Importance of big data in today’s highly competitive world

Last Updated on November 22, 2022

Today, everything is becoming digital and connected. To succeed in this world people need to embrace technology. More than ever, everybody knows that to stay relevant, they have to use technology to be more efficient and meet demands.

But ­technological transformation is just one part of the equation. An invisible, powerful force is lifting the world to new heights. With each digital interaction, data are generated. Up until now, we didn’t have enough computing power and devices were not collecting enough inputs.

But now, more than 60% of the global population has access to the internet, there are an estimated 13.1 billion connected devices and 6.6 billion smartphones in the world. And all these connected devices generate a lot of data.

In this highly digitalized world we are living, an estimated 2.5 quintillion bytes of data are being generated every day. And new technologies are giving us the necessary computing resources to make sense of these enormous inputs.

We have reached a point where we have lots of computing power and a lot of information. Advances in computer science, especially the development of machine learning and artificial intelligence, and advances in data analytics are giving us the necessary understanding of how to examine inputs and draw conclusions.

What is big data?

Data is any kind of accessible information, whether it’s information written on paper, memorized in our minds, or stored electronically. The word ‘data’ is Latin in origin and comes from ‘datum’.

Data is all around us. It can be found in the things we buy, the places we visit, and the people we interact with. In today’s world, the word is most commonly used when information is transmitted electronically.

With the vast amount of information our digital technologies are generating, we have started to use the word big data. This term refers to large datasets that are too big to process by traditional database management applications.

Different disciplines have their definitions of data.  In the case of analytics, it is a collection of facts and statistics collected for reference, analysis, and research purposes. A data scientist’s job entails collecting information from different sources, analyzing them, and then converting them into actionable insights.

Types of big data

With some much data being generated, it’s normal that there are different varieties of it. The information that we generate are not all the same and are not readily available for immediate usage in analytics. They can be classified into three different categories such as:

Structured

Structured data is information that is well-defined, meaning that it is organized in a way that makes it easy for humans to understand. It is the easiest input to work with and is highly organized with a well-defined structured set of parameters.

Structure inputs are in a standardized format that follows a consistent pattern. It conforms to a structure model and can be easily organized in a formatted repository or database. It is estimated that only 20% of our information are structured and the rest or 80% of them are unstructured.

Unstructured

Unstructured data is information that is not organized in a standard way. It can include text, numbers, images, videos, and any other type of input. This type of information is difficult to process and can be difficult to store, use, and share.

Almost everything we do on a computer generates some kind of unstructured input. It is information that is in different forms and doesn’t follow preset models which makes it difficult to process and stored using a traditional setting.

Unstructured data is often used in business and research to capture information about people, products, and trends. It is also used to develop models and predictions.

Semi-structured

Semi-Structured Data is information that is not completely structured. It is information that does not conform to standard models but has some structure to it. It can be found in many different contexts, including email, social media posts, location, time, and customer deets.

Semi-Structured info can be problematic because it is not always easy to understand or process. For example, mail addresses can be formatted in a variety of ways, making it difficult to automatically identify. Most of the time it can be considered unstructured info with metadata attached to it.

Not all information are readily available for analytics and have to undergo a process called data cleansing to make it readable and understandable. These three terms are paramount in big data as understanding the source of raw data is important because information extraction needs to be efficient.

Why big data is important?

Big data is the new gold rush and those that own it are making progress hand over fist. It is a buzzword that has exploded in the business world in recent years. In essence, it refers to large sets of information (intel) that can be analyzed to identify patterns and trends.

As technology has increased in power, so has the ability to collect vast amounts of information about everything from people’s shopping habits to their health records. The term ‘big’ refers not only to the amount of intel being collected but also the volume of analysis required to make sense of it all.

Analytics is used in a wide range of industries, from healthcare and retail to finance and government. It has become an essential part of our lives, helping us make better decisions about everything from what we eat, to where we live and how we travel.

