Big Data Basics: Understanding Big Data

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What is Big Data - Big Data Basics

Big data  refers to the process of managing massive amounts of data from many sources such as Databases, log files, and social media postings.

Big data (text, numbers, photos, and so on) can be classified as structured, semi-structured, or unstructured. Big data can be further defined by characteristics such as velocity, volume, variety, value, and veracity.

There is a strong need to make sense of this data and identify actionable insights. Big data as a field has grown in prominence over the past few years with increasing demand for the systematic extraction of information from complex data sets.

What is Big Data?

Big Data represents the massive amount of data that organizations are exposed to daily and cannot be handled using traditional database management systems.

However, it is not the quantity of data that matters. What matters is how organizations use the data. Analyzing large amounts of data can yield insights that result in better-informed decisions and intelligent business practices.

Why is Big Data Important?

The significance of big data is not determined by the amount of data you have but by what you do with the data.

Data can be analyzed from any source to discover insights to identify:

  1. cost savings
  2. time savings
  3. new product development and optimized services, and
  4. intelligent decision making.

When big data and advanced analytics are combined, it is possible to achieve business-related tasks such as:

  • Determining the root causes of failures, flaws, and difficulties in near-real-time
  • Coupon generation at the point of sale based on a customer’s purchasing patterns
  • Recalculate whole risk portfolios within minutes
  • Identify fraudulent activity before it impacts the organization

History of Big Data

Collecting and storing vast amounts of data and interpreting the collected information has been around for centuries.

However, the history of big data began much later. Here is a timeline containing the most notable milestones in the journey of big data.

1881 – A century after the U.S. Census Bureau started recording the population growth on punch cards, a ‘Tabulating Machine’ was invented to process punch card information much faster than manual processing. This was an early instance of data overload in history.

1928 – Fritz Pfleumer, a German engineer, invented the magnetic tape for data storage, which paved the way for determining how digital data would be stored in the future.

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1948 – Information explosion resulted in a desperate need for a way to store and access large amounts of data.

The Information Theory of Shannon laid the foundation for information infrastructure as it exists today.

1970 – IBM Research Labs published the first-ever paper on relational databases, explaining how information in large databases can be accessed more efficiently without knowing its location or structure.

The idea was similar to an Excel spreadsheet. However, this was, at first, limited to experts and those with good computing knowledge.

1976 – The MRP (Material Requirements Planning) systems started to be used commercially to organize information and became a catalyst for business operations.

1989 – Tim Berners-Lee introduced the World Wide Web.

1995 – Internet commercialization paved the way for Web 2.0. In the beginning, it was information-only, containing static sites.

After the release of Web 2.0, end-users could create, distribute and store content in a community.

2001 – A paper explaining the ‘3 Vs of Data’ was presented by Doug Laney, which became the basis for big data. It was at the same time that ‘software-as-a-service was introduced. In the mid-2000s, internet users started using social media networks extensively, leading to increased data distribution.

Netflix and YouTube also changed the way users viewed and streamed content. Data on these platforms were used to get insight into user behavior as well.

2005 – With the launch of Hadoop, an open-source framework for data storage, it was agreed that big data was the next frontier for revolution.

2008 – A paper about Big Data Computing was published explaining how big data can revolutionize the way organizations work.

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2010 – Google CEO revealed that people created more data in two days than the total data created until 2003.

2014 – The Internet of Things led more and more businesses to shift towards big data to reduce costs, improve efficiency, and develop new products and services.

An increasing number of companies moved their ERP solutions to the cloud.

2016 – A research and development plan for big data was introduced to drive progress in big data applications to influence the economy and society directly.

2017 – According to an IBM study, about 2.5 quintillion bytes of data gets created every day, and a whopping 90 per cent of the total global data was generated in the last two years alone.

Big Data Basics: The V’s of Big Data

Big data is generally characterized by a set of V’s, the attributes whose names start with V. Breaking down the concept into these segments makes it easier to understand the notion. The three big V’s of big data are – Variety, Velocity, and Volume.

