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Big Data Across Industries: Use Cases

Blog: Think Data Analytics Blog

Big data technologies provide access to qualitatively new knowledge and opportunities that not only give companies a competitive advantage in the market, but also develop the industry as a whole, and use hidden potential. 

Such prospects are open for more than twenty industries: from financial organizations and the public sector to metallurgy and the oil and gas industry.

The industry spectrum of big data applications is very wide. Let’s take a look at the most obvious uses for these technologies. 

Retail

Significant volumes of data accumulated by large retail chains can provide a lot of useful analytical information to top managers: what products are in demand, whether they are in sufficient quantity in the warehouse, whether supplies are established, which stores are the most profitable, and so on. 

For analytics, tools such as, for example, SAP HANA are used. It allows a supermarket with more than 10 thousand items of goods to spend on obtaining detailed information on them not 15 days, as with manual analysis, but only 5 minutes.

The system automatically predicts demand and offers a number of solutions for a specific situation: planning promotional campaigns, moving goods to other warehouses and stores, returning to suppliers and other actions. 

As a result, response times to identified sales opportunities are reduced from months to days. An example is the success of Switzerland’s largest retail company, the Migros Group, which owns a chain of 37 department stores. 

The speed and accuracy of processing operational data is achieved thanks to the SAP HANA platform. This solution allows the company to flexibly change its pricing policy and marketing strategies, responding to the slightest market fluctuations. 

A retailer with more than 800,000 items from 3,500 suppliers gets a report on out-of-demand items in each product group in 17 seconds. For comparison, before installing SAP HANA, it took 22 minutes to generate such a report. 

Sales promotion reports are generated in 60 seconds – instead of 7 minutes, as it was before. As a result, the company managed to radically change the strategy of promoting low-demand goods.

In, projects for the implementation of analytical solutions were implemented in such large networks that trade in electronics and household appliances as M.

Video and Eldorado. As a result of the implementation of SAP HANA, the preparation of annual reports in companies has been reduced from ten to three days, the speed of daily data downloads instead of three hours now takes 30 minutes. In addition, the implementation of BW on HANA helped M.Video improve logistics planning.  

Metallurgy

The share of energy resources in the expenses of metallurgical enterprises increased to 30%. Therefore, the topic of energy saving management is becoming more and more relevant, today it is being dealt with by general directors and chief engineers. 

They often do not have complete information on energy expenditure. SAP HANA provides remote operational accounting and monitoring of electricity consumed for various needs (by individual industries, workshops, sections, types of products), and the formation of reporting documents based on these data. 

Result: improving the efficiency of the enterprise’s energy facilities and reducing the cost of electricity by identifying and eliminating the factors of ineffective use of it. In metallurgy, big data is valuable for researching sales strategies and shaping pricing policy.  

Financial industry

In financial institutions, SAP HANA can serve as both an electronic trading platform and a tool for analyzing creditworthiness or calculating capital adequacy ratios. For example, in accordance with the instruction of the Central Bank No. 139-I “On the mandatory ratios of banks”, about 300 indicators are calculated based on a large amount of initial data. In other cases, the platform needs to handle a huge flow of read and write requests (thousands per second) to support bidding or auctions. 

In banks, big data can be useful for credit scoring, as well as underwriting – modeling the scenario of a borrower’s application, in which deviations from credit rules are recorded and the credit limit is calculated. 

Integration of such a subsystem with the system for entering loan applications reduces the processing time of applications by several times. 

As an illustration, the experience of the Ural Bank for Reconstruction and Development is interesting – it began to work as a client base to create loan offers, deposits and other services that can most likely interest a particular client. 

During the year when the relevant IT solutions were applied, UBRD’s retail loan portfolio grew by 55%.  

Oil and gas industry

Big data is used in the oil and gas industry for both resource extraction and marketing. Evaluation of the field development efficiency is of course important in production. 

This implies a huge set of functions: comprehensive analysis and identification of non-optimal development areas, targeted planning of activities, selection of geological and technical measures, forecasting effects, selection of optimal options for programs of activities, development modes in accordance with the requirements for production, economics, and infrastructural constraints. 

