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“Deep learning craves big data because big data is necessary to isolate hidden patterns and to find answers without overfitting the data. With deep learning, the more good quality data you have, the better the results..”

Wayne Thompson, SAS Research & Development

History of Big Data

Do a quick google search and you’ll quickly realize that no one can really agree on the true origins of the term ‘Big Data’. Some argue that it has been around since the early 1990s, crediting American computer scientist John R Mashey, considered the ‘father of big data’, for making it popular.

Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and ‘70s when the world of data was just getting started with the first data centers and the development of the relational database.

Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open-source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.

The development of open-source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data—but it’s not just humans who are doing it.

With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.

While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data. And graph databases are becoming increasingly important as well, with their ability to display massive amounts of data in a way that makes analytics fast and comprehensive.

What is Big Data ?

Big Data refers to the voluminous and constantly growing amounts of data that an organization has that cannot be analyzed using traditional methods. Big data, which includes both structured and unstructured data types, is often the raw material for organizations to run analytics on and extract insights that can help them craft better business strategies. It is more than a byproduct of technological processes and applications. Big data is one of the most important assets today.

Big data can be made up of traditional structured data, unstructured, or semi-structured data. An example of unstructured—and constantly growing—big data is the user-generated data on social media. Processing such data requires a different approach than to structured data coupled with specialized tools and techniques.

Big data is the byproduct of the information explosion of today. All areas of business and everyday life contribute to the burgeoning pile of big data: retail, real estate, travel and tourism, finance, social media to technology, every aspect of our lives from how many steps we take to our financial histories is data.

Characteristics of Big Data

Big Data Diagram

The five V’s of Big Data are universally accepted:

  1. Volume
  2. Velocity
  3. Variety
  4. Veracity
  5. Value

1. Volume

The amount of data matters. With big data, you’ll have to process high volumes of low-density, unstructured data. This can be data of unknown value, such as Twitter data feeds, clickstreams on a web page or a mobile app, or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.

2. Velocity

The term ‘velocity’ refers to the speed that data is generated.

It is not just the volume of big data that can be an asset: how fast it flows, i.e. its velocity, is important too. The closer it is to real-time, the better in terms of competitive advantage for companies looking to extract actionable and valuable insights from it.

An example of this is whether a food delivery company decides to buy a Google Ads campaign on the basis of its sales data 45 minutes into the start of a major sporting event. The same data will have lost its relevance a few hours later.

Technologies driving this need for rapid data include RFID tags, smart metering, and various kinds of sensors.

3. Variety

Variety refers to the spectrum of sources from which a company can acquire big data and the plethora of formats it can appear in. This includes places like smartphones, in-house devices, social media chatter, stock ticker data, and data from financial transactions. The source has to be particularly relevant to the nature of the business for which the data is being collected. For example, a retail company must be tuned in to what users are saying on social media about its recently launched clothing line. A manufacturing company would less embedded value in following social media.

A variety of data can also extend to help organizations with understanding customer profiles and personas. For instance, a company would find it helpful to know not just how many people open their newsletter, but also why they opened it and distinguishing characteristics of the audience.

4. Veracity

Veracity calls into question the quality and accuracy of data. Clean data is the most trustworthy. Organizations must connect, cleanse, and transform their data across systems in order to trust it. They need hierarchies and multiple data linkages to keep control of their data.

5. Value

At the apex of the pyramid sits value, the ability to extract viable business insights from within the avalanche of data.

Value is being able to predict how many new members will join the website, how many customers will renew insurance policies, how many orders to expect, and such. Value is knowing who one’s best customers are and who will fall off the map in a few weeks or months, never to return.

Companies gain value through their ability to monetize the insights provided by big data. They get to know their customers better and continue to make more relevant offerings.

Types of Big Data

Data sets are typically categorized into three types based on its structure and how straightforward (or not) it is to index.

Structured Data

Structured Data

Structured data takes a standard format capable of representation as entries in a table of columns and rows. This kind of information requires little or no preparation before processing and includes quantitative data like age, contact names, addresses, and debit or credit card numbers.

Unstructured Data

Unstructured Data

Unstructured data is more difficult to quantify and generally needs to be translated into some form of structured data for applications to understand and extract meaning from it. This typically involves methods like text parsing, natural language processing, and developing content hierarchies via taxonomy. Audio and video streams are common examples.