Variety

The first V of big data, Variety, refers to the different types of data generated every day. Data is big and fast-growing, and it is also diverse.

A few years back, data was simple in form with plain text format and neat a structure in a database. Also, there were a few options to use this data, probably finding a trend or classifying it.

While plain text still exists, formats like digital images, audio, and video have become increasingly common. Such data has more significant storage requirements and is also unique in how we analyze them to get valuable results.

Velocity

Another V of big data refers to Velocity, the speed at which sources generate big data every day. The growth of big data initially presents new opportunities. However, the rate at which it grows outperforms our ability to decipher it. Estimates indicate that the amount of data in the world doubles every two years.

A more surprising fact is that only 3% of the total data in the world is organized, and just 0.5% of it is ready for analysis. Big data isn’t just big; it’s continuing to grow exponentially.

We can add some context to big data with the help of social media statistics, as social media influences big data to a great extent. Recent reports estimate over 136,000 photos, 293,000 statuses, and 510,000 comments uploaded to Facebook every minute.

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Volume

The most prominent V of big data is Volume which refers to the size of data existing today and in the future. There is so much data available, sometimes so much that it is difficult to comprehend. As 90% of the total data of the history is created in the last two years, equating to about 2.5 quintillion bytes of data every day.

However, according to estimates, the universe will have 163 zettabytes of data by 2025. To understand volume, let us again look at an example of social media. In 2016, Facebook had about 2 trillion total posts and over 250 billion pictures posted since being launched in 2004.

Facebook and other social media networks have amassed a hefty volume of data. Facebook has over 2.8 billion users who share significant volumes and types of information every second on the platform.

Veracity and Value

While the above three are the essential V’s of big data, the traditional V’s named veracity and value are also becoming significant as big data expands. Veracity refers to the accuracy of data. Not all the data is always consistent and precise. As big data grows at a blistering rate, it becomes increasingly difficult to determine which data delivers value.

As an example, consider social media data which is often trending in a particular direction and volatile. Alternatively, a real data example is a weather forecast that you can easily track and predict. On the other hand, Value asks, ‘How can we use the data to derive something meaningful for the business and users?’ When big data is analyzed without a purpose, it is not of much value.

Types of Big Data

With the growth of digital platforms, devices, and storage solutions, not only does the volume of data increase but also the varieties available. Today, there are large numbers of big data sources worldwide producing several different types of data at an exponential rate. Not all data is equal; how you process a number in a database differs from how you derive value from a video clip.

Let us try to understand the different types of Big Data.

Structured Data

Structured data is any data that you can access, process, and store in a fixed format. For the most part, this type of data is organized in a relational database. A quick search lets you do it easily when you need to access a part of the information in a database. It is pretty similar to the machine language the computer understands. Such data sits neatly in a field within a file or record.

A good example of structured data is a spreadsheet. When you are verified via phone for a loan, the chances are that they are working with structured data. It is any quantitative data such as a name, contact, address, billing, age, debit card number, and so forth. This type of data is the easiest to work with. Structured big data is generally coordinated with values obtained from different parameters.

Software engineering has made notable progress in new techniques to store and process structured data in recent times. These days, the biggest problem is the hefty sizes of data, the average size being in zettabytes.

Unstructured Data

It is nice to have all the big data structured; however, most data generated by humans, including text messages, pictures, and voicemails, are highly unstructured. Surprisingly, up to 80 per cent of total data is unstructured, so we can only organize 3 per cent of the world’s total data. Unstructured data means it is not easily identifiable by the computer and does not meet a specific standard of spreadsheet or database.

As the size is vast, unstructured big data has different challenges when deriving a value from it. An example is a data source containing a mix of text files, images, and videos. Most organizations have a lot of unstructured data in a variety of formats and types. In most cases, organizations struggle to turn this data into valuable information because it is in the raw format.

