Business enterprises today owe a major portion of their success to a firmly knowledge-oriented economy. Modern organizations of the world are driven by data and therefore recognizing patterns and trends from data sets and analyzing and making sense of the data can turn out to be hugely profitable for any business.
Oxford dictionary defines data as – “The 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.” The term ‘Big Data’ is also data but with a huge size. It refers to all of the data from all kinds of sources being generated across the globe and this data could be either structured or unstructured.
Traditional data management tools cannot store or process such large datasets. Big Data leads to Business Intelligence which leads to better decision making and strategy planning for organizations irrespective of their size or market share. Currently, Hadoop is the go-to platform for most organizations working with large amounts of data. Analyzing data at extremely high speeds and volumes will put companies in the front line in the race to capture newer markets and customers.
‘Big Data’ Categories:
1. Structured Data: Any data that resides in a fixed field within a record or file is known as structured data. First a data model, outlining the type of business data to be recorded and the methods to be used to store, process and access it, needs to be created. It has the advantage of easy storing and analyzing. Structured data includes data contained in relational databases and spreadsheets. Before constraints relating to costs, these two were the major choices for most firms wanting to effectively manage data.
2. Unstructured Data: Typically not considered as a good fit for the mainstream relational database, unstructured data is information in many different forms that doesn’t hew to conventional data models. With alternative platforms emerging for managing such data, many IT systems can now make use of it to generate business intelligence. Text is one of the most common types of unstructured data and is collected from word documents, emails, blog posts and other social media sites. Other forms of unstructured data include audio, video and image files and machine data.
3. Semi-structured Data: Data containing semantic tags but not conforming to the structure associated with relational databases is termed as semi-structured data. Semi-structured entities belonging to the same class may have different attributes. Examples include HTML, email and other markup languages.
Characteristics of ‘Big Data’
The 4V’s are used to describe ‘Big Data’.
1. Volume: The value derived from a dataset depends on its size. Businesses collect enormous amounts of data and thus volume plays a critical role when it comes to analysis of data.
2. Velocity: The data generation speed is termed as velocity. Data of all types is generated from a variety of sources and the speed of processing this data determines the real potential.
3. Variety: Understanding the type of ‘Big Data’ is critical for value to be derived from it. Data is present in a heterogeneous form in various formats like photos, videos, emails, PDFs, audio files, spreadsheets, databases, etc. Unstructured data poses difficulties in storing and analyzing processes.
4. Variability: The term refers to the inconsistency shown by data at times which hampers the data management process.
Once tools are used to convert Big Data into meaningful information pockets, then decision making in the enterprise becomes easier. The needs of the customers with respect to the product and services being provided will be known beforehand, potential markets can be identified, ways to reduce costs and build up a higher economy of scale can also be planned out. Time and cost benefits provided by mining and analyzing ‘Big Data’ has made organizations all over the globe to dive deeper into this technology.
