From water systems and hospitals to public transport and public relations management, the growth in the volume and importance of data plays a critical role in today's world. Data has long determined the success of businesses, but now it has also come to be a vital element in the smooth running of social, commercial, and government infrastructures.
The IDC predicts that global data volume will grow to 163 zettabytes (or a trillion gigabytes) by 2025. This is ten times more than in 2016 when 16.1 zettabytes of data were generated in 12 months. All this data will open up unique experiences for ordinary users and new opportunities for commerce.
By 2025, the average person around the world will interact with devices nearly 4,800 times a day - about one contact every 18 seconds.
Now imagine that all the information created and transmitted to different systems needs to be stored somewhere and managed intelligently.
Understanding Big Data
Let's start with the definition of data science. This discipline studies the challenges of processing, analyzing, and presenting data in the digital age. The term was coined by Peter Naur, author of 'A Basic Principle of Data Science'. It was in the early 1980s when data became increasingly digital, leading to the development of a new era.
In the past, the increasing amount of data might have been held by just a few big players in the market, or by the government. But after 2007, with the advent of the iPhone, the rise of Google, the advent of Hadoop, and other technologies, coupled with cheaper ways of handling data, the world faced incredible amounts of data.
Undeniably big data streams are called Big Data, thanks to Clifford Lynch, editor of the journal 'Nature'. Yes, this amount of information has existed before, but it has become more prominent nowadays.
Today, big data has several characteristics:
- Volume. Starting at 150GB per day.
- Speed. The speed at which data arrays are accumulated and processed is regular, requiring real-time processing.
- Variety. Big data can be diverse: structured, unstructured, or mixed.
- Veracity. Not only the data itself but also the results of data analysis must be trustworthy.
- Variability. Variability in data can occur due to various factors, making it difficult to analyze.
- Value. Each particular dataset has its own value and complexity.
Big Data Methods and Technologies
Big data require different processing than traditional data. The following methods stand out:
Descriptive analytics uses mathematical functions, and current and 'historical' data analysis results answer questions about what has happened in real-time. The simplest example is a year's revenue data collected in Google Analytics.
Predictive analytics appears to be a way of predicting the possible outcomes of events based on previously collected and analyzed data. In the stock market, for example, predictions are made about the rise or fall of a company's share price.
Prescriptive analytics is similar to predictive analytics in that it can also show possible future scenarios. This allows business owners to see the weaknesses of the project and learn how to avoid them in the future.
Diagnostic analytics analyzes large amounts of data to answer why something has happened. It identifies anomalies or connections between events or other variables.
The tools and technologies required for data analysis also differ from the traditional ones and include data mining (extracting previously unknown data from other datasets), specialized software, neural networks, artificial intelligence, machine learning, visualization of complex data, etc.
Big Data in Healthcare
What is big data in healthcare?
Big data in healthcare refers to the collection, storage, and analysis of large and complex data sets from a variety of sources, such as electronic medical records, medical images, genomics, and certain mobile medical devices. This data is used to improve patient outcomes, reduce healthcare costs and develop new treatments and therapies.
What are the benefits of big data in healthcare?
The benefits of using big data in healthcare are closely linked to its applicability.
Thanks to predictive analytics, large amounts of data collected in a personalized way make it easier to identify patients at high risk of developing certain diseases, allowing for early intervention and preventive treatment. The same applies to personalized treatment plans based on genetics, lifestyle, and medical history.
The company Foundation Medicine uses big data to create personalized treatment plans for cancer patients based on their genetic profiles. The company's genomic database contains information on more than 400,000 tumors, which is used to identify targeted treatments that are most likely to be effective for individual patients.
Pharmaceutical companies can significantly improve the effectiveness of clinical trials, as big data analytics can identify the groups of patients most likely to respond to treatment.
Flatiron Health's database contains information on more than 2.2 million cancer patients, which is used to identify specific patients so they can conduct clinical trials more efficiently.
Population health management, particularly in relation to the global spread of disease, is also becoming more accessible through big data: tracking the health status of entire populations, identifying trends, and developing targeted interventions to improve health outcomes.
Big Data in Marketing
What is big data in marketing?
Big data in marketing is already essential to the successful development of strategies, advertising campaigns, products, and content that are in demand in the market. In times of increased competition, it is necessary to make clear decisions based on concrete data, which Google Analytics, Google Search Console, and other tools provide
How is big data used in marketing and advertising?
What will you be able to do after implementing big data marketing tools?
Improved targeting. The results of big data analysis can be used to determine the most relevant audience for a particular product or service.
Netflix's streaming platform analyzes vast amounts of information to improve content recommendations and reduce user churn. The analysis uses search history, browsing history, preferences, demographics, location, and more.
Personalize marketing and advertising campaigns. Creating personalized marketing campaigns based on individual preferences, behaviors, and demographics is possible with data collected from social media, websites, and mobile apps.
Starbucks uses big data to personalize its loyalty program and offer targeted promotions to individual customers, increasing their engagement.
Measure the effectiveness of campaigns and promotions. Website analytics tools not only give you the data you need in an easy-to-understand format, but also track trends, analyze the effectiveness of promotions, and give hints about changes in real-time.