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Most unstructured data are heavy in text. Text messages are heavily unstructured because humans don’t type in a logical language that machines can understand. This is why language processing and machine learning is used to interpret human languages, jargon, slang, and other elements.

Then, there are some machine-generated unstructured data that machines find easier to process. An example is satellite images captured for weather forecasts.

Structured Vs Unstructured Data

Here are some ways structured and unstructured big data differ from each other:

Defined Vs Undefined

Structured data is generally presented in pre-defined formats, while unstructured data reside in its raw form.

Structured data can be easily organized in rows and columns and mapped to pre-defined fields.

On the other hand, unstructured data has no pre-defined data model and cannot be easily accessed in relational databases like structured data.

Qualitative Vs Quantitative

Structured data is generally quantitative, meaning it can be counted or measured. Such data can be processed using analysis methods like classification, clustering, and regression.

On the other hand, unstructured data belongs to the qualitative type that is challenging to analyze using traditional methods.

To understand qualitative data, consider the example of information coming from interviews, social media communication, and customer surveys. You often need advanced techniques like data mining to process such data and gain insights from them.

Ease of Analysis

A highly significant difference between structured and unstructured data is how easy it is to analyze.

Structured data can be easily searched using computers. Unstructured data is not so easy to analyze and requires some transformation to be understood. It is also challenging to break it down as it doesn’t have a pre-defined data model and does not fit in a relational database.

Several powerful analytic tools exist for handling structured data. However, those for organizing and processing unstructured data are not yet fully developed. Since it lacks a pre-defined structure, data mining is complex. It remains a challenge to handle data sourced from social media, blogs, and internet-based communication.

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Storage

Structured data is commonly organized in data warehouses. Data lakes are used as storage for unstructured data. A data lake is a boundless repository where all the data is stored in its raw format or after some refinement. A data warehouse is basically the end of the data’s journey.

Both types of data can be stored in the cloud. While structured data occupies less storage space, unstructured data needs more space.

For example, a small image file takes up more space as compared to several pages of text. Structured data can be stored in a relational database, while unstructured data is instead put on NoSQL or non-relational databases.

Pre-defined Vs Varying Formats

Structured data generally exists in known formats like text and numerals. Such data is consistently defined beforehand.

Unstructured data, on the other hand, can come in several different sizes and shapes. It can consist of images, audio, video, email, and everything else. Such data have no fixed data model. They are often stored in a data lake and require no transformation.

Semi-Structured Data

The third type of big data falls somewhere in the middle of structured and unstructured data.

Semi-structured data is a type of big data that contains both unstructured and structured data formats. This data contains valuable information that helps comprehend the components even though it has not been organized in a database.

For example, data stored in an XML file or email is semi-structured because they contain tags like date/time, sender/receiver, but the language they use is not structured.

Applications of Big Data

Today, big data has entered almost every industry. Companies use valuable information for several purposes, including understanding customers, growing sales, improving research, targeting audiences, and making forecasts.

Many sectors use big data to get answers to common questions, identify trends and gain insight into customer interests.

Here are some examples of typical applications of big data.

Telecommunications

As the number of mobile users in the world keeps increasing, telecom has become one of the core areas for big data.

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Service providers use big data to recover quickly from events by finding the cause using real-time data. They use analytics to discover personalized, accurate ways to charge customers.

Valuable insights generated from geospatial data, social media, and mobile data can be used to offer personalized options for media and entertainment.

Entertainment

If you watch series and movies on Hulu, Netflix, or any other streaming service, you already know how big data works for entertainment and multimedia.

Companies analyze our habits and interests and suggest recommendations for personalized experiences. Netflix even uses data related to colors, titles, and graphics to analyze user preferences and better serve them with what they like the most.

Finance

Insurance and finance industries use big data analytics for some of the most critical operations, including credit ranking, risk assessment, fraud detection, blockchain technology, and brokerage services.

Banks and financial institutions are also leveraging the power of big data to improve their security efforts and introduce personalized offerings for customers.

