#bigdata

Data Science and Big Data: characteristics, benefits and differences

Data Science and Big Data are interrelated concepts. Both of these concepts are key to using data to drive decision, innovation and value. The active development in the field of data implies the presence of data science and big data analytics. Data Science and Big Data, although related, are different concepts in the field of data analysis.

The focus of Data Science is on the application of statistical and machine learning methods to extract information from data and solve problems. This process includes collecting, cleaning, researching and interpreting data. Big Data refers to large and complex data where the capabilities of traditional data processing methods are not enough.

The key differences between Data Science and Big Data:

  1. Concept and characteristics

Data science is an interdisciplinary field that integrates scientific methods, algorithms, and systems for extracting information from structured and unstructured data. Data is a key source for analysis and decision making. For this, statistical methods and machine learning algorithms are used.

Big Data includes structured (databases), semi-structured (xml) and unstructured (texts and images) data from many different sources. This technology allows for preliminary cleaning and processing, as well as analysis of huge amounts of data in real time.

  1. Scope and methodology

Data Science uses statistical analysis, machine learning, data visualization, and exploratory data analysis to understand data patterns, predict, and find solutions.

Large datasets in Big Data are processed using infrastructure technologies. These include distributed storage and data processing systems. Parallel processing, scalability, etc. provide high-quality control of large volumes and high data transfer rates.

  1. Goals

The goal of Data Science is to represent, extract knowledge and solve complex problems using data.

The goal of Big Data is to efficiently store, process and analyze huge amounts of data.

  1. Usage

Data Science has been widely used in business intelligence to analyze customer behavior, market trends, and sales data. In healthcare, this technology is responsible for analyzing patient data for diagnosis and predicting treatment outcomes. Data Science also helps in clinical decision making and disease outbreak detection. In financial institutions, it helps to detect fraud, simulate risk and make informed investment decisions. The ability to analyze human language makes it possible to use applications such as chatbots, voice assistants, and machine translation.

Big Data enables insights into customer preferences, interests, behaviors, and buying patterns to improve products and inventory management, optimize pricing strategy, increase efficiency, and personalize marketing campaigns. This technology is used to analyze social media data, including user interactions, sentiment analysis, etc.

  1. Benefits

The main advantage of Data Science is the ability to make informed decisions based on the information extracted from the data. This happens with the help of statistical analysis, machine learning methods and data visualization methods. Offers a wide range of applications as well as cost savings through efficient data management.

The main advantage of Big Data is the ability to process and analyze huge amounts of data, as well as gain valuable information and make decisions based on data. Provides a platform for advanced analytics and machine learning applications.

  1. Disadvantages

The use of Data Science requires qualified specialists in the field. Pre-processing and cleaning of data requires significant time and resource costs. Ethical issues can also arise because Data Science deals with sensitive information.

Big Data also requires certain skills and experience in the field. Security and protection issues can be a problem when dealing with sensitive information.

Data impact on marketing campaigns effectiveness

Information on the Internet appears at an exponential rate. In this regard, search engines had to figure out how to solve the problem of content management on the Internet. The solution has been found, and now search engines are transforming content into data that is easy to quantify and analyze. So, the user can receive relevant links for their search queries. Also, users can see snippets that meet their needs without having to click on the link.

Businesses use these opportunities to improve their services. Big Data and content analytics tools based on Artificial Intelligence provide new opportunities for marketing, namely:

  1. Better results understanding

The main purpose of using Big Data in marketing is to understand the target audience. Understanding the characteristics, preferences and behavior of users in the digital space makes it possible to increase the effectiveness of marketing campaigns. This, in turn, allows to convert clicks into sales.

SEO companies use Big Data to get more detailed information about their customer base. AI-based analytics tools allow to provide useful information that can be used to optimize company services. For example, working with relevant words in order to determine the specific intentions and demographics of the user. This allows to create better content, which helps to increase the number of conversions on the site.

