|Big Data For Project and Program Managers Training||3 days|
|Big Data Training||2 days|
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|Sensor and Data Fusion Training Bootcamp||3 days|
Big Data is an evolving term.
It most commonly refers to data sets that are so voluminous and complex that traditional data processing application software is inadequate to deal with them. This has created Big Data challenges for businesses, which includes capturing data, data storage and data analysis.
Quite simply, Big Data reflects the changing world we live in. The more things change, the more the changes are captured and recorded as data.
There has always been data. Data in itself isn’t a new invention. Before computers and databases there were transaction records, customer records and archive files. Then along came computers, spreadsheets and databases, which gave us a means for storing and organizing data, which has grown exponentially over time.
Today, almost every action we take leaves a digital trail. Data is generated when we go online, when we shop, when we carry around our GPS-equipped smartphones and when we communicate through chat applications or social media. On top of the human-generated data, there is now machine-generated data such as when servers communicate with one another.
Now, every two days we create as much data as we did from the beginning of time until 2000. By the year 2020, it’s forecasted that the amount of digital information available will have exploded from five zettabytes today to 50 zettabytes.
The Three Vs
The thing about data is this: In its raw form it has no value. Data needs to be processed in order to be useful. However, this presents a dilemma for organizations. Is the Big Data accumulated worth the capital expense of processing? Or is there just too much data with unknown values to justify the gamble of processing with Big Data tools?
When making the decision to process or not to process, organizations need to consider the three Vs that define Big Data:
• Variety – Data can be stored in multiple formats. Can you organization be successful by just storing data in a single text file? Or are your Big Data needs such that you must arrange data differently to make it relevant?
• Velocity – There was a time when we used to believe that data of yesterday was recent. Is yesterday’s news old news for your organization’s needs?
• Volume – Has the database grown to the extent that the applications and architecture built to support the data needs to be reevaluated quite often?
Today Big Data has become big capital. For many companies such as those in the tech industry, a large part of their value comes from their data, which they must constantly analyze to produce efficiency and develop new products.
Big Data Successes
Utilizing Big Data propitiously can make significant improvements in our world. Examples of this include:
• Crime prevention – Policing agencies are using data-driven strategies based on their own plans and public data sets to deploy resources and fight crime more efficiently.
• Improving healthcare – Big Data driven healthcare is helping spot disease earlier and develop new medicines.
• Predicting natural and man-made disasters – Big Data in the form of sensor information is now being used to predict everything from hurricanes to earthquakes. Big Data technology has also been useful in monitoring large fires and helping to safeguard the flow of refugees away from war zones.
Big Data Concerns
There’s also a downside to the Big Data era. Processing large volumes of data also raises questions and concerns about data privacy, data security and even data discrimination. Organizations must confront these challenges head on or face legal and financial consequences.
But any way you look at it, Big Data is changing our world. For businesses, the ability to use Big Data correctly will become increasingly critical in the coming years. Companies that view data as a strategic asset should thrive. Those that ignore or fail to stay current in this Big Data revolution risk being left behind.
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.