Nivedit Majumdar Nivedit Majumdar

Big Data meets Context

In the last few years, big data has risen to a global prominence like never before. More  and more organisations are joining the bandwagon on big data analytics to gather a better and more meaningful perspective on the large amounts of data that are being dealt with each and every day.

Also, it is worth noting that due to the large amount of technological advancements and the improvement in the overall interconnection of the world as a whole (thanks, Internet!), the amount of information and data being generated is also increasing by leaps and bounds. Therefore, it becomes all the more imperative for big data tools to be more adept at handling all this data.

I believe that context is one way of improving big data tools, and that is the focal point of my article here.

CONTEXT AND BIG DATA: AN AGE OLD RELATIONSHIP

Context has, in a way, always been at the helm of affairs in business decisions through big data. Through context, organisations and companies all over the globe can derive various trends in patterns and appropriately form relationships from both unstructured data as well as related structured data.

emberify_big_data_market_forecast_worldwide_2011_2017_billion_us_dollars
(Data Source: Statista)

The best instances to substantiate this point would be the sheer popularity of social networking sites in recent times. Twitter and Facebook have gathered millions of users and they basically build up an observational space in which trends and attitudes of the populace can be ascertained – all this thanks to a simple hashtag!

Companies and organisations can accordingly keep track of the general trends and accordingly make decisions. It’s all a cause-effect relationship out here, and context would be providing all the details of the changing landscapes, and this added to big data would enable organisations to make planned crucial decisions.

THE KEY QUALITIES OF BIG DATA

According to IBM, Big Data consists of four Vs: Volume, Velocity, Variety and Veracity. But a fifth V could be added in the form of Value.

emberify_elements_of_big_data
(Data Source: IBM and i-scoop)

Let’s study all these aspects in detail. I’ll also be explaining about how each aspect can be substantiated with the help of Context, so that should clarify the importance of Context in Big Data Analytics.

VOLUME

The entire crux of big data is dealing with the sheer volume of information generated. Context would enable big data tools to gather a more intelligent approach to tackling this volume, and the overall efficiency would be improved greatly.

VELOCITY

While we’re on efficiency, the major factor that would be addressed if Context is applied to Big Data would be in terms of the speed of analysing all the data. Improved speeds would imply quicker decision formulations, which would make business strategies all the more flexible.

VARIETY

As I’ve mentioned before, the data is growing in amounts and in varieties as well. An apt knowledge of all the varieties would hint at the changing consumer tendencies and trends, which would in turn make organisational decision formulation more robust.

VERACITY

Accuracy is the crux here, and the amount of trust on a data analysis tool would improve greatly if the tool has a Contextual aspect to it.

VALUE

Finally, the most relevant aspect of the entire system. How important the process is to a stakeholder and a shareholder would be ascertained by the value, which can be improved greatly through the presence of context in the system.

AN OVERVIEW ON ANALYTICS

I’ve already spoken about Analytics tools and the various forms of analytics in my article here, but just for a recap, here are the types of analytics.

emberify_types_of_analytics

DESCRIPTIVE

This is the most common type, focusing on statistics and data regarding “what happened?” in a given period of time. It is easily available, and server tools like Google Analytics are very popular in this regard.

DIAGNOSTIC

A deeper insight into the statistics gathered, to understand “why some things happened?”. Needless to say, this requires a very high level of expertise and has limited abilities, but when done right, it can provide some valuable insights into the driving force behind events.

PREDICTIVE

Based on the data gathered from descriptive and diagnostic analytics, some future events can be predicted. The factor of contextual data and its correlation with other user behaviour datasets comes into play here.

PRESCRIPTIVE

A rare type of analytics type, this takes in the data from the other three types to give suitable recommendations and advice on how to increase the output.

Now I firmly believe that a fifth type of analytic tool might be up and coming in the near future, and that would be Contextual Analytics. According to a report by IBM, contextual analytics would denote the incremental context accumulators that can detect like and related entities across large, sparse, and disparate collections of data. This data would comprise of both current and historical data – a concept which I have touched upon in my article on Contextual App Interactions.

The key driving forces in this regard would simply be the completeness of the data that has been received. Every data observed would result in a new style of operation, and every new style of operation would make organisations and their big data tools more robust to combat the rise in data all over the world.

ENTER REAL TIME ANALYTICS

Analytics tools till date have merely been able to comprehend and formulate strategies based on observations over a period of time, and the biggest drawback in this scenario would be that the data is old. We’re in a day and age where information is produced in massive amounts – in fact, almost 3 zettabytes of data existed in the world in 2012, and this number doubled every year through 2015, according to a statistic by the International Data Corporation.

emberify_key_analytic_elements_of_big_Data
(Source: IBM)

This makes it all the more important for real time data analytics – a feat which I think might be achieved by contextual analytics. Context at the end of the day aims at painting a picture of stimuli and driving elements in real time. Target markets could be sought out faster, products and services could be designed better, risk analysis would be improved and more business opportunities would be available for the organisation. All in all, real time analytics would improve the way companies assess numbers.

TO CONCLUDE

The combination of Contextual Awareness and Big Data Analytics would result in the birth of Real Time Analytics – an intelligent, flexible and powerful business tool which would power organisations to new heights. It would result in better results, and ultimately improve the overall functioning and efficiency of organisations and the market in general.

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