  1. Transition to targeted marketing

Modern analytics tools are becoming more and more precise and allow processing a huge amount of data in a short period of time. Along with this, companies are increasingly willing to use targeted marketing. Target marketing removes the need to spend millions to increase reach without the guarantee of an increase in conversion rate. At the same time, special analytics tools use AI and Big Data to identify specific users who are interested in the company’s products or services.

At the moment, companies are often looking for a Big Data expert to help them better understand the data and improve their marketing strategy. This allows to make a targeted offer to users who are already interested in a particular product or service.

  1. Data from social networks

A large amount of data allows for a qualitative analysis of trends and patterns. Social networks contain a huge data amount. Users number of popular social networks (Facebook, Twitter, Instagram etc.) is increasing every day, which makes it possible for businesses to make sales of their products and/or services. AI allows to analyze user behavior and determine which product they are interested in, which platform they searched on, etc. Search information is collected by big data systems through autonomous analysis systems. Such information may be used to target advertisements from companies that offer a similar product.

Big Data and Machine Learning as indispensable technologies of the modern world

The most important and indispensable technologies of our time are Big Data and machine learning. Machine learning automatically trains computers based on data. It is possible by transferring data to the computer, which it uses to increase its performance while performing tasks. Big Data is the main data source for machine learning, so their connection is critical.

Big Data is a large data amount that is difficult to analyze and process. Also, it is difficult for users to understand and use such large amounts of data. Machine learning applications must process large data amounts quickly and efficiently. However, machine learning algorithms can simplify this process by automatically detecting patterns in the data.

These 2 technologies often complement each other. When used together, machines can be taught to recognize patterns in complex data sets and make accurate predictions. Therefore, modern companies are increasingly implementing solutions for working with data.

Big Data

Data can come from a variety of sources, including social media, internet traffic, sensor readings, and customer behavior. Big Data is used for various purposes, such as improving marketing performance by analyzing website visitors’ behavior or to predict customer needs, etc. However, the key purpose of using Big Data is to increase company productivity.

Big Data is widely used in various fields of activity. The healthcare sector is an active user. Physicians have an access and a possibility to analyze patient data. They allow to track the symptoms of patients, identify non-obvious patterns, make a more accurate diagnosis and effectively treat patients.

Machine learning

Machine learning is a field of Artificial Intelligence that provides training for computers using data. Very often, companies use this technology to predict customer behavior. For example, machine learning algorithms allow to analyze the previous behavior of the client and determine the likelihood of his re-applying to the company.

Also, machine learning algorithms are able to detect traces of fraud, which is also a common purpose for its use. By identifying patterns in data that indicate fraud, companies can prevent high investigation costs and fines.

Using machine learning and Big Data is beneficial. Big Data provides huge amounts of training data that is essential for machine learning algorithms. This contributes to the creation of more accurate forecasts. Also, Big Data improves the accuracy of machine learning algorithms by providing additional information about the data. For example, the analysis of historical data about stock prices helps to determine a more accurate forecast price.

These technologies are interconnected as Big Data can be used to train machine learning models. This, in turn, facilitates the discovery of patterns in the data, which allows to make accurate forecasts, better understand customers, conduct qualitative analysis and increase the overall company efficiency.

Use of Big Data around the world

In the world of Big Data, there are leading countries that use and implement Big Data the most to achieve better results. This plays an important role in the evaluation of applications and software. Many organizations collect, store and evaluate information around the world for various purposes. First of all, Big Data covers finance, insurance, marketing, construction, transportation, consumer goods, trade, communications and education. Below are the leading countries in Big Data implementation.

USA

The users of Big Data are organizations from different fields. They use different kinds of devices, technologies and sources (mobile phones, social media, websites, etc.) to do their daily work. This results in the regular generation of large data amounts. The data obtained is analyzed and used to meet various needs (forecasting, researching the current situation, etc.).

India

The rapid technology development and the growth in the number of Big Data services can lead India to a leading position in Big Data analytics in the world.

Japan

The Big Data industry in Japan is developing and expanding. A key element of the Big Data industry here has been the rise in the use of machine learning and social media.