Monitoring of the drilling process, tracking the capital construction schedule, analyzing the current situation at the well relative to historical data, identifying incidents and further forecasting possible incidents at other assets are also important.

When selling petroleum products through retail networks (filling stations), big data helps marketers to forecast demand, conduct brand analytics, analyze prices and their changes in the context of competing companies, regions, taking into account macroeconomic indicators. 

And the commercial director will be interested in the possibilities of increasing sales of related products (by identifying patterns) and reducing the downtime of gas stations (due to more accurate logistics of fuel trucks).

As an example, we will cite the active introduction of big data technologies in the Brazilian oil and gas industry, which is under the influence of two factors: the discovery of large hard-to-recover oil and gas reserves and the rapid growth of the IT sector. 

Modern IT tools made it possible to analyze the entire volume of exploration data, extract the most valuable information from it, and on its basis, investors were able to make an informed decision about investing in “complex” Brazilian oil.

The result of the introduction of new technologies is massive foreign investment in the industry. Thanks to partial government support and investments from foreign energy companies, many international IT companies are now investing in the country’s economy. 

EMC alone has already poured nearly $ 100 million into a research and development center in Rio de Janeiro that develops big data technologies, specializing in storage, analysis and management solutions for big data, abundantly generated by the country’s oil companies.

Despite the fact that today only 2% of oil exploration work in Brazil is carried out by rival companies of the Brazilian state-owned Petrobras, last year alone they invested about $ 500 million in R&D here.

At the same time, this figure is expected to increase by 25 in the next five years. %, and by 2017, the total investment in R&D for oil exploration and production in Brazil will amount to $ 7 billion.  

Telecom

Advanced big data technologies are of course also used in telecommunications. One of the areas of application is subscriber loyalty management. 

Companies use big data to form subscriber profiles: they segment the customer base, assess preferences and calculate profitability for each group. 

Then they analyze the records of customer calls by tens and hundreds of customizable parameters, and determine the social groups of subscribers. After that, planning and preliminary assessment of marketing campaigns, high-quality targeting based on subscriber profiles are carried out. 

As a result, marketing helps prevent subscriber churn by identifying and assessing the importance of factors that affect loyalty.

In early 2014, the Spanish telecom company presented an analytical system based on working with big data in 3G and 4G networks. 

The system predicts the behavior of crowds of people based on information transmitted by their smartphones, both on the street and in hospitals, libraries, shopping centers and other public buildings. 

The system can be used by law enforcement agencies, during concerts and sporting events, and for organizing advertising campaigns.

In telecommunications companies are also using big data. For example, MGTS now provides online management of technical personnel: predicts the needs for field resources, plans personnel changes, and also optimizes employee work schedules. Such a system is already used by large foreign operators.

Another solution in the field of telecommunications is preventive diagnostics. By analyzing various parameters of equipment operation, it is possible to identify patterns of system behavior that precede the occurrence of failures, to determine the causes of failure. 

Early diagnosis allows you to plan for preventive maintenance, replacement and repair of equipment in a routine, hassle-free way for customers.

Telecom operators try to analyze which of their customers is considered an authority in their circle of communication and what their needs are. 

This information is provided by the analysis of social networks. If suddenly one of the reputable clients publicly announces that he is switching to another operator, then this can cause a domino effect. 

It is naturally in the interests of the company to prevent these events. And predictive analytics can help her in this, which will reveal the tendency of an increase in the cost of a service, or a decrease in the number of calls. 

The company has a chance to rectify the situation without bringing the user to the transition to a competitor. Predictive analytics can automatically alert when action is needed. It is reported that in this way the American T-Mobile has reduced the level of customer churn by 50% in the quarter. 

Transport

SAP HANA also offers solutions for the transport sector. An interesting solution exists for controlling the deployment and execution of schedules, planning timetables on the railway. 

First, it provides an analysis of train deviations with an indication of the reasons for the delays. 

And, secondly, it provides the possibility of flexible and quick processing of customer requests by quickly calculating various options for fulfilling the request (for example, offering other deadlines or a different volume, at more favorable prices for the customer) with the criteria of optimality for the implementation of the schedule, the company’s profit and satisfaction client. 

