Course NameLength
Applied Data Science and Machine Learning Training3 days
Big Data for Project and Program Managers Training3 days
Big Data Training2 days
Fundamentals of Data Analytics and Methods3 days
Fundamentals of Multi-Target Tracking & Multi-Sensor Data Fusion2 days
Hadoop Training3 days
Python Programming Bootcamp | 3-Day Introduction3 days
Sensor and Data Fusion Training Bootcamp3 days

The digital era has brought about massive changes, including the production of big data.

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems organizations wouldn’t have been able to tackle before.

The 5 V’s of big data (velocity, volume, value, variety and veracity) are the five main and innate characteristics of big data. Most people determine data is “big” if it has the four Vs—volume, velocity, variety and veracity. But in order for data to be useful to an organization, it must create value—a critical fifth characteristic of big data that can’t be overlooked.

Value refers to the value that big data can provide, and it relates directly to what organizations can do with that collected data. Being able to pull value from big data is a requirement, as the value of big data increases significantly depending on the insights that can be gained from them.

Organizations can use the same big data tools to gather and analyze the data, but how they derive value from that data should be unique to them.

Analysts believe that data isn’t like a traditional asset. For real-time applications, data only has value for a short period of time, but large volumes of data might contain valuable insights that have to be mined. Measuring both ends of this spectrum is a challenge. It can be hard for chief data officers and leaders to justify expenditure on analytics if its value is not adequately captured.

One approach to measuring data is to quantify the various ways in which data is used in an organization. Data has costs—related to collecting, analyzing, and storing it—and provides benefits as it is monetized or used for decision-making.

Data also suffers from depreciation, as it can lose value over time.

Some of the big data trends include increased focus on information quality, improved governance, leveraging AI and ML technologies, enabled federated search, and wider usage of RPA technologies.

In the private sector, businesses that apply big data analytics have experienced a 26% improvement in performance, and harvesting big data for decision-making can increase global corporate profits by 21%.

The increasing use of big data is fuelled by IP networks that are connecting billions of physical devices in what we call the Internet of Everything, which describes the value from connecting devices, data, processes and people. And this volume of data is accelerating, driven by four major trends:

Industry influencers, academicians, and other prominent stakeholders certainly agree that Big Data has become a big game-changer in most, if not all, types of modern industries over the last few years. As Big Data continues to permeate our day-to-day lives, there has been a significant shift of focus from the hype surrounding it to finding real value in its use.

According to Research and Market reports, the global Big Data market size is expected to reach USD 268.4 billion by 2026.

Tonex Training

Tonex currently offers four classes in Big Data Training. Learn the procedures and methods to identify, store, manage, process and analyze massive amounts of unstructured data, picking the right data is even more important. Learn how to define target or goal and subsequently establishing disciplined parameters and key indicators for the data you want to collect or process.

These classes in Big Data Training are excellent for individuals or organizations seeking to develop an understanding of Big Data and Data Science from the perspective of a practicing Data Scientist.

For more information, questions, comments, Contact us.