Canada

Corporate Big Data services and cloud integration services have had a high and scalable impact. They pull behind them large-scale investment projects that can provide important information.

China

The Big Data industry has experienced significant growth in recent times and will continue to grow. Technology integration policy is the main reason for the rapid growth of Big Data in China.

South Africa

The reason for the huge Big Data market in South Africa has been the development and use of social media, Artificial Intelligence technologies, the Internet of things and machine learning, and cloud technologies. Big Data solutions are widely used by South African companies.

Saudi Arabia

Opportunities for the implementation of machine learning, the Internet of things, cloud computing and software services have accelerated and expanded the development of Big Data. Big Data is used by various organizations, regardless of their size and scope of activity, to cover their needs and achieve goals.

Great Britain

The UK has seen a significant increase in Big Data services adoption and Big Data technologies development. The telecommunications industry is one of the leading and actively using Big Data. Companies from other industries also try to explore and implement Big Data technologies.

Big Data use in different business areas

Data value has increased dramatically in recent years. Every business in every field of activity owns data and uses it for the main purpose – to increase revenue. It is no longer possible to imagine a business that makes decisions without relying on data but relies only on intuition. And this is logical. Data reflects a real and predictable picture, which allows to make informed and effective decisions.

Below are the top 10 industries that use big data applications.

  1. E-commerce

One of the reasons for the rapid development of this industry is Big Data. They play a key role in the process of improving the user experience. Advances in technology have made it possible for e-commerce sites to use data in almost every activity (getting recommendations based on a customer’s preferences, showing specific products that match a customer’s past purchases, etc.).

  1. Education

The higher education system is actively using Big Data. This allows to track the entry of each student into the system, find out and analyze the amount of time spent on different pages in the system, analyze students’ progress etc. Also, Big Data allows to evaluate the level of teachers’ efficiency. So. it’s possible to analyze and ensure an effective educational process and interaction between teachers and students.

  1. Media and communications

Users want to consume information in different formats and on different devices. Consumer data mining, analysis and usage makes it possible to understand the patterns of media content use in real time.

  1. Health sector

Data is very actively used in the healthcare industry. This made it possible to solve many problems in this area, as well as improve the economy. Data has made it easier to conduct research, identify the chain of diseases and their spread. Also, historical data and medical information usage has helped discover new medicines.

  1. Gaming

The gaming industry is also an active user of Big Data. They provide an opportunity to increase income by providing information about trends and player preferences. This, in turn, allows to make relevant offers to players.

  1. Financial sector

One of the most common financial problems faced by many companies is fraud. Big Data helps to solve this problem. With its help, financial institutions monitor activity in financial markets, and network analytics allows to identify illegal trading activities in financial markets.

  1. Manufacturing and natural resources

Big Data in this area is used for predictive modeling in order to make better decisions.

  1. Insurance

Big Data allows to provide customers with transparent product information. Insurance companies have the ability to predict customer behavior by analyzing data from social networks, GPS-enabled devices, video recordings from CCTV cameras, etc. Also, Big Data can increase customer loyalty.

  1. Human resource management

Big Data allows to explore information on certain parameters. Thus, recruiters have the opportunity to study candidates’ profiles and resumes, analyze information and select the most suitable specialists for a specific position.

  1. Energy

Smart meters collect data almost every 15 minutes. Such detailed data allow for analysis of utility consumption. This, in turn, makes it possible to improve customer feedback and control over the use of services.

Machine Learning & Big Data

Among other modern terms and concepts, the most relevant are machine learning (ML) and Big Data. These 2 terms are often used in conjunction, although they have a fundamental difference. And it is important to understand this difference during a data strategy development.

The similarity between machine learning and Big Data is that both terms refer to the field of theoretical academic research and practical data-driven business applications. It is a scientific discipline that studies information and use cases.