Semistructured Data

Semi-structured Data

Semi-structured data falls somewhere between the two extremes and often consists of unstructured data with metadata attached to it, such as timestamps, location, device IDs, or email addresses.

AI and Big Data

Big Data management is dependent upon systems with the power to process and meaningfully analyze vast amounts of disparate and complex information. In this regard, Big Data and AI have a somewhat reciprocal relationship. Big Data would not have a lot of practical use without AI to organize and analyze it. And AI depends upon the breadth of the data sets contained within Big Data to deliver analytics that are sufficiently robust to be actionable. As Forrester Research analyst Brandon Purcell puts it, “Data is the lifeblood of AI. An AI system needs to learn from data in order to be able to fulfill its function.”

How Big Data Works ?

Before businesses can put big data to work for them, they should consider how it flows among a multitude of locations, sources, systems, owners and users. There are five key steps to taking charge of this “big data fabric” that includes traditional, structured data along with unstructured and semi-structured data:

  • Set a big data strategy.
  • Identify big data sources.
  • Access, manage and store the data.
  • Analyze the data.
  • Make intelligent, data-driven decisions.

Set a big data strategy

At a high level, a big data strategy is a plan designed to help you oversee and improve the way you acquire, store, manage, share and use data within and outside of your organization. A big data strategy sets the stage for business success amid an abundance of data. When developing a strategy, it’s important to consider existing – and future – business and technology goals and initiatives. This calls for treating big data like any other valuable business asset rather than just a byproduct of applications.

Identify big data sources

  • Streaming data comes from the Internet of Things (IoT) and other connected devices that flow into IT systems from wearables, smart cars, medical devices, industrial equipment and more. You can analyze this big data as it arrives, deciding which data to keep or not keep, and which needs further analysis. 
  • Social media data stems from interactions on Facebook, YouTube, Instagram, etc. This includes vast amounts of big data in the form of images, videos, voice, text and sound – useful for marketing, sales and support functions. This data is often in unstructured or semistructured forms, so it poses a unique challenge for consumption and analysis. 
  • Publicly available data comes from massive amounts of open data sources like the US government’s data.gov, the CIA World Factbook or the European Union Open Data Portal. 
  • Other big data may come from data lakes, cloud data sources, suppliers and customers.

Access, manage and store big data

Modern computing systems provide the speed, power and flexibility needed to quickly access massive amounts and types of big data. Along with reliable access, companies also need methods for integrating the data, building data pipelines, ensuring data quality, providing data governance and storage, and preparing the data for analysis. Some big data may be stored on-site in a traditional data warehouse – but there are also flexible, low-cost options for storing and handling big data via cloud solutions, data lakes, data pipelines and Hadoop.

Analyze the data

With high-performance technologies like grid computing or in-memory analytics, organizations can choose to use all their big data for analyses. Another approach is to determine upfront which data is relevant before analyzing it. Either way, big data analytics is how companies gain value and insights from data. Increasingly, big data feeds today’s advanced analytics endeavors such as artificial intelligence (AI) and machine learning.

Make intelligent, data-driven decisions

Well-managed, trusted data leads to trusted analytics and trusted decisions. To stay competitive, businesses need to seize the full value of big data and operate in a data-driven way – making decisions based on the evidence presented by big data rather than gut instinct. The benefits of being data driven are clear. Data-driven organizations perform better, are operationally more predictable and are more profitable.

Why Is Big Data Important?

The importance of big data doesn’t simply revolve around how much data you have. The value lies in how you use it. By taking data from any source and analyzing it, you can find answers that  1) streamline resource management, 2) improve operational efficiencies, 3) optimize product development, 4) drive new revenue and growth opportunities and 5) enable smart decision making. When you combine big data with high-performance analytics, you can accomplish business-related tasks such as:

  • Determining root causes of failures, issues and defects in near-real time.
  • Spotting anomalies faster and more accurately than the human eye.
  • Improving patient outcomes by rapidly converting medical image data into insights.
  • Recalculating entire risk portfolios in minutes.
  • Sharpening deep learning models’ ability to accurately classify and react to changing variables.
  • Detecting fraudulent behavior before it affects your organization.

Big Data use cases

Big data can help you address a range of business activities, from customer experience to analytics. Here are just a few.