Agriculture

From predicting crop yields accurately to engineering seeds, automation and big data are rapidly revolutionizing the agriculture industry.

With the growth of data in the past decade, information is abundantly available, inspiring scientists and researchers to use big data to analyze nutrition and food-related aspects.

As many groups promote open access to agricultural and nutrition data, more progress is seen in the battle against world hunger and malnutrition.

Education

A single education model does not suit different groups of students. Some are visual learners, while others can grasp audio better. Some may prefer on-premises learning, while others like to study online.

Big data analytics helps build customized learning experiences for all types of learners. Institutions are also using big data to bring down dropout rates by determining risk factors in students who fall behind.

Healthcare

Health research professionals, hospitals, healthcare institutions, and pharmaceutical companies are gradually embracing big data solutions to improve their processes, resulting in advanced.

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As more population and patient data become available, the industry is improving treatment methods, gaining valuable insights on popular health patterns among groups, developing more effective medications, and carrying out extensive research on deliberating diseases.

Some other applications of big data include e-commerce, marketing, the internet of things, sports, and business.

Big Data Examples & Use Cases

Businesses have started realizing the importance of evolving to a learning organization and are striving to be more data-driven. This is why they are embracing the power of data and technology to uncover patterns, correlations, and insights to make better decisions.

Today, big data analytics is done using software solutions.

Let look at some real-life examples and use cases to understand how big data is being used.

Improving Customer Acquisition & Retention

The use of big data helps businesses observe different trends and patterns related to customers to stay on top of customer behavior.

The more the business collects data, the more trends and patterns it can identify and the better it can boost loyalty and retention. In today’s age of technology, an organization can easily collect all the data it needs to understand clients.

Having a well-designed customer data analytics system lets businesses derive valuable behavioural insights to act on to acquire and retain a customer base.

A good understanding of customer insights will also allow businesses to deliver what customers want, thereby boosting their ability to attain high customer retention levels.

Coca-Cola is an excellent real-world example of how businesses use big data to boost customer retention. The brand, in 2015, made efforts to strengthen its data-driven strategy by introducing a digital loyalty program. Coca-Cola listens to customers through different modes and uses the data to create relevant content for varied audiences.

Identifying and Managing Risks

Regardless of the industry and sector, risk management is critical for any business that aims to remain profitable. Big data analytics have always contributed to developing robust risk mitigation solutions, offering newer tools to help businesses model risks they encounter every day.

As more and more data is available in a wide variety, big data can improve the efficiency of risk management strategies.

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An example of a company using big data for risk management is Singapore’s UOB Bank. As a financial institution, it has a high potential for losses if a good risk mitigation strategy is not put in place. The bank recently tested a risk management method based on big data.

This system enables it to lower the time taken to calculate the value at risk. It has successfully reduced the time from 18 hours to a few minutes using this system and looks forward to carrying out real-time risk analysis in the coming years.

Driving Innovation and New Product Developments

One of the most essential uses of big data is to help companies innovate and improve their products. Big data has recently become a source of extra revenue through innovations and product improvements.

Businesses correct data as much as possible before designing new product lines and improving existing products.

A good example of how big data can drive innovation is Amazon’s Fresh. This giant uses big data analytics to enter a larger market space. It helps the brand understand how customers buy groceries and use these insights to implement changes to its business model. Data-driven logistics gives Amazon the expertise to create and achieve greater value.

Improving Supply Chain Management

Big data provides supplier networks with better clarity, accuracy, and insights. With the application of big data analytics, suppliers can overcome the constraints they faced in the past. Big data based supply chain systems allow more complex networks of suppliers built on collaboration and knowledge for improved contextual intelligence.

PepsiCo is an excellent real-world example of companies using big data to make their supply chain more effective. The brand commits to ensuring that retailer shelves always have the right type and volumes of products. Clients help the company with inventory reports, and this data is used to forecast production and shipment. This is how PepsiCo makes sure retailers get the right products at the right time in the correct quantity.