Data is the main engine of technological progress. It helps to create new tools and platforms to change the world through analytics, more accurate modeling and forecasting. The development of the Covid-19 vaccine is a great example of the data importance in today’s world. Usually, it took up to 10 years to develop a vaccine. However, over the past decade, the ability to collect and process data has expanded significantly. It has significantly accelerated the pace of vaccine development. If this pandemic had happened in 2010, it would have taken a lot longer to solve this problem, just because technologies for deep data understanding were in their infancy.

This situation is made possible by both Big Data and Machine Learning. Let’s make sense of the terms.

Big Data is a collective term that includes a huge amount of ever-growing information, as well as tools, methods and technologies that have been developed to work with data, including Machine Learning. With the Internet transformation into a daily use tool, Big Data has begun to be identified as a powerful tool. Big Data isn’t just about size. Data definition as big assumes the presence of 3 characteristics («3 V»):

Machine Learning is a type of computer algorithm. It can be viewed as part of Artificial Intelligence (AI). A fundamental aspect of intelligence is learning. Machine learning is involved in creating programs that help to perform better taking into account an ever-growing data amount.

It is important to understand the difference between supervised and unsupervised ML. Supervised learning is a Machine Learning technique that includes tagged learning algorithms that lets you know immediately how well an operation has been performed. Unsupervised learning is a method of Machine Learning, as a result of what the system under test spontaneously learns to perform tasks.

Big data and Machine Learning are intertwined. The best results are most likely to be obtained by using the most appropriate ML and Big Data processes.

However, if the business does not work with Big Data, Machine Learning is unlikely to be needed. Its main advantage is the extraction of value from datasets that are difficult for classical computer and statistical analysis. For example, for a static dataset that fits into an Excel worksheet, the ML implementation will not be justified. It is advisable to use this tool in the case of working with unstructured data that cannot be understood using tables (text, graphic, sound data etc).

Big Data – Top 5 characteristics

The modern world is made up of data. The daily amount of data generated is 2.5 quintillion bytes (Google search, online shopping, smartphones usage, pictures, videos review, etc.). Companies’ success largely depends on how well they work with their data.

The term «Big Data» appeared because of data amount increase. But how do you know exactly if the corporate data is big? There are 5 main characteristics that define big data: volume, velocity, variety, veracity, value.

Volume

The first Big Data characteristic is volume. Every minute a huge amount of data is generated, which equates to the amount of data generated from the beginning of time to the year 2000. Data amount that needs to be processed every day reaches terabytes and petabytes. The explosive growth of data has led to the development of new technologies and strategies. For example, tiered data warehouses that provide secure collection, analysis and storage of information.

Velocity

The speed of generating and moving data is the second Big Data characteristic. Any user action on the Internet creates data that must be processed instantly: sending a message, viewing the feed of social networks (Facebook, Instagram), online shopping, etc. Anyone can represent the amount of data by assessing the number of personal actions per day and adding to this actions of people around the world. Therefore, processing speed is a key characteristic of big data.

Variety

Data can be structured, semi-structured and unstructured. The processing algorithm may differ depending on the data type. In addition to structured data, big data also includes unstructured data: text, images, video, voice files, and other unstructured data that cannot be fit into the frames of a regular spreadsheet. At the moment, there are technologies that allow to analyze both structured and unstructured data. This allows to take advantage of all the possibilities that data offers.

Veracity

Veracity is the next characteristics of Big Data. Because it comes from a variety of sources, it is important to understand the entire storage chain, metadata, and context to get accurate information. Reliable data drives effective analytics and business excellence.

Value

Data must be transformed into business value. To do this, it is imperative to develop a data processing strategy that combines goals and data that will help achieve them. Effective analysis helps to understand customer behavior and needs, optimize business processes, improve application performance, and be competitive. Regardless of what purpose the data is used for, it should definitely be useful and work «for the business».

Corporate data value directly depends on the strategy

The most important business asset today is data. In addition, data in general is a key component in the global digital transformation, including artificial intelligence, the Internet of Things and other advanced technologies. It is possible to get all benefits from business information resources with the help of a smart approach and data management strategy.

What is Big Data?

Transactional and customer data existed before computers and databases. With computers advent it has been possible to greatly simplify the accessing data process and organize it using spreadsheets and databases. Now users have access to certain data at the mouse click.

