроизводственная транспортировка, промежуточное хранение, хранение готовой продукции, отгрузка, внешняя транспортировка), а также затраты на персонал, оборудование, помещение, складские запасы, на передачу данных о заказах, запасах, поставках[2].
