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Challenges for Big data Analytics in India

, September 6, 2015, 0 Comments

big-data-analytics-challenges-performance-marketexpress-inBig data is taking its startling curve,  everywhere we hear about data and related analytics.  It helps us take a step further by making decisions based on real-time data rather than only past and big bang experiences. The power to foresee the driving forces of the market is now in the hands of  the young generation, who are exploring and experiencing astonishing volume of data across different disciplines and areas.

Performance with Big data

Big data is in its infancy stage at present in India, mostly we are dealing in data warehousing but gradually we are heading towards solutions. The learning is percolating from global companies and importantly from one’s own work experiences in a specific discipline or domain. Agile analysts are smartly piling up unstructured and semi-structured data efficiently and unravelling hidden patterns or latent layers; the corollary is that, demands, performance and quality are being well anticipated and worked out.

A majority of the firms and companies leverages Big data to identify the business potential, merchant-customer relationship and possible approachable services to customers. The growth, revenue, operational excellence and cost reduction are optimized to finer levels because of implementation of Big data technology in India. Insights about customers, their preference and behavior, helps technically to decipher the use of products, consumption behavior in population and utility & response of money and services.

Challenges in Big data

Working on Indian data has been a challenge to infer insights. Data are being compiled in fragments and hence the inference can be made for selected segments in a short duration.  Gradually it is predicted that India would become the hub of data warehousing of one of its kind, which has a huge implication on regulating the market. This impacts on  business precision and to comprehend consumers’ behavior and preferences. The solution analysts are using statistical methods and mathematical modelling so that different set of information is integrated and aligned to provide insights that would be robust and stable.

The first step required in deriving decent insights from Big data depends on the methodology and the analytics one opts for. The change in the outlook of data (Big data) brings a shift in the approach, from classical to non-parametric methods. Non-parametric methods have large acceptance, which can be applied on real-time data. Second important step is to understand the application of Bayesian methods, probabilistic methods (including conditional probabilities) and the process of randomization over the Big data, which can be applied over segmented data.

None of these have better methods, compared to each other; most of the analysts are using classical methods to meet the assumptions and assuming it provides robust estimates. However, on the flip side it is not available for the entire distribution which limits the strength of classical methods.

Also, probabilistic estimates cannot be used with confidence; however, conditional probabilities do provide good insights from Big data, as it partly incorporates the information of complete distribution. In caveat, randomization process makes all the sense in the current age of Big data as it allows to construct data-dependent distribution function and its moments.

And therefore, you can have series of non-parametric distribution as you update the data or plug-in data in segments by series. Over time, it gives a realistic view of the distribution of the underlying data, which might be different from known distributions functions.

The milestone to achieve in Big data is to construct a generalized methodology to generate distribution functions empirically based on randomization process for Big data so that every set of data is independent of any subjectivity of fitting distribution, and then insights are based on moments of the distribution obtained.