Users generate a huge data amount every day. Data amount that is generated every 2 days corresponds to the data amount created from the beginning of time until the year 2000.

This growth is due to the fact that almost every user’s action leaves a digital footprint. Any user action on the Internet, GPS usage, music or weather forecast searching are recorded. Development companies and retailers collect data and use it to achieve competitive advantage. However, it can be done effectively with a big data strategy.

Big data strategy

Investing in analytics and technology must be competent. Before investment planning it is necessary to understand particular business needs. The first and important step is to develop a big data strategy. The strategy provides comprehensive answers about how the data collection process will work in practice and what data type is needed to achieve business goals. The constant data growth makes it more complex. It is possible to link the information assets of business to its goals using a big data strategy.

Not all data that company owns can be equally valuable. Therefore, the strategy development process should start by identifying use cases for the data. 3 – 5 scenarios – the right amount for all size companies. An effective big data strategy must align with the company’s goal. Linking to the strategic goal ensures that the business team has the right focus.

Key use cases for data:

It makes sense to identify several scenario options that will lead to a quick result and several options per year. This approach will help demonstrate the business value of the data.

Developing the strategy should consider the following:

  1. Requirement for data: definition of the data type, collecting and storing method;
  2. Data management: determining the current state of data quality, security, access, ownership, ethics and confidentiality;
  3. Technologies: an appropriate infrastructure to support the entire data management process (collection, storage, processing, analysis, transmission of information);
  4. Skills and opportunities: determining team’s knowledge and skills level, training needs and expert attraction;
  5. Implementation and change management: management support.

Big Data and Business Transformation

During the  time, all new technologies become simpler and more affordable for large-scale use. Now Big Data is going through this phase. As a result, different industries transformation is taking place. Here are some examples of key industries influenced by Big Data.

Retail

In recent years, selling and buying procedures have changed a lot. However, both online and offline store owners use the data to better understand customers, their needs, and comparison with the current offer. This approach ensures an effective operation and allows for huge benefits.

Data analytics is applicable to almost every step of the retail process. By predicting trends, it is possible to determine the demand for a product, optimize the price, determine the target audience, and gain a competitive advantage.

Health care

Big data in healthcare is helping to improve disease detection and treatment, improve life quality and reduce mortality rates. The main Big Data task is to collect as much information as possible about the patient and identify the slightest changes and illness signs at the earliest stages. It prevents disease development, provides a simpler and more affordable treatment protocol.

Financial services, Banking, Insurance

Big Data helps financial companies and banks detect fraudulent transactions. Insurance companies use Big Data to establish fairer and more accurate insurance premiums, improve marketing efforts, and detect fraudulent claims. British insurance company Aviva is offering a discount to drivers for being able to control their driving using smartphone apps and car devices. It allows insurers to observe how safe is driving.

Manufacture

The production process is changing dramatically with the development of robotics and the automation level. Sportswear, footwear and accessories company Adidas is actively investing in automated factories.

In traditional manufacturing, Big Data matters too. With the help of built-in sensors, it is possible to monitor the specific equipment performance, as well as collect and analyze data on its effectiveness.

Education

Now, data is being collected about how people learn. This information is used for new ideas, defining strategies for a more effective learning process, highlighting ineffective areas of the learning process and ways to transform it. In one Wisconsin school district, data was used for almost everything from defining and improving cleanliness to planning school bus routes. The performance data analysis of a particular person in online learning mode leads to the personalized, adapted learning development.

Transport and logistics

There are cameras to monitor inventory levels in warehouses. With the help of data from the cameras it is possible to provide reminds about replenishment. Also, this data using machine learning algorithms can be transmitted to train an intelligent inventory management system. In the near future, warehouses and distribution centers will be almost completely automated and require a minimum of human intervention.

Transport companies collect and analyze data to improve driving behavior, optimize transport routes, and improve vehicle maintenance.

