Big Data is a complex and voluminous collection of different information. They are presented “raw” and require preprocessing in order to obtain valuable information from them that can be of benefit to businesses and organizations.
What is Big Data?
The term Big Data appeared in 2008. For the first time it was used by the editor of the journal Nature – Clifford Lynch. He talked about the explosive growth in the volume of world information and noted that new tools and more advanced technologies would help to master them.
To understand Big Data, you need to define the concept and its function in marketing. These days, users generate data on a regular basis: when they open an application, search for information on Google, shop online, or just travel with a smartphone in their pocket. The result is huge amounts of valuable information that companies collect, analyze and visualize.
Big Data literally translates into Russian as “Big Data”. This term defines the masses of information that cannot be processed or analyzed using traditional methods using human labor and desktop computers.
The peculiarity of Big Data is that the data array continues to grow exponentially over time, therefore, the processing power of supercomputers is required for the operational analysis of the collected materials. Accordingly, Big Data processing requires cost-effective, innovative methods of processing information and providing conclusions.
But why put so much effort into organizing and analyzing Big Data? Big data analytics are used to understand the attractiveness of goods and services, predict market demand and reaction to an advertising campaign.
Working with Big Data helps companies attract more potential customers and increase revenues, use resources efficiently and build a competent business strategy.
This means that analysts who can extract useful information from big data are in great demand right now. You can learn this even if you have never worked in IT. For example, “Faculty of Big Data Analytics»From GeekBrains offers convenient online classes and a dozen cases in the portfolio. By the way, the first six months of training are free. Those who have successfully completed the course will definitely be employed – this is spelled out in the contract.
Difference of approaches
| Traditional analytics | Big data analytics |
|---|---|
| Gradual analysis of small data packets | Processing the entire array of available data at once |
| Editing and sorting data before processing | Data is processed in its original form |
| Starting with a hypothesis and testing it against the data | Search for correlations across all data until the desired information is obtained |
| Data is collected, processed, stored and only then analyzed | Analysis and processing of big data in real time, as it becomes available |
Functions and tasks of big data
Analyzing Big Data starts with collecting it. Information is received from everywhere: from our smartphones, credit cards, software applications, cars. Websites are capable of transferring huge amounts of data. Due to different formats and ways of origin, Big Data differ in a number of characteristics:
1. Volume : The huge “volumes” of data that organizations receive from business transactions, smart (IoT) devices, industrial equipment, social media and other sources need to be stored somewhere. In the past, this was a problem, but the development of information storage systems has eased the situation and made information more accessible.
2. Velocity : Most often, this item refers to the growth rate at which data is received in real time. In a broader sense, the characteristic explains the need for high-speed processing due to the rate of change and bursts of activity.
3. Variety : The variety of big data is manifested in its formats: structured numbers from customer databases, unstructured text, video and audio files, and semi-structured information from multiple sources. Whereas previously data could only be collected from spreadsheets, today data comes in a variety of forms, from emails to voice messages.In Russia, Big Data also means processing technologies, and in the world – only the object of research itself.
| Function | A task |
|---|---|
| Big Data – actually arrays of raw data | Storage and management of large volumes of constantly updated information |
| Data mining – the process of processing and structuring data, the stage of analytics to identify patterns | Structuring a variety of information, searching for hidden and non-obvious connections to bring to a common denominator |
| Machine learning is a machine learning process based on the connections found in the analysis process | Analytics and forecasting based on processed and structured information |
Big Data characterizes a large amount of structured and unstructured data that is generated every minute in the digital environment. IBM claims that in the world, enterprises generate nearly 2.5 quintillion bytes of data every day!And 90% of the global datareceived only in the last 2 years.
But it is not the amount of information that is important, but the possibilities that its analysis gives. One of the main advantages of Big Data is predictive analysis. Big Data analytics tools predict the results of strategic decisions, which optimizes operational efficiency and reduces company risks.
Big Data combines relevant and accurate information from multiple sources to accurately describe the market situation. By analyzing information from social media and search terms, companies optimize digital marketing strategies and consumer experiences. For example, information about the promotions of all competitors allows the firm’s management to offer a more profitable “personal” approach to the client.
Companies, government agencies, healthcare providers, financial and academic institutions are all using the power of Big Data to improve business prospects and customer service. Although research shows that almost43% of commercial organizations still do not have the necessary tools to filter out irrelevant data, losing potential profit. Therefore, today the market has outlined a course for the modernization of business processes, the development of new technologies and the introduction of Big Data.
Blockchain and Big Data: the potential of combined technology
Blockchain is a decentralized transaction system where every transaction is verified by every element of the network. Such a system guarantees the immutability and impossibility of data manipulation.
Cryptocurrencies and other blockchain technologies are becoming more and more popular. In Japan alone, nearly 50 banks have partnered with Ripple, an open source blockchain network and the third largest cryptocurrency market capitalization in the world.
For banks, the cooperation will provide instant, risk-free transactions at a low cost. Financial structures in other countries are showing interest in such operations, which means the further development of new technologies in the banking sector.
The popularity of the technology foreshadows an exponential growth in the volume of transactional data recorded in registers. By 2030, the information contained in the blockchain ledger will be up to20% of the world market Big data and will generate up to $ 100 billion in annual revenue. Keeping these “data lakes” with traditional cloud storage providers (AWS or Azure) will cost a fortune.
In a timely manner, decentralized data warehouse providers appeared on the market offering cost savings up to 90%… Their work facilitates the implementation of blockchain around the world and guarantees the development of the field.
If big data is quantity, then blockchain is quality.
The use of blockchain opens up a new level of Big Data analytics. Such information is structured, complete and secure, as it cannot be tampered with due to the network architecture.