Here are some impressive numbers from the internet:

• In 2021, an average internet user spend 415.5 minutes online every day
• Around 3.2 billion images are shared every day
• An estimated 720,000 hours of videos are uploaded on youtube per day
• Around 500 million tweets are sent every day
• An average of 95 million images are uploaded and 4.2 billion likes are generated on Instagram daily
• In 2021, on average 319.6 billion emails were sent or received every day
• Google process 8.5 billion search query per day
• An average of 7.5 million blog posts are published every day

These are just some of the common activities from the thousands if not millions others that can be done over the internet. Each and everything single interaction with the internet produces a piece of information, even alike.

Now imagine how much information is being generated every day over the internet from online shopping to ticket booking and location tracking. The rapid growth in big data has led companies to not just collect it but also analyze it.

Big data analytics enables companies to gain insight into their customers’ behavior and preferences, allowing them to target them with personalized offers or tailor products more effectively.

Big data is an overwhelming amount of information that is used for various purposes. It is used to provide companies intelligence for present decision-making as well as future predictions, such as planning for business growth or creating new products and services.

Information analytics involves collecting, storing, and analyzing massive amounts of intel in real-time to create actionable insights into business processes and customer behavior. This also improves decision-making and operational efficiency. It is estimated that insight-driven businesses can take up to USD 1.8 trillion per year from competitors.

The amount of inputs that are produced every day has increased over the past few years, nearly doubling every year since 2010. This trend has greatly changed how businesses operate and how they interface with consumers.

Therefore, businesses need to understand the inputs generated so that they can capitalize on their benefits while limiting risks as much as possible. It is also an extremely important tool by which society is going to advance.

In the past, we used to look at small information and think about what it would mean to try to understand the world, and now we have a lot more of it, more than we ever could before. More info doesn’t just let us see more but also allows us to see new, see better and see different.

The importance of big data is growing day by day because of the sheer amount of insightful knowledge that it can provide. It’s no surprise that 97.2% of companies reveal they are investing in big data and artificial intelligence.

The use of bulk information is a major driving force in the innovation process. Its importance can be seen in many industries, from banking to healthcare and from agriculture to logistics as it improves the efficiency of business operations.

By using analytics, businesses can identify patterns and trends in their business operation and market which enables them to make more informed decisions. This also improves business efficiency by unlocking insights that would have otherwise been missed or unavailable.

While mass information can provide companies with insights into their customers and their operations to improve decisions making and efficiency this ultimately increases speed and effectiveness in terms of response time, competitiveness and service offered.

Technology has allowed organizations to collect information at unprecedented levels, which has also led to an increase in accuracy. Thanks to big data, organizations can now get a more accurate picture of their customers and businesses. The market size is projected to be worth USD 273.4 billion by 2026.

Extracting value from large databases provide businesses with the intelligence they need. Business intelligence is the process of using information and analytics to improve operational performance as well as products and services. The big data analytics market is expected to reach USD 655.53 billion by 2029.

Business intelligence is about extracting value from information to better understand operations and make business decisions. This value can be found in a variety of ways, such as providing insights that help to improve decision-making, increasing efficiency, and effectiveness, or helping to optimize operational processes.

Source of big data

There are many sources of large information. Some common sources are social media, sensors, online surveys, and information collected from industrial and commercial applications. Sources of big data are classified as:

Machine data is any information generated by computers, smart sensors, wearables, cameras, satellites, desktops, mobile, machinery, and many more. It is processed by computers, whether that input is stored on a hard drive, in a database, or some other form.

Social data is collected and analyzed by social media platforms and other online platforms. It includes information about users’ activities on these platforms, such as posts, comments, likes, tweets, videos upload, and shares.

Transaction data is information about transactions whether online or offline that have taken place on a platform. These include information such as types of transactions, time, payment method, product detail, location, and other relevant information.

Cloud data is information stored in the cloud, meaning it is stored on a physical server from afar. It can be accessed from anywhere in the world and used for a variety of purposes, such as storing files, making presentations as well as processing, analyzing, and tracking inputs.

Web data is the information that is collected and stored by websites. This information can be anything from the names and addresses of visitors to the pages they view.

Internet of things data is collected from or transmitted by devices that are connected to the internet. The internet of things is a network of physical devices connected to the internet. The goal of IoT is to make these devices smarter and easier to use. IoT data can be collected in a number of ways, including through sensors that are attached to devices, through the use of software that monitors activity on devices, and through the use of networks that connect devices.