Big data can also improve diesel fuel accounting. To do this, an analysis of fuel consumption is carried out in order to determine the optimal speed mode along the route with different tonnage and the number of stops.

Railroad construction has also become a big data target. Every year, more and more kilometers of railway tracks are being built in the world, and, accordingly, its repair and maintenance are also a costly part of the country’s budget. 

For example, the Canadian engineering company Bombardier has built monorail trains in Saudi Arabia and São Paulo that are 25% lighter and 10% less energy efficient than traditional metro rolling stock. 

The company borrowed ideas for the construction from the aerospace industry. In addition, the system is highly economical, not requiring significant construction and infrastructure costs.

In the Netherlands, railways use state-of-the-art software to analyze 56,000 variables, including the condition of the railways and the level of passenger demand. 

Thanks to the analysis of this data, the carrier dispatches more than 5,000 trains per day, increases operational efficiency by 6% and saves about 20 million euros per year.

An equally telling example is the use of big data in aviation. Bangkok Airways uses a full range of solutions based on the SAP HANA platform. 

One of the advantages of this solution for passengers is that the company receives information on the most popular and busiest routes in real time, quickly introducing additional flights on them. SAP solutions improve the efficiency of airports around the world, so, using them, the management of Fraport AG (Frankfurt am Main airport) has achieved a 70% reduction in maintenance and equipment management costs.  

Energy

Solutions based on SAP HANA and energy are applied. A popular topic in recent years has been smart power systems. 

They have a wide range of functionality – from monitoring and adaptive network management in real time, analyzing and changing the topological parameters of power grids, to ensuring communication between consumers and suppliers. 

The main task is to optimize generation and consumption, and, as a result, to reduce energy costs. 

Another solution is monitoring the technical condition of power grid equipment, which implies early detection (forecasting) of malfunctions and an increase in equipment availability, which ultimately makes it possible to switch to maintenance of production assets “according to the actual state” and enables the management of enterprises to make more informed decisions.

Another area of ​​work in the energy sector is operational monitoring and forecasting. This refers to the operational monitoring of transmitted and consumed electricity, electricity metering in real time and the formation of reporting documents based on these data. 

The result is an increase in the efficiency of the energy sector and a decrease in electricity costs due to the identification of irrational use and forecasting consumption volumes.
As an example, we can cite one of the developments of SAP – the concept of “Manhattan”. 

The project scenario assumes that every home will be equipped with a smart meter. The readings will be measured every 5 minutes, and the results obtained will be sent to an analytical system based on advanced technologies for working with big data, integrated with GIS (including online maps). 

Thanks to this, it will be possible to see an overall picture of energy consumption in the system and obtain detailed information for each district and house: how energy consumption changes depending on weather conditions, time of year and day. 

And based on this real and accurate data, it will be possible to plan the energy supply to one of the busiest and most energy-intensive areas.  

Cross-industrial scenarios

In addition to solutions for specific industries, big data is also used for cross-industrial scenarios. Let’s briefly talk about some of them.

Organization of “repairs on condition” (Predictive Maintenance) allows you to reduce equipment downtime, more accurately plan repairs, and reduce inventory.

Situational center – it is organized for instant response to events with an action plan in all areas (personnel, ecology, production), automatic control of the set parameters of the system functioning, identification of possible threats and support for the development of solutions in atypical, crisis and emergency situations.

Anti-fraud helps in identifying fraudulent transactions and certain types of object behavior – either falling under pre-configured filters (inconsistency of information in various data sources, transaction codes), or containing deviations (the number of services provided is greater than the average in the group, the current level of service consumption is not corresponds to the history of consumption), etc.

Brand analytics provides for the use of big data to analyze data from social networks, media and forums, the formation of a personalized approach to customers.

Image recognition and identification – identification of offenders, fraudsters, customers using CCTV cameras and a recognition system, data profiling.
The number of such scenarios will only increase over time as technologies for processing and analyzing big data develop.

The post Big Data Across Industries: Use Cases appeared first on ThinkDataAnalytics.

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