Big Data Analytics – the next human leap

Since the past few centuries, there have been initiatives taken to come up with methods to predict human behavior. The current initiative is an effort to understand the world using ‘Big Data.’ Efforts have been made to assess behavior based on models developed on human nature. The basic understanding of big data involves gathering huge amount of data, observe the patterns emerging from it and estimate how things or people in this case will act out in the future.

Viktor Mayer-Schönberger and Kenneth Cukier clearly define in their book ‘Big Data’ that big data is a movement from causation to correlation. The people who happen to use big data are not psychologists or novelists, who apply the intuitive perspectives to explain the causal chain of happening of events. Many of them are analysts, statisticians and mathematicians that apply these techniques to loads of amounts of data in database to ascertain valuable information. Popular ecommerce companies like Wallmart use it for assessing the sales trend and inventory management outlook. A number of doctors in big hospitals use it for clinical trials to undertake the diagnosis of certain diseases in an anteceding manner.

Correlation is the key attribute of Big Data, which holds a great importance in developing perceptions about a certain process or service. Big Data helps in discerning meaningful correlations from meaningless ones by relying on certain causal hypothesis. Thus, Big Data Analytics helps in drafting a clear picture of what leads to what and what happens eventually.
Like beings ruled by instinct, we are discontinuous most of the times. Hence, the data collected may be ambiguous and full of outliers. Past mistakes help us to learn or mislearn. Hence, unpredictability is shelved with us and to understand about it, one needs predictive modeling – a service offered by Big Data Analytics.

Big Data has attained fame because it is able to store different types of information. The diversity allows analytics of data considering different views and dimensions. Hence, the concept is said to be broadly applied in the investment industry where huge amounts of data needs to be analyzed and patterns need to be deduced to assess the upcoming tick price for individual financial instruments (like Stocks).

Not only is Big Data Analytics limited to the finance industry, but also it finds its use in the corporate world. Decisions regarding production line and sales strategy involve studying of different reports. Big Data Analytics provides feasible reports that cover different realms of the organization and help in taking better decisions. Thus, with great potential of applications in different sectors, Big Data Analytics is said to be the next big human leap.

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3 prime needs to choose Big Data Analytics

The rise of the dot com industry brought in a huge surge of data. The beginning of millennium brought in the intense requirement to manage huge amount of transactional data associated with ecommerce and other online businesses. Computers took over the working of departments of several industries bringing in the concept of enterprise wide data. Many forerunners came up with enterprise based solutions to tackle the terabytes of data, but the scalability was limited due the use of relational databases.

Big data is defined as collection of datasets, so vast and assorted, that a single database following relational storage mechanism is unable to store them. The traditional database management tools strike to be incompetent, when it comes to handle the complex data covering the different horizontals of the mentioned industry. To manage this special kind of data, we have Big Data Analytics.

Big Data analytics includes a process of analyzing large amounts of data with an intention to find useful patterns, usable correlations and other hidden opportunities that are not visible directly. Here are the 3 basic requirements which Big data analytics fulfills to make any business survive.

1) Effective decision making –
Big data analytics helps in scrutinizing data from different sections irrespective of category or department. This helps in finding hidden relationships which could seldom be found using analytics for typical relational databases. This assists the top management in making decisions lucrative for the business.

2) Capacity to manage industry wide data-
It is proven that all the departments of the industry are said to be inter-related and work in tandem to deliver the final product or service. Big Data allows the stratification of data with specific requirement based storage for individual departments. The IT managers find the implementation of Big Data Analytics feasible and help in providing reports aggregating diverse data sets keeping in congruence with the data relationships.

3) Use of predictive analytics in place of deterministic approach-
Traditional approach of analyzing data included querying of relational databases. This would take a considerable amount of time scrutinizing loads of datasets associated with different horizontals. Further, the portability of data would strike as an issue, when relational databases were used. Big Data Analytics employs the use of NoSQL databases like Hadoop and MapReduce. The queries put are dynamic and the nature of output is versatile and probabilistic as associated with the requirements of user. This gives the liberty to determine patterns and fill in the missing values with the use of complex algorithms available in Big Data Analytics. Thus the decision process becomes time critical with greater accuracy.

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Big Data analytics – an IT requirement

There is a persistent hype in market when ‘Big Data’ is spoken of. However, data taken into consideration is nothing new. It’s just presented in a better assorted form with higher volumes taking over. With the rise of Internet industry, the volume of data flowing in and out has become colossal. And that’s when the industry experts came up with the concept of ‘Big Data’.

Big Data is typically a dataset, which happens to be huge and defined by several attributes. Also the defined dataset is not specific to a certain horizontal of industry. It traverses around different levels in the industry and gathers attributes which make it highly impossible to store and analyze for a typical Relational Database Management System. To fill the gaps, technical fore runners have come up with NoSQL databases like Hadoop to store this massive amount of data. However, data stored cannot produce results without applying analytics to it. So, Big Data Analytics is said to be the big news in the market.

Big Data analytics helps in addressing the 3 basic needs of any data – Volume, variety and velocity. The NoSQL databases have an efficient architecture much advanced over typical RDBMS, which provides solutions for storing this huge amount of data. Variety is a concern when the data obtained is from different sections of industry. The datasets encompass different factors that are independent and defined as per the sections of industry. Hence, there is a requirement to propose stratified solution to manage this diverse data. Big Data Analytics provide the feasible solution over the same. And when velocity is concerned, the highly stratified system works independently for every industry section, thus able to grant higher access speeds along with capacity to handle multiuser needs.

With the rise of internet and social media platforms, unstructured data sources have been commonly used. It has been reported that 84% of individuals are analyzing and processing unstructured data which hails from weblogs, social media, email, photos and videos. This data is sometimes in batch or sometimes real time. IT managers face the need to source out these both categories of data equally. However with time, the need to manage real time data is increasing. The prevalent IT infrastructure cannot handle the rising needs without turning cost sensitive. Hence, IT managers feel the need to choose Big Data Analytics.

Analytics of typical databases involved monotonous querying while NoSQL databases like Hadoop do not employ standard SQL querying techniques, allowing versatile queries to be used to gain probabilistic results. This gives an edge for Big Data Analytics to be used.

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