How to Start Learning About Big Data?

Big Data, as a concept, has been extracted in almost every discussion about digital innovations, the Internet of Things (IoT), and data science research. However, there is still some confusion about what precisely this term means.

Simply put, big data is the collection, analysis, and processing of large amounts of varied data emerging from multiple sources. These large datasets can give insights into human behavior, and inform business practices, strategies, product design, artificial intelligence, and more.

Let’s understand the difference between small data and Big Data:

Small data vs. Big Data

It is easy to know the scope of big data through comparison to small data. Small data is information that can be controlled by a single machine, or by using traditional methods of analysis. The source and result of this data are on a smaller system. For example, production logs can be used to develop weekly performance reports on the productivity of a manufacturing line; or survey results can be applied in a marketing report about the brand plan.

Big Data Characteristics

How do you process mixed data on such a large scale, where traditional methods of analytics fail? This has been one of the numerous significant provocations for big data scientists

1. Volume:

This is the primary distinguisher when it gets to Big Data systems. Each of us has a digital footprint, and the number of data-sets that can be gathered from each of our devices is mind-boggling.

2. Velocity:

90% of data currently available was created in the last two years alone. 2.5 quintillion bytes of data gets produced every single day, and this data is expected to be processed in real-time, to make insights that will not be rendered redundant in a continually changing world. This is why big data analysts have moved away from a traditional batch-oriented way, and have adopted real-time analysis to assure they are generating information that is relevant to the current situation.

3. Variety:

What makes big data systems so important to businesses and communities is the fact that these are unique datasets, as they emerge from different sources, and are prepared using various methods. Data can be sourced from social media feeds, physical devices such as Fitbit, home security systems, automobile GPS systems, and more.

Read: Big Data Overview — Types, Characteristics Advantages

The Three Vs. have been used to distinguish big data since 2001, but the latest narratives are in favor of adding ‘veracity, visualization, variability, and value’ to this list, which widens the scope of big data analysis even more.

That was about the characteristics of Big Data, next on this Big Data tutorial, let’s talk about how to make this data workable and derive insights from it.

How to Get Knowledge of Big Data?

The USP of Big Data is the kind of insights that can be drawn. This usually cannot be done by traditional methods, as a lot of the ideas, trends, and patterns are often not-obvious. Furthermore, small data analysis technologies do not present themselves to the large volume and variety of content generated through big data methods.

To overcome these difficulties, various new technologies have been developed- the most popular being the Apache Hadoop. These technologies utilize clustered computing to consume information into a data system, and compute and analyze the data, and visualize the data streams.

Read: Benefits of Big Data Certification

Uses of Big Data

1. Personal Development:

On a further individual level, big data is being used to optimize own health. Armbands and smartwatches use data of sleep cycle, calorie consumption, activity levels, and more to develop insights on developing the user’s health- which supplies back to the individual user in a personalized manner.

Advertising by Marketing companies are using a variety of data points, including GPS, traffic patterns, eye-movement tracking, etc. to define what advertisements people are more moved in, thereby establishing a more specific marketing strategy. This is a break from the traditional marketing strategy, where the pricing was ‘per impression’ of the ad.

2. Supply Chain Optimization:

Big data is playing a big role in delivery route optimization, where traffic data, driver behavior, etc. live are tracked using radio frequency identifiers, and GPS systems, to recognize the right route to take, depending on the time of day and year.

3. Weather Forecasting:

Applications on mobile phones are being used to crowdsource information about weather patterns, in real time. By using a combination of setting thermometers, barometers, and hygrometers, these apps can make detailed real-time data for predictive models, which can tremendously improve the accuracy of weather forecasting systems.

Read: Impact of Big Data on the World in 2018

4. Building Smart City Infrastructure:

Cities are leading Big Data analysis systems to develop intelligent city infrastructure. Drought-ridden California used big data analytics to track water usage by consumers, easing the cut-down water usage by 80%. Los Angeles has decreased its traffic jam by 16% by monitoring traffic signals around the city.