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Technologies for
Game of Data and it’s Analysis

By:  Shreya Parmar    

Technologies in Data Analysis featured image

Everybody is gabbling in Data Analysis, Big Data, and Google Analytics. Where is that all coming from? With Data, I would like to introduce 3 more terms; Information, Knowledge and Intelligence. What is Data? Answer is Information. Okay then what is Information? Is the answer Data? In fact these 4 terms differ a little bit in meaning.


Suppose you have a Pen and 20. Here “20” is Data. A question will arise: what is 20? Is it the no. of pens? Or the amount of pen? Let's make it clear. You have a 20 ₹ pen. A slight change and the whole meaning is altered. Now this 20 ₹ becomes your information. Human mind is designed in a way which looks first at their own benefit. From this information your knowledge will examine for attributes this pen is having. Are these attributes worth enough of 20 ₹? Now that you have proper knowledge about the 20 ₹ pen, your intelligence will decide whether to buy that pen or not!


What is Data Analysis?

Data analysis is a methodology that comprises multiple activities such as data collection, data cleaning and organizing data in such a way that it can be analyzed to extract top insights that support our decision making.


Wikipedia shares their discovered origin of Google Analytics. “Google's service was developed from Urchin on Demand. The system also brings ideas from Adaptive Path, whose product, Measure Map, was acquired and used in the redesign of Google Analytics in 2006.”


The growth of the IoT market doesn’t seem to stop whether it’s business or personal use, this technology won’t say Goodbye. There’s no doubt that the Internet of Things fulfills all of our aspects of life, ranging from urbanization to industrialization. IoT technologies keep on advancing quality and comfort of life. What makes them effective is their collective use by the business groups to obtain relevant results for proper management and implementation. And hence there is one use of it in data analysis too. As IoT is able to perform in every form whether it is data collection or data cleaning and that is what data analysis is all about.


While the growth of more and more technologies based on the Internet of Things, Data collected is growing exponentially I believe. Not linearly but exponentially. Here comes the need of effective tools to analyze and control this huge amount of Data. Evolved Analytics authorized with Artificial Intelligence and Machine Learning gives a helping hand to IoT devices by hastening Data Analysis.


That’s a lot of data together.. If Sherlock Holmes were here….. I would bet he would say his dialogue on these data analysis and other data containing terms: “Data! Data! Data! I can't make bricks without clay.”

A person sitting on top of a pie chart showing data analysis

Here are some technologies which will help in building Data Analysis tools:

The characteristics of these techs should be that it should ease your job of data analysis. Because at last if those technologies are not going to alleviate those big data analysis works then it will be offensive for the on-the-way Tech world which is still evolving.

A person sitting on top of a pie chart showing data analysis

I. Data Virtualization

Data analysis or any other field containing data can’t be empty without Data visualisation. It enables applications to retrieve data without implementing technical restrictions such as data formats, the physical location of data, etc. Used by Apache Hadoop and other distributed data stores for real-time or near real-time access to data stored on various platforms, data virtualization is one of the most used big data technologies.


II. Predictive Data Analysis

One of the chief tools for firms to avoid risks in decision making, predictive analytics can help businesses for lowering the threats. Predictive analytics hardware and software solutions can be used for finding, evaluating and at last deployment of predictive scenarios by processing big data analysis. Such data can help companies to be prepared for the future and help solve problems by examining and exploring them.


III. NoSQL Databases

Not-only-SQL. Big data analysis, we know from the beginning that there will be a huge amount of data. in NoSQL; data is not split into multiple tables, as it allows all the data that is related in any way possible, in a single data structure. When you work with a huge amount of data, you don't need to worry about the performance lags when you query a NoSQL database.These databases are used for reliable and efficient data management across a scalable number of storage nodes. NoSQL databases store data as relational database tables, JSON docs or key-value pairings. Hence NoSQL plays a little


IV. Tools for Knowledge Discovery

These are tools that allow businesses to mine big data which is stored on multiple sources. These sources can be different file systems, APIs, DBMS or similar platforms. With search and knowledge discovery tools, businesses can isolate and use the information to their benefit.


V. Distributed Storage

A way to counter independent node failures and loss or corruption of big data sources, distributed file stores contain replicated data. In data analysis, sometimes the data is also replicated for low latency quick access on large computer networks. These are generally non-relational databases.


VI. Data Integration

One of the challenges for most businesses handling big data and performing data analysis is to process terabytes of data in a way that can be useful for customer deliverables. Data integration tools allow businesses to smooth data across a number of big data solutions such as Amazon EMR, Apache Hive, Apache Spark, Hadoop and MongoDB.


VII. Data Preprocessing

These software solutions are used for manipulation of data into a format that is consistent and can be used for further analysis. The data preparation tools accelerate the data sharing process by formatting and cleansing unstructured data sets. This tech has only one imitation which is that all its tasks cannot be automated and it also requires human oversight, which seems quite boring.


VIII. Data Quality

An important variable for big data processing is the data quality. For data analysis, the data quality software can conduct cleansing and enhancement of very large data sets by using parallel processing. These softwares have main use for getting consistent and valid outputs from big data processing.


Our main challenge is to put up the “Best” we have in a “Smart” way. The smart solutions should be built with the most sophisticated devices mixed up with a spoonful of practical knowledge, a pinch of experience and half a teaspoon of expertise. C.S. Lewis said that “The task of a modern educator is not to cut down jungles, but to irrigate desserts.”

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