Big Data

How Big Data Science and Analytics is the Lure for Businesses Today

The leveraging of machine learning and traditional algorithms to analyze the Big data for any organization can solve problems in multiple verticals
How Big Data Science and Analytics is the Lure for Businesses Today
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Managing Partner and Regional Head, Europe & Mainland Asia (EMEA), KiE Square
6 min read
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It’s high time to change the proverb ‘Survival of the fittest’ to ‘Survival of the smartest’. The exponentially growing global economy, fast-paced business world, and ultra-modern technological advancements are compelling everyone from a small company to big corporations to increase their client base and grow the business more.

Big data science and analytics have changed the course of market strategies and paved altogether new paths for the growth and profit of the companies. We have entered the digital age in this decade and the big data analysis is the latest digital technology that has accomplished even unbelievable tasks in real-time. By the end of 2020, the big data volume is going to reach 44 trillion gigabytes, breaking down all the previous trends and setting a new business world.

Coexistence of Two Systems

The leveraging of machine learning and traditional algorithms to analyze the Big data for any organization can solve problems in multiple verticals and forecast the business future with  greater speed and reliability. Data analytics has been in the Business Intelligence space for quite a long time providing ‘Point solutions’ for specific problems in any business.

For example, Customer churn forecasting, Repayment risk calculation, Customer default propensity, Price points optimization for promotions etc. have been some prominent point solutions across sectors like Insurance, Telecom, FMCG, Retail, Banking and financial services. While the traditional ‘Causative model’ solutions by business analytics providers  help in explaining the underlying explanations of a business problem and any corrective measures for the same, it does not often provide a real time systemic approach to the same. The big data analytics does not only develop a high speed reliable solution but also organizes a variety of structured and semi structured sources of company and external data for multiple systemic uses.

Big data analysis originated from data science and it encompasses mathematics, statistics, and many other scientific tools for the analysis of ever-increasing data. With the help of AI applications and machine learning, predictive analysis is performed that brings results categorized into various domains catering to requirements of different business verticals. These accurate predictions help in accentuating the business growth very effectively.

Having a look at recent reports, there has been a large shift in companies opting for big data and analysis, telecom and financial services being the industry leaders in opting for this. In fact, the vast implications of big data have been foresighted by industry giants and the science is being combined with Internet of Things & Services (IoT/IoS) to leverage the maximum advantage for organizations. This underlines the fact that analytics would move from erstwhile point solutions to enterprise phase in its true entirety for the very first time.

Advantages

The big data science and analytics have three major advantages – these are the turnaround speed through distributed computing, varied to limits by virtually adopting any data source, and ability to churn much greater volumes of data. Although organizations are still confused about the co-existence of their already existing Data and BI Systems with the big data, yet the underlying potential of bringing profit to the organizations compel them to take the initiative.

Currently it may not be possible for Big data to supersede the existing data systems, so the two systems would co-inhabit the workspace till the organizations are able to adapt to the Big Data Systems & complexity. The related more important question is the division of Big data infrastructure and capabilities in-house vis-à-vis off shore. As organizations prefer to retain certain kind of confidential and core business data at their premises and only release non-core data to offshore, Hybrid data clouds are being implemented, that divide data and work zones between in-house and offshore. As a result of these two complexities, the Decision makers of the companies, IT heads and service providers have to actively design the Big Data Ingestion Pathway, otherwise it could diminish the ROI significantly.   

From the solutions provider point of view, all point solutions shall become a part of the big data systems and become a segment of the service platform. This Platform-as-a-service (Paas) becomes pertinent in big data science and plays an important role in not only delivering a variety of solutions to choose from but also take away the Capex driven models to Opex driven models. The choice of modern-day obsolescence prone and expensive infrastructure such as Solid state drives which is a high capability environment coupled with in-memory technology, may only be justifiable in a Cloud based Opex model

Impact of Big Data

Big data can make a huge impact in infrastructure oriented business because in this sector, coupled with IOT/IOS (internet of things or internet of services) it will have a more visible impact. Infrastructure industry including manufacturing and retail can greatly benefit from big data science. There is tremendous scope for machine level or customer interface level interventions to increase the business opportunities manifold. These interventions typically include customer marketing opportunities and risk reduction needs. Cyber security also benefits from big data advances as real time traditional or artificial intelligence based pattern recognition and clustering algorithms are both going to be extremely useful for minimizing the security and transaction risk.

Government and Public Sector

Government and Public Sector machinery has been one of the biggest investors in big data, machine learning and data analytics space. From macro modeling (market level models) to micro modeling (entity/ transaction level risk models) to 360 profiling, the monetary potential of such implementations in India is worth several billion dollars by 2020.  

The global scope for the same in government and public sector is a multiplicative worth the notice out of a USD 50 billion Total Big data market size in 2020 (Source: Statista 2018). Whatsoever opportunity is there for big data and analytics, the initial demonstrable success is still important for big data to ensure sustained investment in this area. The biggest threat to this growth story may still be the high quality skill sets that are required to make the Big data implementations successful.

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