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By: Priya Saxena


“Those who do not remember the past tend to repeat it”, so it’s extremely important to keep evolving with time. The quantity of computer data generated on planet earth is growing exponentially by every passing day. Organizations working in the logistics, financial services, healthcare and many other sectors are capturing more and more data and wish to generate an additional value from it. The social media is also creating gigantic quantities of digital information that may potentially be mined to generate valuable output. The only matter of concern is, ‘How to manage this abundant data’, also ‘How can this data be mined to get meaningful insights’.


Big Data in the present times have become extremely important in IT world. It involves processing of very large quantities of digital information including a variety of data types, which cannot be analyzed with traditional computing techniques to uncover correlations, hidden patterns and other insights. The analytical findings can lead to more effective marketing, superior customer service, better operational efficiency, and new revenue opportunities competitive advantages over competitor organizations and other business benefits.
In the Present time colossal amounts of data are being generated. In the good old days, normal relational databases were used to manage and access data, but it’s impossible with this dynamic data. Previously employees of business firms used to enter data manually, and then came the existence of social media portals, where data is entered by the users themselves, now the data is even generated by machines such as satellites etc. Currently it has become impossible to take the data and bring it to the CPU (processor), this abundant data would overwhelm the CPU and it won’t be able to process it.
To solve this problem multiple CPUs are brought to the data, in such a way that each CPU is parallel to different servers, there can an infinite no of CPUs for an infinite no of individual servers, this is called parallel processing. This is a technological shift, making the processing scalable larger as data needs.


The problem of storage of the data is solved, now the question is ‘how would it be accessed’. There are unbelievable insights hidden in this data which can be incredibly useful if one knows how to extract the data. The most popular tool used to access the Big Data is ‘Hadoop’; it is the leading big data technology which is used by many leading big data pioneers. It’s an open source, so freely available. It can tell what and where is the useful data in which exact server this data helps in creating new algorithms to create services.
Hadoop distributes storage in processing of large datasets into groups or clusters of server computers, it detects and compensate for hardware failures or other system problems at an application level, this allow a high level of service continuity to be delivered by clusters of individual computers each of which maybe prone to failure. It consists of 2 key components:

Hadoop distributed file system- It permits the high-bandwidth cluster-based storage, which is essential for Big Data computing.

MapReduce- is a data processing framework, based on Google search technology it distributes (maps large data sets) across multiple servers (yellow portion),

Each server creates a summary of the data allotted (blue dots), after this all summary is given back in a reduced state its concept is to reduce data before more complex data analysis tools are used.  

Cloud based data solutions are available for startups or small firms for they cannot afford an inbuilt big data infrastructure.


These two techniques are branch of advanced analytics which are used to make predictions about unknown future events and ensuring that the declarations and statements of a program are semantically correct.
The most important key points that cover the semantic analysis are what semantic analysis is, what it means in the context of data science and machine learning, and why is it important to the marketers.
As you already read above, the machines lacked the ability to determine what is relevant and why. Advances in Machine Intelligence and Natural Language Processing (NLP) has impacted deep semantic analysis heavily through advanced algorithms, and powerful computers, they are getting impressively better by the day.
Semantic analysis is not about teaching the machines, but about getting them to learn.” When seen from a data processing point of view, semantics are considered as “tokens” that provide context to language. They provide clues to the meaning of words as well as their relationships with other words and other tokens. The goal, as it is for any reader, is to look beyond the words on and see the meaning.
For example, a computer can see patterns that tell it things like,
  • a)      ‘Apple’ and ‘fruit’ are semantically related.
  • b)      ‘Apple’ and ‘red’ are more closely related than ‘Apple’ and ‘fruit’.
  • c)      ‘Red’ can mean a ‘color’.

To achieve the semantic understanding the computer would have to make the connection that an Apple is a red colored fruit.
This has proved to be extremely helpful in many ways:
  • ·      In extracting relevant and useful information from huge clusters of unstructured data.
  • ·      Creation of chatbots, which solves problems of users without human interference.
  • ·      Speech recognition (AI).
  • ·      Bots became multilingual.

Predictive analytics uses many techniques from machine learning and artificial intelligence, data mining also statistics modeling to analyze current data to make predictions about future. 
This technology has helped to a great extent to many big shots and even small firms, analyzing the previous data and drawing a meaningful result for the same would be extremely useful to grow. It’ll give a better understanding of the customer needs, market demand, competitor’s strategic plans and a lot more.


The primary goal of big data analytics is to help companies make more informed business decisions. Houssup is a Tech-backed Interior Décor Solutions Platform and focuses on the customer needs. It is soon going to inculcate this technology and work on the semantic and predictive analysis for greater understanding of the customer needs and also predicting the unknown future events.

A famous author once quoted, ‘Without big data analytics, companies are blind and deaf, wandering out onto the Web, like deer on a freeway’, which is true in every sense.