Optimizing Marketing Campaigns and Solving Advertiser Problems

The advertising and marketing sector has finally been able to embrace big data. It has started making advanced analysis that involves observing internet activities, tracking POS transactions, and detecting dynamic changes in user trends.

Big data helps gain insights into customer behavior resulting in a capability to achieve more targeted advertising campaigns, thereby ensuring efficiency and cost-saving.

Big data and predictive analysis are beneficial for organizations in defining their target audience. With the use of data about customer behaviors, businesses can work in the right direction to get an effective reach while avoiding losses incurred from ad fraud problems.

A good example of big data’s use for targeted advertising is the media giant Netflix. With more than 200 million subscribers, Netflix gathers enormous volumes of data that helps it achieve the status it has today. Users would know how Netflix sends suggestions for the next movie they should watch. This is achieved with the help of past activities. Data about the past films and searches give insights into what interests the user the most.

Elements of Big Data Environments

There are several components and processes involved in the management and processing of big data. Here are some of the primary elements making up big data environments.

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Architecture

Big data sets in structured format can be stored in existing data warehouses. However, big data architecture also includes data lakes that store data sets in raw form. A robust architecture also offers the necessary support for data engineers to create pipelines to funnel data into repositories and analytics software.

Analytics

Big data is generally used for analytics programs, for either simple business intelligence or advanced analytics. Machine learning has revolutionized how large sets of data are analyzed to derive patterns and anomalies – the combined use of big data and machine learning results in more efficient analytics.

Collection

Before you can use big data sets, they should be collected from internal systems and external sources. The volume of data, its variety, and the complexity of sources can make this task complicated and challenging. Privacy and security problems add to the challenges as businesses should comply with several regulations when handling data.

Integration

Big data environments also need to focus on integrating data sets. This adds newer requirements and challenges to the existing processes. Traditional procedures for extraction and transformation may not work for the variety, volume, and speed of big data.

Data management teams often need to adopt newer integration techniques. Once integrated, big data should also be prepared for analysis. When data is stored in raw form, it is prepared by data engineers and scientists to suit the needs of analysis programs.

Governance

The efficiency of data governance helps make sure big data collections are correctly used and consistently while being compliant with data standards and regulations.

However, the wide variety of data poses new challenges for teams. One of the most critical aspects of data governance is quality management which needs new processes and tools.

Common Big Data Challenges

The nature of big data makes it challenging to manage, process, and use at times.

Big data environments are generally complex, with complicated systems and tools set up to work together. The data is complex as well, particularly when streaming data is involved or the sets are massive.

The most significant challenges for big data can be summarized as:

  • Data Management – Everything from storage and processing of vast volumes of data to preparing, cleaning, integrating, and governing the big data is challenging.
  • Technical – The biggest challenges are associated with selecting tools and technologies and scaling the data systems to the organization.
  • Analytics – These include challenges faced in ensuring all the business requirements are correctly understood, and the results of analytics are relevant to the business strategy.
  • Program Management – Such challenges include managing costs and finding the right set of skills in big data.

Big Data Analytics

Software-based big data analytics picks up where traditional analytics platforms don’t seem to work, considering the enormous amounts of structured and unstructured data.

Business Intelligence tools analyze data within the data warehouse of the organization to help businesses make informed decisions. It focuses more on data management and the improvement of business operations.

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On the other hand, big data analytics considers raw data to uncover trends, patterns, and preferences for accurate predictions. Here are some ways big data analytics help businesses.

  • Descriptive Analysis – This big data analysis technique generates graphs, reports, and other types of visual representations to help businesses understand specific events. This type of analysis only applies to events from the past.
  • Diagnostic Analysis – This type of analysis focuses on giving insights about a specific problem rather than an overview. Companies can use diagnostic analysis to find out why a problem occurred. It is a more complex form of big data analysis and often involves machine learning and AI.
  • Predictive Analysis – When powerful algorithms are used with AI and machine learning, organizations can predict the future. The ability to forecast trends and patterns of the future certainly provides great value to the business.
  • Prescriptive Analysis – This type of analysis is quite complex and not widely implemented. As compared to other analysis tools that let you draw your conclusion, the prescriptive analysis gives you accurate answers in the form of reports using high-level machine learning.