It is estimated that one terabyte of information is generated by the New York Stock Exchange every day and more than 500 terabytes are created by Facebook daily. Moreover, 30 minutes of flying time for a single jet engine generates around 10 terabytes of intel.

One terabyte is the equivalent of 10¹² bytes. It is estimated that by the end of 2021 there were 74 zettabytes in the world and by the end of 2022, there will be 94 zettabytes. If you are wondering, one zettabyte is the equivalent of 10²¹ or one billion terabytes.

And by 2025, it is estimated that there will be more than 200 zettabytes in cloud storage. While most people are familiar with traditional sizes like megabytes, gigabytes, and terabytes, big data is stored in petabytes, exabytes, and zettabytes. 

It is estimated that Google, Facebook, Amazon, and Microsoft store at least 1,200 petabytes (10¹⁵). Big data is getting bigger. It is so voluminous that it overwhelms the technologies of today and challenges us to create the next generation of storage tools and techniques.

Data lifecycles

Data lifecycle is a process that ensures the accuracy, completeness, and timeliness of information across its life cycle, from capture to disposal. It encompasses all the activities and processes that support data creation, management, use, and disposal. Data lifecycle components are:

Creation

Data creation is the process of bringing new information into existence. It includes everything from compiling inputs from various sources to creating new models or databases, to developing new methods of data analysis. Creation can have a significant impact on a variety of fields. It is a process that helps organizations manage and procure raw information.

Storage

Data storage is the process or technology of organizing, managing, and storing information. It may refer to the physical storage of inputs on tangible media like hard drives, optical discs, or tapes, or the conceptual storage of information in a database or server.

Storage is the process of taking information from its source and placing it into a format that can be used by the data management system. It is about storing deets in a secure and tamper-proof system with robust backup.

Usage

Usage refers to how information is used throughout its lifecycle. This includes data acquisition, management, analysis, presentation, and dissemination.

Sharing

Data sharing is the process of allowing multiple individuals or organizations to access, use, and share the deets. Sharing is necessary for organizations to improve their efficiency, understand customer needs, and keep pace with the ever-changing digital world.

It is the act of sharing information between different organizations or individuals. It can be done for a variety of reasons, such as improving efficiency or sharing knowledge. Data

sharing can be a controversial topic. Some people believe that information should be kept confidential, while others believe that it should be shared as widely as possible.

Archival

Data archival is about making sure deets are properly stored in a way that can be accessed and used in the future. It is also about keeping intel in a safe and secure location, in case it will be needed again

Destruction (purge)

The volume of information and inputs keeps increasing, destruction refers to the process of deleting data from a system. Destruction can be done manually by an individual or automatically by software.

Purging is a process of removing irrelevant information from a dataset. This is done in order to make the dataset more concise and easier to use as well as improve the accuracy of the dataset.

There are many reasons why information might need to be destroyed. For example, info might be deleted because it is biased or outdated, to free up space on a system, or to conform to company policy.

Applications of big data

Big data is a big driver of innovation and there is no doubt that it is having a profound impact on business and society as a whole. From enabling faster and more accurate decision-making to improving communication and collaboration, it is driving innovation in a wide range of sectors. It is considered the new frontier for information-driven decision-making and is being used in a number of industries such as:

Finance

The finance industry is the one that rips the most out of big data, especially in the fintech sector. It is being used to improve decision-making by understanding how customers interact with their products, services, and businesses. The Global Financial Analytics Market size is projected to reach USD 17.1 billion by 2028.

Big data is also being used for risk management, to identify and assess potential risks associated with investments and financial products. It is also used for trading, identifying patterns in stock prices, and other financial info that can be used to make profitable investment decisions.

Manufacturing

The importance of big data in the manufacturing industry cannot be overstated. It is being used in various aspects from decision-making and business processes to increasing the efficiency of operations and productivity.

Research reveals that 72% of manufacturing executives rely on data to increase productivity. The use of information analytics in the manufacturing industry helps them save time and money which ultimately leads to more profits.

Manufacturing companies use analytics to make better decisions about what products to produce, where they should be produced, how long production will take, how much inventory they need to stock up on, etc… The manufacturing analytics market is projected to reach USD 9.11 billion by 2026.