Big Data in Business
What is big data in business?
Big Data is used in business to analyze complex data sets from a variety of sources, such as customer interactions, social media, and online transactions. The information gained from analyzing this data can help companies make more informed decisions, improve the customer experience, optimize operations, and identify new business opportunities.
Procter & Gamble uses big data and the results of its analysis to predict consumer demand. Once the key attributes of a particular set of products or services have been classified, you can model the relationships between these attributes and the commercial success of offers. Based on this data, decisions can be made to create new products.
How big data is stored and managed in organizations?
Big data is stored in organizations using a variety of storage systems and technologies. These include data warehouses, Hadoop clusters, cloud storage platforms, and NoSQL databases. The choice of storage system depends on factors such as the volume and variety of data, the performance required, and the resources available.
In general, storing such volumes of information requires powerful computing equipment that can be purchased and managed directly on the organization's premises. However, the more financially- and resource-efficient option is to rent a dedicated server in the country or region that is most profitable for the business. Read more about the benefits of a dedicated server in our article.
The ideal solution for large-scale projects. Impeccable protection, high performance, and flexible settings.
Renting a server with the right configuration and in the right location is ideal if you want to manage large amounts of data competently and do not want to spend time training your staff in administration. Just choose the right technical specifications and let your business grow.
How to implement big data in an organization?
Is it necessary to use big data in e-commerce? The use of big data is not mandatory, but it can bring significant benefits to e-commerce businesses. It can help businesses gain a competitive advantage and increase sales and profitability.
Step by step, the use of big data in business can be described as follows:
- Identify the problem or opportunity that needs to be proven or disproven using big data.
- Collect and store data from a variety of sources, including customer interactions, social media, sales results, etc.
- Use big data analytics tools and techniques to process the data, extract relevant information, and identify patterns and trends.
- Interpret the results of the analysis and use the insights to make decisions based on the data.
- Propose an idea that has the potential to improve the efficiency of the business.
Best practices for using big data:
- Align business challenges with the use of big data. Determine the need and importance of big data research and analysis specifically for your project.
- Use all available sources of information to compensate for the lack of certain skills or knowledge, and hire qualified personnel. Consider this investment at the planning stage.
- Use the data you already have and the data you plan to acquire through new technologies. Collect and analyze all information to produce more reliable results.
- Implement cloud technologies that allow you and the specialists involved to use more resources and tools. Develop a strategy for managing and allocating cloud infrastructure resources.
Other Applications of Big Data
What is big data in AI?
The use of large and complex data sets to train machine learning algorithms and improve the accuracy and efficiency of AI systems is now commonplace among major IT players. By analyzing large amounts of data, AI systems can learn to recognize patterns, make predictions and improve their performance over time.
In general, machine learning and deep learning are inextricably linked with the term 'big data', as it is the vast amount of data that makes these technologies possible.
How is big data used in banking?
Banks, stock markets, and the financial sector all generate huge amounts of information that need to be stored, structured, analyzed, and managed intelligently.
Big data can be used to identify patterns and anomalies in customer transaction data, which can help banks detect fraudulent activity and prevent financial loss. More generally, big data can be used to personalize the customer base and improve offerings.
Risk management also becomes more predictable with predictive and prescriptive analytics.
What is big data in cloud computing?
Big data in cloud computing is not just about using cloud platforms and technologies for storage, but also about analyzing big data on a regular basis. Cloud computing allows organizations to scale storage and processing capacity on demand, making it easier and more cost-effective to manage big data.
Is the use of big data in HR and recruitment applicable?
Recruitment, employee engagement, performance management, and related areas also require the analysis of large amounts of data.
Specialized programs, applications, and social networks are used to optimize the processes HR professionals utilize. They can assess employee engagement and teamwork, identify growth points for each employee, and resolve inclusion or unfair pay.
How do IoT and Big Data relate?
In addition to people, IoT (Internet of Things) devices generate a lot of data. IoT refers to a network of physical objects, such as devices and sensors, that are connected to the Internet and can communicate with each other. These objects collect and transmit data about various aspects of the environment, including temperature, humidity, pressure, location, and more.
The volume, speed, and variety of data generated by IoT devices can be impossible to store and analyze using traditional methods. This is where big data technologies are needed.
This symbiosis increases the likelihood of success and wider adoption of the smart home and smart city concept.
How can big data be used in law enforcement?
More detailed analysis of criminal activity, identifying weaknesses in legislation, and generally improving the level of security in a particular area are all possible thanks to big data.
Visualization technology can be used to create maps of criminal activity, predictive analytics can anticipate likely patterns of crime, and the detection of violence can occur through social media analysis and the use of investigative analytics.
What is the purpose of big data in the travel industry?
In the tourism industry, big data can be used to make personalized travel offers, analyze the prices of different means of transport in order to select the best option according to various criteria, and assess the seasonality of certain destinations and thus their attractiveness for tourists.
In general, the applications of big data and related technologies are limitless. Even if in today's reality some of the possibilities of Big Data are not available to all, the importance and value of information are growing every year. Information is an indispensable resource for achieving goals.