Farming and agriculture

Traditional industries also use data to generate new opportunities. American manufacturer John Deere has applied Big Data techniques and launched several services. They enable farmers to benefit from crowdsourcing real-time data from thousands of users.

Energy

The volatility of international politics complicates discovering and producing oil and gas process. Royal Dutch Shell has developed a «data-driven oilfield» with the aim of reducing the cost of its production.

Hospitality business

Recreational service providers use data to make their customers happier. The main goal is to ensure each room profitability, taking into account seasonal changes in demand, weather conditions, local events that can affect the number of bookings.

Professional services

The professional services like accounting, law and architecture are also changing as a result of advances in data, analytics, machine learning, artificial intelligence and robotics.

For example, accounting software allows to automatically import transactions, track digital receipts and taxes, and automate payroll calculations.

Big Data – big opportunities

Now it is too popular to discuss such term as Big Data. But not everyone clearly understands what it is and which value it has. Let’s figure it out in order.

So, big data is a huge and complex dataset from different sources and constantly growing in volume. 3 main characteristics of big data: high speed of reception, large volumes, variety. Big data is mainly used to solve business problems in consequence of information content depth and width. At the moment, many organizations already work with big data, reaping the full benefits of its usage.

10 main areas where big data is actively and successfully applied:

1. Customer understanding and targeting

At the moment, this ​​business area actively uses big data. The main goal is to understand better customers, their behavior and preferences. Companies gain a more complete picture of their customers by expanding information sets with data from social media, browser logs, text analytics, and sensory data. The main goal is to develop predictive models.

2. Business processes understanding and optimizing

Companies use big data to understand better operational processes and improve its efficiency. For example, companies can optimize their inventory based on forecast data, web search trends, and social media data.

3. Personal indicators assessment and optimization

Big data can be useful not only for organizations, but also for people. For example, a person who owns a smartwatch or bracelet receives certain data every day (number of steps, number of calories consumed per day, activity level, sleep pattern, etc.). The correct this data usage brings benefits to the user.

4. Health care system improving

Big data analytics can decode entire DNA strands in minutes, develop new medicines, and better understand and predict disease patterns. Also, it is possible to track and predict epidemic outbreaks, monitor newborns in specialized departments.

5. Athletic performance improving

Big data analytics are widely used among elite sports. At the moment, it is already developed IBM Slam Tracker for tennis tournaments. Video analytics is actively used, with the help of what it is possible to track individual football or basketball player’s performance during a match. Sensor technology of sports equipment helps to obtain data about the game and improve it. Smart technologies can be used to track the routine of each athlete: his diet and sleeping mode, his emotional state through messages on social media, etc.

The US National Football League (NFL) has developed a platform that allows to make effective decisions by analyzing the pitch condition, weather conditions, statistics of the individual players results.

6. Science and research development

Science and research field is empowered by big data. The European Council for Nuclear Research (CERN) conducts various experiments to reveal the Universe secrets, its origin and existence, generating huge data amounts. Big Data computing power can be applied to any dataset, discovering new opportunities and sources for scientists. Researchers can easier access census and other data to create more accurate picture of public health and social sciences.

7. Machines and devices performance optimizing

Big data analytics enables to create smarter and more autonomous hardware. Big data technologies are used to drive self-driving cars, optimize computers and data warehouses performance.

8. Security improvement

Big data is widely used in this area. Thus, the US National Security Agency (NSA), by analyzing big data, has the ability to prevent terrorist operations. Big data analytics is also used to detect and prevent cyber-attacks, to catch criminals, predict criminal activity, and detect fraudulent transactions.

9. Cities and countries improvement

Big data is used to improve many aspects of life in cities and countries. For example, optimize traffic by analyzing traffic conditions in real time. Big data analytics is also used to transform a city into a «smart» city, where transport infrastructure and utilities processes are combined.

10. Financial markets

Big data is widely used in high-frequency trading. Decision making processes take place using big data algorithms. Share sale is carried out using big data processing algorithms that consider signals from social media, news sites, etc. It allows to buy and sell shares in a matter of seconds.

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