Big Data Interactive Guide

Data-Driven Services

BIG DATA_

Interactive Guide

Learn More About Big Data and The Impact it has on Business

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INDEX

big data is driving data-driven innovation

machine learning Vs deep learning (video)

what is big data?

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06

03

the three v's of big data

the history of big data

thank you

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04

10

The use cases for big data

the value and truth of big data

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05

WHAT IS BIG DATA?

A recent report about the future of Big Data predicted that there would be 60% growth in the number of devices connected to the Internet by 2020.

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THE THREE V's OF BIG DATA

THE THREE V's

VARIETY

VOLUME

The Three V's of Big Data are Volume, Variety and Velocity. Volume is the amount of data that is being produced. Variety is the different types of data that is being produced. Velocity is the speed at which the data is being produced. Big data is a term that is used to describe the large volume of data that is being produced. This data can be in different formats, such as text, images, audio, or video. The data is also being produced at a very high speed.

Volume Volume is simply the quantity of data being processed. The more data that is collected and analyzed, the more valuable big data becomes. This is where big data gets its name – the volume of data is simply too large for traditional data processing tools to handle. Velocity Velocity is the speed at which data is generated and changes.

The second "V" is Variety which is the different types of data. The third "V" is Velocity which is the speed of data. Variety is the different types of data. There are three types of data: structured, unstructured, and semi-structured. Structured data is data that is organized in a specific way. Unstructured data is data that is not organized in a specific way.

VELOCITY

The Velocity of Big Data is the speed at which data is created, captured, and analyzed. The faster that data can be captured and analyzed, the more value it has. The Velocity of Big Data is important because it determines how quickly insights can be gained from data. The faster insights can be gained, the more value the data has.

The Value and Truth of Big Data

The value of data

We can all agree that businesses are collecting a lot of information every day – from email traffic to customer interactions on social media sites like Facebook or Twitter. But what if we could use this data to make better decisions? What if we could take advantage of our customers’ behavior online and offline to provide them with more relevant content and services? And what if we could do so at an increasingly lower cost?

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BIG DATA IS DRIVING DATA-DRIVEN INNOVATION

The rapid expansion of data is often called the “big data” phenomenon. The McKinsey Global Institute has estimated that, for many organizations, the opportunities to improve performance through data-driven decision making could be as much as $300 billion in value over the next five years. To capture this value, companies will need to overcome the challenges posed by big data, including the need for new skills, new organizational structures, and new ways of thinking. Organizations are grappling with how best to take advantage of big data, and an important part of this process is understanding the various factors that are driving data-driven innovation.

Artificial intelligence

Custom systems

machine learn

Systems that react quickly to changes in market conditions. For example, a bank could develop a system that monitors credit card transactions and alerts managers when there is unusual activity. As soon as suspicious activities become apparent, managers can stop payment orders before they cause damage.

Using machine learning to automate tasks that require human skill sets. Companies can now apply algorithms to learn from vast amounts of data and then act independently. A flight crew could train a computer program to take off and land a plane automatically.

Using AI to build better models by analyzing enormous amounts of data and applying statistical techniques. For example, an insurance company might analyze how customers drive to determine where to put gas stations or car repair shops.

60%

A retailer using big data to the full could increase its operating margin by more than 60 percent. (mckinsey.com)

THE HISTORY OF BIG DATA

The history

The concept of big data is anything but new. The history of big data can be traced back centuries, to when the first large-scale data sets were created. The first large-scale data sets were created by governments and other institutions. Over time, the size and complexity of these data sets has increased exponentially. Today, big data is used by businesses and organizations of all sizes to improve decision-making and gain a competitive edge. The history of big data is continually evolving, and the future of big data is looking brighter than ever. Thanks to the continued growth of big data, businesses and organizations are able to make better informed decisions.

THE USE CASES FOR BIG DATA

USE CASES

Some of the most common ones include customer segmentation, fraud detection, and marketing analytics. Each of these use cases can provide businesses with a competitive edge. Big data is also being used to improve operations in other industries. For example, healthcare providers are using big data to improve patient care. By analyzing large data sets, they can identify trends and patterns that would otherwise be invisible. This allows them to provide better care to patients. Big data is revolutionizing the way businesses operate.

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MACHINE LEARNING Vs DEEP LEARNING

AI ANALYTICS

In this new era, companies can get real-time insights on customer behavior by using artificial intelligence. In the example listed, a company could give AI access to all of their customer records. The system will identify patterns in the data set and provide key indicators for which products are most likely to be sold.

50% 80%

Data scientists spend 50% to 80% percent of their time curating and preparing data before it can actually be used. (oracle.com)

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