Lets understand the concept of big data
We know data is actually quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media.
Now, What is Big Data?
Big Data is also data but with a huge size. It is a term used to describe a collection of data that is huge in volume and yet growing exponentially with time. In short such data is so large and complex that none of the traditional data management tools is able to store it or process it efficiently.
Examples Of Big Data
Following are some of the examples;
1. Social Media
The statistic shows that 500+terabytes of new data get ingested into the databases of social media site Facebook, every day. This data is mainly generated in terms of photo and video uploads, message exchanges, putting comments etc.
2. Jet Engine
A single Jet engine can generate 10+terabytes of data in 30 minutes of flight time. With many thousand flights per day, the generation of data reaches up to many Petabytes.
It is the most loved American entertainment company specializing in online on-demand streaming video for its customers. Netflix has been determined to be able to predict what exactly its customers will enjoy watching with Big Data. As such, Big Data analytics is the fuel that fires the ‘recommendation engine’ designed to serve this purpose.
Types Of Big Data
It could be found in three forms;
Any data that can be stored, accessed and processed in the form of a fixed-format is termed as ‘structured’ data.
An Example Of Structured Data
An ‘Employee’ table in a database is an example of Structured Data.
Any data with an unknown form or structure is classified as unstructured data. In addition to the size being huge, unstructured data poses multiple challenges in terms of its processing for deriving value out of it. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos etc.
Examples Of Un-structured Data
The output returned by ‘Google Search’
Semi-structured data can contain both forms of data. We can see semi-structured data as structured in form but it is actually not defined with e.g. a table definition in relational DBMS. An example of semi-structured data is data represented in an XML file.
Examples Of Semi-structured Data
Personal data stored in an XML file
Characteristics Of Big Data
(i) Volume –The size of data plays a very crucial role in determining the value out of data. Also, whether a particular data can actually be considered as Big Data or not, is dependent upon the volume of data. Hence, ‘Volume’ is one characteristic that needs to be considered while dealing with Big Data.
(ii) Variety – Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. During earlier days, spreadsheets and databases were the only sources of data considered by most of the applications. Nowadays, data in the form of email ids, photos, videos, monitoring devices, PDFs, audio, etc. are also being considered in the analysis applications.
(iii) Velocity – The term ‘velocity’ refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data.
Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks, and social media sites, sensors, Mobile devices, etc. The flow of data is massive and continuous.
(iv) Variability – This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.
(iv) Value – This is the last and most important V of Big Data. It sits at top of the big data pyramid. This refers to the ability to transform a tsunami of data into business
Why is Big Data Important?
Every company uses data in its own way; the more efficiently a company uses its data, the more potential it has to grow. The company can take data from any source and analyse it to find answers which will enable:
Cost Savings: Some tools of Big Data like Hadoop and Cloud-Based Analytics can bring cost advantages to business when large amounts of data are to be stored and these tools also help in identifying more efficient ways of doing business.
Time Reductions: The high speed of tools like Hadoop and in-memory analytics can easily identify new sources of data which helps businesses analyzing data immediately and make quick decisions based on the learnings.
Understand the market conditions: By analyzing customers’ purchasing behaviours and current market conditions, a company can find out the products that are sold the most and produce products according to this trend.
Using Big Data Analytics to Solve Advertisers Problem and Offer Marketing Insights :
Big data analytics can help change all business operations. This includes the ability to match customer expectation, changing the company’s product line and of course ensuring that the marketing campaigns are powerful.
Big Data Analytics As a Driver of Innovations and Product Development :
Another huge advantage of big data is the ability to help companies innovate and redevelop their products.
Comment your thoughts below.