#bigdata

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.

Big Data is changing the healthcare system

Big Data technologies have already exceeded the scope of IT subdiscipline and began to penetrate deeper into different organizations life. And it is difficult to imagine the sector where data could be useless and unnecessary tool. Manufacturing, commerce, finances, education, hospitality, healthcare – this is a partial niche list where data can be used for effective operations. If we’re talking about such important life sector like medicine – Big Data usage is vitally important.

Big Data role in medicine is essential. As expectations in healthcare could become fatal it is critical to get all necessary data on an urgent basis. With the help of Big Data, it is possible to make shorten time for research and results reception that gives an opportunity to develop effective treatment or preventive therapy protocols. Also, doctors have a possibility to get, analyze and give treatment of their patients over a distance.

Let’s consider 3 main Big Data affected areas in healthcare:

1. Research assistance

Currently everybody generates a huge amount of data every day (metadata, text data, video data and location data). All this data is useful especially medical and healthcare data. There are hidden patterns, correlations and relationships. However, it is impossible for human to investigate petabytes of information for logical patterns extract. The life saver in this situation is AI that allows to process big data set, separate all unnecessary and find possible correlations. Big Data plays an essential role in cancer research helping to find answers for questions about cancer treatment, causes if occurrence and mitigating factors that were previously unknown.

2. Changes in insurance

With wearable technologies people can easy monitor heart rate, activity level and sleep cycles etc. This information could be valuable not only for someone but also for doctors, insurance companies and hospitals. Thus, the issue of choosing more suitable insurance in each case could be solved. An open question is an invasion of privacy and personal information usage by insurance companies. Big Data will change the industry, but what restrictions and regulations may be passed remain unseen.

3. Telehealth

Big Data and AI are created for each other and perfectly work together. Without AI it would be difficult to understand, analyze and organize big data. And without big data it would be difficult to develop training data that accelerate AI. Besides help in the doctors’ exploratory activity big data and Ai are used for telehealth applications. Such applications with personal AI assistant give people who have chronic illnesses an ability to get medical advisement and help every day. Also, with the help of these tools it is easier for doctors to process big data that simplify telediagnosis and examination.

Big data makes changes the health care system for better: doctors can detect patients’ condition exactly, get research results on an operational basis and analyze them. Such changes give an ability to continue with an effective treatment keeping to a minimum «guesses» and precious time commitment for them. But collaboration of data scientist and medical and research centers, hospitals and other healthcare companies allows to achieve such results qualitatively and rapidly.

You have a possibility to make the first step to organize the process of data extracting, analyzing and management contacting DataLabs team for advice.

10 Big Data trends that accelerates business development

Technological advance and achievements instigated huge amount of data appearing. Many of us even don’t suppose that we’re producers of them. Every search request generates data and as a result we produce data in a few days more than in a decade in history. Received data has not to be just stored, it has power and effect if it is manipulated. Data is corporate assets that are used by different organizations to improve operative business. The reduction to practice of artificial intelligence (AI), machine learning (ML), IoT and other technologies improved the quality of data-driven business decisions. And it is not a hyperbolic affirmation «data makes business smarter».

Let’s get a view of 10 big data trends that accelerates business development:

1. Accessible AI

Big data that company has can generates value if it is processed using advanced technologies. For effective analyzing companies use: AI, ML and neural networks to forecast. However, to minimize technical readblocks there is cloud sources trend that essentially simplify data access. The hybrid cloud is capable to provide with more flexibility and possibilities to deploy data by moving processes between private and public clouds.

2. Continuous intelligence

Gartner explains continuous intelligence as a design pattern in which real-time analytics is integrated into business activity, processing current and historical data to prescribe actions in response to business events. Continuous intelligence use technologies like event stream processing, business rule management, ML, optimization and advanced analytics.

3. Advanced Analytics

Advanced Analytics is a part of data science. It uses ML and AI to improve analytics across all data life cycle (from the preparatiom method to analyzing). This technology promotes development of business flexibility, rapidly and credible information gaining, data sharing, and cutting time for information extracting and understanding. More detailed information in the entry «More analytics – more possibilities».

4. DataOps and self-service analytics

DataOps is a newish term, but it already is in favor in IT world. DataOps is a technology mix of continuous integration designed to afford actual data for every process member rapidly and fluently. With the help of this companies have a possibility to rise speed and improve data management quality.

Self-service analytics is a kind of analytics whereby users have an opportunity to make data requests and generate reports by themselves. Self-service analytics implementation allows to receive quick and exact result, simplify information sourcing all across chain.

5. Data-backed tools

Currently data for business processes is generated from all sides. The essential accelerator of this became IoT devices influence. Therefore, it instigated problems appearing. The fact of the matter is that data passes a long way to the centralized source.  But technologies allowed to avoid crisis. The edge computing conception allows to hold data in the local storage device near the IoT device for better data management.

6. Smart chat-bots

Chat-bots became an indispensable contact source between the business and consumers. With the help of this tool companies process customers’ requests and establish more personalized cooperation with them herewith reduce a real staff necessity. Chat-bots are based on big data as a connection source needs large data sets to work in the personalized format. Big data is the main source of the information transfer to chat-bots.

7. Intelligent security

One of the biggest business problems is a security threat. Using big data in a corporate security strategy it is possible to get essential profit. As big data contains all information concerning previous cyberattack attempts, phishing attacks, ransomware etc. it is possible to forecast, prevent and cushion an impact of future attempts.

8. Big data as a service (BDaaS)

BDaaS is a suite of big data analyzing methods and cloud computing platforms with the help of which it is possible to manage big data in cloud and provide their access at any time and for every user. Also, BDaaS promotes cost and time saving to deploy big data projects.

9. Dark data

Dark data is a part of big data that stays in the background. It is collected in consequence of specific network operations that aren’t covered by analytics. However, such data can have more value as one can imagine. Also, dark data can create a business security threat. That’s why it’s necessary to recover it or use correctly.

10. Cloud usage

Companies show a high interest in cloud technologies. This may be due to the fact that they able to change management methods of information and technologies business resources, providing their efficiency, security, flexibility, safety, automation, accessibility and optimization.

Big Data Analytics can improve customer experience

Currently the world is composed of data that every day is being produced by businesses as well as individuals. According to a tally, in 2020 each Internet user created 1,7 MB of information every second, Google processed more than 40 000 queries, about 3 million emails were sent by people around the world. And those rates continue to rise rapidly.

This amount of data processing requires advanced analytic solutions, specifically big data analytics.

What is this?

Big Data Analytics (BDA) – is the complex of advanced analytic methods that are focused on the work with big and different size data (from terabytes to zettabytes) to uncover valuable information from structured, semi-structured and unstructured data sets from different sources. By using this tool, it is possible to uncover hidden patterns, unexplored correlations, market trends and customer preferences. BDA comes with technologies and methods like predictive analytics, statistic analysis, text analysis, data visualization, machine learning, artificial neural networks, spatial analysis, Data Mining, pattern recognition, simulation etc.

World data amount stupefies but opens huge capacities for business. For instance, in working with customers and their behavior. By collecting and analyzing data for each client it becomes possible to provide an individualized offer upon request. As a result, a company becomes more competitive, customer experience becomes higher and revenue is growing.

Some examples how BDA can help to improve customer experience:

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