We are living in a world, where the technology has evolved to an extend such that almost everything we come across or use everyday is digitized. For example, we have an automated house hold equipment in our home which we can control by sitting in our office , we have smart phones , smart cards , we do a lot of on-line shopping, we use Twitter, Facebook, Linked in etc. Almost everything we do result in some kind of data generation now-a-days and the rate of data generation gets multiplied several times each year when compared to the year just before. Since the data is of different variety and huge , the traditional techniques which have been using is not so efficient to store and process this sort of data in a reasonable time. In short, the data grows too ‘BIG’ to big data.
What is Big Data?
“Big Data” can be considered as a kind of explosive word now-a-days. As the name says, “big” data is actually the data which is quite big. It consists of different varieties of data which are generated in a very high speed. In other words, big data is the large sets of structured or unstructured data which cannot be processed by using traditional databases or techniques.
How data becomes Big Data?
In the earlier stages, the data was entered into the system by data entry professionals. Later, when internet and social networking became popular, people started generating data by their own in the form of images, videos and texts. The amount of data that is generated at this scenario is huge compared to the previous one. And now, devices are generating data in addition to the two other sources which cause the quantity of data even several times higher on each passing day. As per the statistics, the amount of data that will be generated by 2020 will be close to 45 zettabytes (1.0 × 1021 bytes) . The popularity of IoTs (Internet of Things ) contribute even more to the show.
Lets take social media as an example. We update status which is in the form of text, upload images or videos on Facebook, or Twitter. The data which is in different formats get into the scene. The amount of data get accumulated into their servers each minute is massive. For perspective, the pieces of content that gets shared in Facebook in a day is is almost 1 billion.
We do smart card shopping. Our smart card company will store the data of the shops from which we make purchases. The shops may store the details of items which we purchase. When we do an on-line shopping, we will be able to get a list of item which we have viewed so far and there will be another list of suggested items, which are the items purchased by the people who has bought the same item as we did. The on-line retailers are storing each and every click happening on their site, not only the purchases.
Sources of Big Data
1. Smart phones
4. Social networking
5. Online shopping
Characteristics of Big Data
The main characteristics of big data is said to be as follows which can be depicted by 3’V’s. Volume, Velocity, Variety.
Volume: Volume defines the amount of data . The data generated is in huge volume. The volume of data that is produced now is more than 50 times than that of 10 years back.
Variety: There are different varieties of data that gets generated which includes unstructured, semi structured and structured data. The data can be in the form of text, images, video, music or any other format. In short, there will be a large variety of data.
Velocity: The velocity or the speed in which the data is getting generated is very high.
There is another characteristic called Veracity which points to the uncertainty in the data that is being stored. The data which is stored and being analysed cannot be ensured to be solving the problem for which the analysis happens.
How to make use of Big Data?
Since huge amount of data is being generated and stored in their servers, the think tanks in big companies thought of making use of big data in their business. The data scientists started analysing the big data to find out some patterns or insights which is hidden in the data that enable them to market their product or service much better which is called Big Data Analytics. The big fishes like Facebook analysed the big data to give suggestions and recommendations to the users on their interest areas and started an advertising model which is quite a great success in their run. The term widely known as ‘Predictive Analysis’ came into picture which make use of the data to analyse the trends and and predict the future growth.
Benefits of Big Data Analytics
1. Analysing big data helps in better decision making.
2. Big data analysis helps in targeted audience advertisement and improved marketing strategy. By analysing data, a firm can understand which group of people will help them to get their business to the next level so that they can market their product to a similar crowd to achieve better results.
3. The increase in customer base in turn increases the revenue of the company on big data analysis.
4. The analysis of data helps in the health care domain to predict and diagnose diseases in a much better manner.
Challenges in Big Data Analysis
The main challenges are as follows:
1. Since data is so huge, chances are there that the relevant information can be missed out during the analysis process.
2. Skilled Professionals who are efficient in analytic is not too easy to achieve.
3. Need of meeting speed has to be addressed since the result of analysis are being used in sectors like share markets where fluctuations happen so abruptly.
4. The quality of data being stored cannot be ensured always and hence the result of the analysis also gets affected by that.
Disadvantages of Big data
Since the data which gets entered in the internet is being used for the analysis purposes, the users cannot be specific in which all information of theirs can be used for the same. The privacy of the users data cannot be always protected.
The big data revolution has begun and now lets wait and see what is happening.. 🙂