Big Data Tools and Technologies

As the requirements for storage, computing, and networking for these large sets of data are beyond the capabilities of individual processors; there is a need for appropriate tools and techniques to process data through distributed computers.

The introduction of the Hadoop distributed framework in 2006 marked the beginning of the big data age. It was released as an open-source platform to handle massive amounts of diverse data.

An ecosystem of technologies was created around Hadoop, and different NoSQL databases came up to offer more platforms to store and manage data that traditional databases and tools could not handle.

Some popular tools and technologies employed in modern big data environments include:

  • Storage repositories – Cloud storage options such as Google Cloud and Amazon Simple, and Hadoop Distributed File System
  • Processing Engines – Hadoop MapReduce, Spark, Structured Streaming from Spark, and stream processing platforms such as Storm, Samza, Flink
  • Data Warehouse and Data Lake Platforms – Some examples are Google BigQuery, Snowflake, Delta Lake, Amazon Redshift
  • NoSQL Databases – The most common database options are HBase, Cassandra, MongoDB, MarkLogic Data Hub, Couchbase, Redis, and more
  • SQL Query Engines – Trino, Hive, Drill, Presto
  • Managed Services – Google Cloud Dataproc, Amazon EMR, Cloudera Data platform, and more

Future of Big Data

There is no sign of the growth of global data slowing down. This growth has been driven primarily by the rise of the web and social media. Another significant catalyst to the increase in data is the use of IoT devices and sensors.

Today’s world is powered by big data. Organizations have increasingly acknowledged the importance of taking a data-driven approach to marketing and other processes for internal function and better customer experiences.

Some of the trends you can expect to see in the future of big data are:

Volumes of data will continue increasing and migrating to the cloud

Most data experts predict that the future will see a further increase in the amount of data created by digital sources. Forecasts suggest that the global data will reach more than 175 zettabytes in 2025.

Such rapid data growth is expected, seeing the growing number of users who prefer to do everything online. Another reason for this growth is the rise of systems and devices that generate and share a wealth of IoT data daily.

Machine learning will continue to be impactful

One of the most significant technologies associated with big data, machine learning, is sure to impact the future to a great extent. Machine learning evolves rapidly as a part of everyday business operations and processes.

As compared to all the AI systems, machine learning has received the most funding in the past years. With the introduction of open-source platforms, technologies are readily available to organizations. They can combine this availability with the right skills to configure solutions.

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Data scientists will be in significant demand

Though the roles of data scientists and data officers are relatively new in the industry, the need for these specialists is already rising.

Without analysis, big data is of no use. Data scientists gather and analyze data using reporting and analytics tools, converting it into actionable results.

Privacy will remain a core issue

The ever-growing volumes of data will create more challenges when it comes to protecting data from cyberattacks because the levels of security cannot meet the data growth rates.

The primary reason for this is the growing security skill gap. The evolution and increasing complexity of threats and inappropriate adherence of organizations to security standards further worsen the issue.

Fast and actionable data will come to the forefront

Predictions of big data suggest the rise of ‘fast data’ and ‘actionable data’.

Fast data is different from big data as it allows stream processing for instantaneous data analysis. This delivers better value to organizations as they can take action and make decisions immediately when data arrives.

Final Thoughts

Daily, the world generates a massive amount of data, and this volume continues to increase with technological advances.

While the increasing size is undoubtedly a challenge, modern businesses have developed the latest tools and techniques to process big data and get valuable insights and benefits from it.

As more and more industries and businesses continue to leverage the power of big data, it is sure to grow more prominent in the future.

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