Big Data: How is it a Game Changer!

In business, applied knowledge is power and data make this knowledge possible. The expert estimated that this year, data volume might exceed 44 trillion gigabytes, by leveraging large volumes of structured and unstructured data to harness the power of big data.

There are different definitions of big data, but most of them are based on the concept of “three V” big data:
1. Volume – Data is measured by the value of the physical amount.
2. Velocity – This defines the momentum and speed in which data is regularly processed and updated
3. Variety – Data comes from a wide range of sources in various formats (it can be online magazines, interaction on social networks, online commerce and online transactions, financial transactions, etc.).

In modern systems, two additional factors have emerged:
• Variability – Bursts of unstructured information are challenging to manage and require powerful processing technologies.
• Data value (Value) – Information can have different complexity for perception and processing, which complicates the work of intelligent systems. The task is to determine the degree of importance of the incoming information to structure quickly.

When is big data needed?
Despite all the information available, many organisations are not aware that they are facing a big data problem, or are not ready to think in such categories. An organisation can benefit from big data technologies if its existing applications and databases are no longer able to scale and cope with sudden increases in the volume or variety of data or their processing speed requirements.
If you don’t find the right approach to working with big data in time, this can lead to higher costs, as well as reduced work efficiency and competitiveness. A smart big data strategy can help your organisation reduce costs and gain additional business benefits by implementing ongoing large workloads using big data technologies, as well as deploying new applications to take advantage of opportunities.

The Evolution of Big Data Processing
Big data technologies continue to evolve. Nowadays, organisations have a choice between different types of analytics to implement various functions.

The descriptive analysis helps users answer the question: What happened and why? An example is the traditional query and reporting environment with dashboards and rating systems.

Predictive analysis allows users to assess the likelihood of certain events in the future. Examples include forecasting systems, early warning and fraud detection, and preventative maintenance applications.

The prescriptive analysis forms for the user-specific recommendations (prescriptions). It helps to answer the question: “What if event X occurs?”

Initially, big data infrastructures such as Hadoop only supported batch workloads. Large data packets were loaded for processing immediately, and the process of waiting for results stretched out to hours and even days. Gradually this became a critical factor, and the required speed of processing big data served as an impetus for the development of new infrastructures such as Apache Spark, Apache Kafka, Amazon Kinesis, etc., capable of supporting real-time streaming data processing.

How does big data work?
Big data technologies include new tools for all stages of the data processing cycle, the use of which is quite affordable both from a technical and financial point of view. Using these tools, you can solve the problems of collecting and storing large data packets, and by processing them to obtain new valuable analytical information. In most cases, working with big data involves a standard workflow: from collecting raw data to getting useful information.

Collection: The collection of raw data (transactions, logs, events of mobile devices, etc.) is the first problem that organisations face when working with big data. A high-quality platform for working with big data simplifies this stage, providing developers with the ability to collect a wide variety of data, structured or unstructured, at any speed, from real-time to batch processing.

Storage: Any platform for working with big data should include a reliable, secure and scalable repository for storing data both before and after processing. Depending on the specific requirements, temporary storage is necessary.

Processing and analysis: At this point, the raw data is converted into a usable format. Usually, this is achieved by sorting, aggregating, combining, or using special advanced functions and algorithms. After that, the resulting data packets are saved for further processing or provided for use with the help of business intelligence and visualization tools.

Visualisation: The main goal of working with big data is to obtain valuable analytical conclusions on their basis for practical application. Ideally, big data should be accessible to all interested parties so that they can easily and quickly study data packets using business intelligence tools and custom visualisation, designed for independent use. Depending on the type of analytics, end users get the finished results in the form of data from static “forecasts” (in the case of predictive analytics) or recommended actions (in the case of prescriptive analytics).

Summary
We cannot hide from technology. Big data is already changing the world, slowly seeping into our cities, houses, apartments and gadgets. How fast technology captures the planet is hard to say. One thing is certain for sure – hold on to fashion or die in the crap, as Bob Kelso said in the TV series Clinic.

If you’d like to know more why not download our whitepapers on this link www.cxportal.com/whitepaper . However, if you’d like to visit our website on www.cxportal.com and we’ll help you in any way we can.