Healthcare

In healthcare, big data is being used to improve patient care and to better understand how diseases and treatments work. Some of the ways it is being used in healthcare include:

• Monitoring health trends and identifying patterns that could indicate a health problem
• Improve patient care by monitoring and managing treatments
• Studying the genetic makeup of patients to better understand their disease
• Developing new treatments or therapies by understanding the behavior of individual patients
• Remote patient monitoring and internet of medical things

The global market size for big data analytics in the healthcare industry is projected to reach USD 59.10 billion by 2028, increasing at a CAGR of 9.12% from 2021 to 2028. The market was valued at USD 29.30 billion in 2020.

Media entertainment

In the media industry, large information is being used to improve the targeting and delivery of advertising, to understand audience preferences, and to develop new content strategies. For example, by tracking users’ online activity, companies can learn their interests and preferences. This information can then be used to target ads directly to them.

Retail

The retail industry uses analytic information to better understand customer behavior, improve customer experience, and increase revenue. The retail analytics market is expected to reach USD 13.13 Billion by 2027. Retailers use it to learn:

• How customers shop in-store, online, and on mobile
• How they interact with promotions
• What products do they buy and when
• What items do they add to their shopping cart but don’t purchase
• The brands they prefer
• Their location throughout the store
• What time of day do they shop

With this information, retailers can create personalized experiences for each customer, which will make them more likely to buy products from a retailer’s website or store again in the future. It is estimated that the customer experience management market will be worth USD 16.9 billion by 2026.

Agriculture

Agriculture is one of the most important economic sectors in the world. With the world population projected to reach 10 billion by 2050, food production will become increasingly important. But the sector is vulnerable to global warming.

Despite the importance of agriculture, the application of big data in this sector is relatively new. Its application in precision agriculture has shown to be extremely powerful for cultivating the field. It is used to predict weather patterns and when it’s best to cultivate or harvest.

With precision farming, farmers are collecting vast amounts of inputs that can be used to make informed decisions about their crops and land. By tracking the growth of crops over time, farmers can make sure that their plants are getting the nutrients they need to grow healthy and robust.

Logistics

The application of big data in logistics has been gaining momentum in recent years. There are many advantages to this, it can provide insights that are not available through traditional analytics.

It also helps optimize transportation networks. By understanding the flows of goods and people, you can optimize your shipping routes and freight forwarding arrangements. The supply chain analytics market is expected to reach USD 9.28 billion by 2028.

Education

The potential for using mass information to improve education is vast, and there are many ways that it can be used. It is used to monitor and track student performance. This can be done by collecting information such as attendance, grades, and test scores which are then used to identify trends and make changes to the curriculum or teaching methods to improve student performance.

Energy

Energy companies are constantly looking for ways to save money and increase efficiency. One way to do this is to use large intel to improve energy management. It can be used to identify energy usage patterns and identify opportunities for savings. The energy analytics market is expected to grow at a CAGR of 11. 28% during the forecast period, 2022-2027.

Marketing

Big data is revolutionizing marketing by providing insights to better target customers and make more informed decisions about ad campaigns. One of the most obvious ways information is used in marketing is by analyzing customer deets. This can include things like demographics, interests, and buying habits. This information allows the creation of targeted ads and campaigns that are more likely to increase sales.

Final words

Big data is one of the most talked subjects in today’s highly competitive world. It is important to understand what this term means and how it is used to make informed decisions about the future.

The term refers to the size and volume of information that can be gathered and analyzed to reveal patterns, trends, and potential new insights. The more intel a company has, the more likely it will find something novel, innovative, optimized operations, and enhanced products and services.

Big data is a large amount of information that no organization can afford to ignore. It offers organizations an increasing number of opportunities to improve customer experience, understand their business better, and create new business opportunities.

With information, businesses can learn about a customer, figure out what content people want to see on their site, decide which content to create, can plan their marketing, and can be successful at everything they do.

The power of data lies in its ability to give actionable insights about what’s working and what isn’t in a business. But this information is not only important for businesses, it is also useful for individuals as it helps them make better decisions. Hence, the future of big data will be determined by the way it is used and applied by its user.