Product developmentCompanies like Netflix and Procter & Gamble use big data to anticipate customer demand. They build predictive models for new products and services by classifying key attributes of past and current products or services and modeling the relationship between those attributes and the commercial success of the offerings. In addition, P&G uses data and analytics from focus groups, social media, test markets, and early store rollouts to plan, produce, and launch new products.
Predictive maintenance
Factors that can predict mechanical failures may be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost effectively and maximize parts and equipment uptime.
Customer experience
The race for customers is on. A clearer view of customer experience is more possible now than ever before. Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively.
Fraud and compliance
When it comes to security, it’s not just a few rogue hackers—you’re up against entire expert teams. Security landscapes and compliance requirements are constantly evolving. Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster.
Machine learningMachine learning is a hot topic right now. And data—specifically big data—is one of the reasons why. We are now able to teach machines instead of program them. The availability of big data to train machine learning models makes that possible.
Operational efficiencyOperational efficiency may not always make the news, but it’s an area in which big data is having the most impact. With big data, you can analyze and assess production, customer feedback and returns, and other factors to reduce outages and anticipate future demands. Big data can also be used to improve decision-making in line with current market demand.
Drive innovationBig data can help you innovate by studying interdependencies among humans, institutions, entities, and process and then determining new ways to use those insights. Use data insights to improve decisions about financial and planning considerations. Examine trends and what customers want to deliver new products and services. Implement dynamic pricing. There are endless possibilities.

Big Data applications

The insights and deep learning afforded by Big Data can offer benefit to virtually any business or industry. However, large organizations with complex operational remits are often able to make the most meaningful use of Big Data.

  • Finance
    Big Data “plays an important role in changing the financial services sector, particularly in trade and investment, tax reform, fraud detection and investigation, risk analysis, and automation.” Big Data has also helped to transform the financial industry by analyzing customer data and feedback to gain the valuable insights needed to improve customer satisfaction and experience. Transactional data sets are some of the fastest moving and largest in the world. The growing adoption of advanced Big Data management solutions will help banks and financial institutions protect this data and use it in ways that benefit and protect both the customer and the business.
  • Healthcare
    Big Data analysis allows healthcare professionals to make more accurate and evidence-based diagnoses. Additionally, Big Data helps hospital administrators spot trends, manage risks, and minimize unnecessary spending – driving the highest possible budgets to areas of patient care and research. In the midst of the pandemic, research scientists around the world are racing toward better ways to treat and manage COVID-19 – and Big Data is playing an enormous role in this process. A July 2020 article in The Scientist describes how medical teams were able to collaborate and analyze Big Data to help fight coronavirus: “We may transform the way clinical science is done, leveraging the tools and resources of Big Data and data science in ways that have not been possible.”
  • Transportation and Logistics
    The Amazon Effect is a term that describes how Amazon has set the bar for next-day delivery expectations to where customers now demand that kind of shipping speed for anything they order online. Entrepreneur magazine points out that as a direct result of the Amazon Effect, “the ‘last mile’ logistics race will grow more competitive.” Logistics companies are increasingly relying upon Big Data analytics to optimize route planning, load consolidation, and fuel efficiency measures.
  • Education
    During the pandemic, educational institutions around the world have had to reinvent their curricula and teaching methods to support remote learning. A major challenge to this process has been finding reliable ways to analyze and assess students’ performance and the overall effectiveness of online teaching methods. A 2020 article about the impact of Big Data on education and online learning makes an observation about teachers: “Big data makes them feel much more confident in personalizing education, developing blended learning, transforming assessment systems, and promoting life-long learning.”
  • Energy and Utilities
    According to the U.S. Bureau of Labor Statistics, utility companies spend over US$1.4 billion on meter readers and typically rely upon analog meters and infrequent manual readings. Smart meter readers deliver digital data many times a day and, with the benefit of Big Data analysis, this intel can inform more efficient energy usage and more accurate pricing and forecasting. Furthermore, when field workers are freed up from meter reading, data capture and analysis can help more quickly reallocate them to where repairs and upgrades are most urgently needed.

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What is Big Data Analytics ?

Big data analytics is the process of extracting useful information by analyzing different types of big data sets. Big data analytics is used to discover hidden patterns, market trends and consumer preferences, for the benefit of organizational decision making. There are several steps and technologies involved in big data analytics.

Big data is about new uses and new knowledge, not so much about the data itself. Big data analytics is the process of examining very large and granular data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and new business knowledge. People can now ask questions that were not previously possible with a traditional data warehouse as it could only store collected data.

Imagine for a second you were transposed into the karmic driven world of Earl. This is the view you are getting from customers in a data warehouse. To get a detailed picture of your customers, you will need to store fine, granular, nano-level data for these customers and use big data analytics such as data mining or machine learning to view the portrait with fine particles.