By analyzing it, algorithms will be able to verify every transaction in real time, which will virtually eliminate digital fraud. Instead of analyzing the records of fraudulent activities that have already taken place, banks can instantly identify and prevent risky or fraudulent activities.
Blockchain technology is applicable not only to the financial sector. Immutable records, audit trails and confidence in the origin of the data – all of this applies to any business area.
Already, companies are introducing blockchain in food trade, and on the other hand, they are studying the prospects for technology in space exploration. Future Big Data and blockchain solutions are expected to radically change the way we do business.
Machine learning
Today, machine learning is being introduced in many industries to automate business processes and modernize the economic sphere. The concept provides for the training and management of artificial intelligence (AI) using special algorithms.
They teach the system based on open data or lessons learned. Over time, such an application is able to predict the development of events without explicit human programming and hours spent writing code.
For example, using machine learning, you can create an algorithm for the technical analysis of stocks and their estimated prices.
Using regression and predictive analyzes, statistical modeling and action analysis, experts create programs that calculate the time of profitable purchases in the stock market. They analyze open data from exchanges and suggest the most likely course of events.
When working with Big Data, machine learning performs a similar function: special programs analyze impressive amounts of information without human intervention. All that is required from the operator to “teach” the algorithm to select useful data that the company needs to optimize processes.
Thanks to this, analysts create reports in a few clicks of the mouse, freeing up their time and resources for more productive tasks: processing results and finding the most effective strategies.
In a fast-paced world where customer expectations are ever higher and human resources are more valuable, machine learning and data science play a critical role in the development of the company. Digital technologization of the workflow is vital to maintain a leading position in a competitive environment.
Big data in business
Everyone who deals with big data can be roughly divided into several groups:
- Infrastructure providers – solve the problems of data storage and preprocessing. For example: IBM, Microsoft, Oracle, Sap and others.
- Dataminers are algorithms that help customers extract valuable information. Among them: Yandex Data Factory, Algomost, Glowbyte Consulting, CleverData, etc.
- System integrators are companies that implement big data analysis systems on the client side. For example: “Force”, “Croc”, etc.
- Consumers are companies that buy hardware and software systems and order algorithms from consultants. These are Sberbank, Gazprom, MTS, Megafon and other companies from the branches of finance, telecommunications, and retail.
- Developers of ready-made services – offer ready-made solutions based on access to big data. They open up the possibilities of Big Data for a wide range of users.
The main suppliers of big data in Russia are search engines. They have access to massive amounts of data, and in addition, they have a sufficient technological base to create new services.
GoogleIn the business intelligence market since 2012, when the company launched Google BigQuery, a cloud service for real-time Big Data analysis. A year later, it was integrated into Google Analytics Premium, the paid version of the counter. Google recently introduced Cloud bigtable Is a scalable, cloud-based database service.
Yandex
Most of the company’s services are based on big data analysis: a search algorithm based on Palekh neural networks, machine translation, spam filtering, targeting in contextual advertising, traffic and weather prediction, speech and image recognition, and self-driving car control.
For some time there was a separate division in Yandex. Yandex Data Factory which rendered consulting services large companies. But later this structure was introduced into the search department.
Mail.Ru Group
The Mail.Ru Rating web analytics system is the first project to use big data processing technologies. Now Big Data is used in almost all services of the company – Target.Mail.Ru, Mail.Ru Mail, Odnoklassniki, My World, Mail.Ru Search and others.
Using big data analysis, Mail.Ru targets ads, optimizes searches, speeds up technical support, filters spam, studies user behavior, etc.
Rambler
At first, the media holding used big data only in search, and then the data mining direction appeared in the company. Rambler uses technologies to personalize content, block bots and spam, and process natural language.
Benefits of using technology in business
- Planning is simplified.
- The speed of launching new projects is increasing.
- The chances of the project being in demand are increased.
- User satisfaction can be measured.
- Easier to find and engage your target audience.
- Interaction with clients and contractors is accelerated.
- Integrations in the supply chain are optimized.
- The quality of customer service and the speed of interaction are increasing.
- Loyalty of current customers is increasing.
Interest in big data technologies in Russia is growing, but Big Data has both drivers and constraints.
| Drivers | Limiters |
|---|---|
| Strong demand for Big Data to improve competitiveness through technology opportunities | The need to ensure data security and confidentiality |
| Development of media processing methods at the global level | Lack of qualified personnel |
| Implementation of the sectoral plan for software import substitution | In most Russian companies, the volume of accumulated information resources does not reach the Big Data level |
| The trend of using the services of Russian providers and system integrators | It is difficult to implement new technologies into established information systems of companies |
| Creation of technoparks that contribute to the development of information technologies | High cost of technology |
| State program for the implementation of grid systems – virtual supercomputers that are distributed across clusters and connected by a network | Freezing of investment projects in Russia and outflow of foreign capital |
| Transferring servers that process personal information to the territory of Russia | Growth in prices for imported products |
Big Data in Marketing
Why Big Data in Marketing? Analysis of arrays of information about the company opens up new opportunities:
- Understand how a business works in numbers.
- Study your competitors.
- Get to know your customers.
Marketing will be able to reach a new level of understanding and analytics, which will reduce costs and increase sales.
Benefits of using technology in marketing
- Creation of accurate portraits of target consumers.
- Predicting consumer reactions to marketing messages.
- Maximum personalization of advertising messages.
- Increase in cross-selling, repeat sales, remarketing.
- Search and determination of the reasons for the popularity of popular goods and products.
- Improving products and services, increasing customer loyalty.
- Improving the quality of service.
- Fraud prevention.
- Reducing costs in dealing with suppliers and customers.
Thanks to the special services of big data technology, Big Data can be used in any marketing department, including medium and small businesses. You do not need to install and maintain expensive equipment and maintain a specialist.
