Big Data is a term which describes a large volume of diverse, complex and fast-changing data, derived from new data sources. These data sets are so extensive that it is difficult to manage it with the traditional data processing software or the traditional software management tools.
Now, every organization aims to make their mark in this competitive market. Effective data management is extremely essential for this. And handling huge chunks of data is no easy task. This is where Big Data comes into the picture.
To begin with, data management is not just another competency factor on the shelf. It is more of a critical differentiator which can determine the market winners. It’s not the amount of data that is important. What matters the most is how well businesses manage the data and what they do with that data.
What can Big Data do?
If you are still wondering why to incorporate Big Data development in your business; here are a few reasons why:
- Big Data can determine user behaviour
- Big Data is capable of conducting predictive analysis
- Big Data helps in deriving insights to frame business strategies
- Big Data can conduct processes so as to extract value from data sets
Keeping all the above-mentioned factors in mind, organizations and businesses are looking forward to Big Data development. Also, by incorporating Big Data, they can come up with new initiatives and reformed strategies which have the capability to transform any business.
In addition to that, the applications of Big Data are not just limited to software or application development. Big Data development is used in many other sectors like:
- Environmental research
- Informatics and cybersecurity
Since the amount of Big Data keeps on increasing exponentially, it is not easy to analyze it. But, proper management and study of that data can help you to make an informed decision for your business. Thus, to simplify things, it is necessary to understand the different types of Big Data.
Types of Big Data:
Big Data can be broadly classified into three categories:
Any data which you can store, process and access in a fixed format can be classified as structured data. The data is already stored in databases in an ordered manner. The format of data and how to derive value out of it is priorly known.
Example of structured data: Information stored in any database software
Any data which has an unknown structure or format can be termed as unstructured data. The data size is massive and it is not easy to derive value out of it. The data can contain a mix of text files, videos and images.
Example of unstructured data: The output for any Google search
It contains both the above forms of data. Many a time, if the data is defined but not structured, it can be classified as semi-structured data. Semi-structured data contains information which contains organizational properties but is not in the traditional database format.
Example of semi-structured data: Any data stored in an XML file
Three V’s Of Big Data
In the early 2000 ’s, Gartner analyst, Doug Laney articulated the concept of Big Data in the form of three V’s. Also, as Big Data comprises of data creation, storage and retrieval; it can be characterised remarkably in terms of:
The definition of Big Data itself denotes a copious amount of data. All businesses have huge amounts of data which includes data collected from — business deals, transactions and investments, social media stats and other data as well. Thus, the volume of data is crucial as it gives an idea about how to extract value out of the data.
Also, whether a certain data set can be determined as Big Data or not depends on the volume of the data. With Big Data, you will process high volumes of low-density data. The size of the data could range from terabytes or petabytes.
Hence, volume is one important parameter which has to be considered when working with Big Data.
Velocity refers to the speed at which data is generated. Generating includes both; receiving of data and the action performed on the data. How fast the data is generated and processed determines the actual potential of the data.
Also, the stream of generated data is unprecedented and massive.
So, velocity in Big Data deals with how quickly the data flows in and from what sources. The data inflow is continuous and voluminous as it comes from a number of sources- Business transactions, social media sites, application logs and other networks.
Processing and functioning with this unprecedented data stream will determine the real potential of the data. That is why velocity is crucial to Big Data.
Nowadays, when it comes to data, we are not just limited to plain text data or structured data in the form of databases. Data means different types of data — structured, unstructured, numeric, audio, video, pdf, financial transactions and ticker data. All these different types of data require different preprocessing and correct handling to derive context out of it.
Also, a variety of data means different ways to mine, store and analyze each type of data. As applications have evolved to large volumes of users, agile processing is required and traditional databases are not enough to generate business value.
Thus, variety is included in the three V’s of Big Data characteristics.
Big Data analysis has a definite business value. Incorporating it into your business has five major advantages-
- Cost reduction
- Exponential growth
- Smart decision making
- Optimized offerings
- Reduced product development time
Nowadays, more and more businesses are using Big Data to outperform their competition and analyze data. So, don’t wait any longer to exploit this excellent business opportunity. It will cost your business a lot in terms of time, money and resources.
Moreover, Big Data development will open up new opportunities for your business and help you get a better perspective of consumer preferences. Combining Big Data with high powered analytics will help your business in accomplishing complex tasks smoothly and without any hassle.