Data lakes are a central storage repository that holds large data from many sources in a raw and granular format. It can store structured, semi-structured or unstructured data, which means that the data can be stored in a more flexible format for future use. When storing data, a data lake links them to identifiers and metadata tags for faster retrieval. Data scientists can access, prepare, and analyze data faster and more accurately using data lakes. For analytics experts, this large body of data – available in a variety of non-traditional formats – offers the unique opportunity to access data for a range of uses such as emotion analysis or fraud detection.

The importance of Big Data Analytics

The true value of Big Data is measured by the degree to which you are able to analyze and understand it. Artificial intelligence (AI), machine learning, and modern database technologies allow for Big Data visualization and analysis to deliver actionable insights – in real time. Big Data analytics help companies put their data to work – to realize new opportunities and build business models. As Geoffrey Moore, author and management analyst, aptly stated, “Without Big Data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway.”

10 Sectors using Big Data Analytics

  1. Banking and Securities : For monitoring financial markets through network activity monitors and natural language processors to reduce fraudulent transactions. Exchange Commissions or Trading Commissions are using big data analytics to ensure that no illegal trading happens by monitoring the stock market.
  2. Communications and Media: For real-time reportage of events around the globe on several platforms (mobile, web and TV), simultaneously. Music industry, a segment of media, is using big data to keep an eye on the latest trends which are ultimately used by autotuning softwares to generate catchy tunes.
  3. Sports: To understand the patterns of viewership of different events in specific regions and also monitor the performance of individual players and teams by analysis. Sporting events like Cricket world cup, FIFA world cup and Wimbledon make special use of big data analytics.
  4. Healthcare: To collect public health data for faster responses to individual health problems and identify the global spread of new virus strains such as Ebola. Health Ministries of different countries incorporate big data analytic tools to make proper use of data collected after Census and surveys.
  5. Education: To update and upgrade prescribed literature for a variety of fields which are witnessing rapid development. Universities across the world are using it to monitor and track the performance of their students and faculties and map the interest of students in different subjects via attendance.
  6. Manufacturing: To increase productivity by using big data to enhance supply chain management. Manufacturing companies use these analytical tools to ensure that are allocating the resources of production in an optimum manner which yields the maximum benefit.
  7. Insurance: For everything from developing new products to handling claims through predictive analytics. Insurance companies use business big data to keep a track of the scheme of policy which is the most in demand and is generating the most revenue.
  8. Consumer Trade: To predict and manage staffing and inventory requirements. Consumer trading companies are using it to grow their trade by providing loyalty cards and keeping a track of them.
  9. Transportation: For better route planning, traffic monitoring and management, and logistics. This is mainly incorporated by governments to avoid congestion of traffic in a single place.
  10. Energy: By introducing smart meters to reduce electrical leakages and help users to manage their energy usage. Load dispatch centers are using big data analysis to monitor the load patterns and discern the differences between the trends of energy consumption based on different parameters and as a way to incorporate daylight savings.

Benefits of Big Data Analytics

  • Faster, better decision making

Businesses can access a large volume of data and analyze a large variety sources of data to gain new insights and take action.  Get started small and scale to handle data from historical records and in real-time.

  • Cost reduction and operational efficiency

Flexible data processing and storage tools can help organizations save costs in storing and analyzing large amounts of data.  Discover patterns and insights that help you identify do business more efficiently. 

  • Improved data-driven go to market

Analyzing data from sensors, devices, video, logs, transactional applications, web and social media empowers an organization to be data-driven.  Gauge customer needs and potential risks and create new products and services.

CONCLUSION

The availability of Big Data, low-cost commodity hardware, and new information management and analytic software have produced a unique moment in the history of data analysis. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue, and profitability.

The Age of Big Data is here, and these are truly revolutionary times if both business and technology professionals continue to work together and deliver on the promise.

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By d-tech-educate

The passion for technology, the curiosity and the desire to discover more about the world of the internet pushed me to create an educational peace for technology which I hope will help a lot of people with the information they will get from my posts. For the creation of the website I followed many videos on Youtube and WordPress attracted me more and I started to create it, now I am very happy that I created it. D-Tech Educate is a new website created to publish materials that will educate site visitors to be adopted with the latest technology, take advantage of its benefits while being careful with privacy of personal data etc. Thank you !

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