My visit to the Skolkovo Institute of Science and Technology, Russia, as part of the Global Assembly, in September last year, had been a mind-blowing experience. Although I have read about and seen on television the world of data — not to mention the science fiction movies about a data-based world — it had remained in my consciousness as an acceptable distant reality, until I was an eyewitness to the most sophisticated programs and projects underway there, on their way to a future in a digitalised world.
One of the projects which had interested me the most was the solar-fuelled data collecting, or may I say, data extracting device, which was a hybrid of an aeroplane and a drone. Interestingly, it was placed in the zone between the stratosphere and exosphere, which implies that the movement zone of the hybrid device remains in the virgin area between the flight ceiling, at the lower level, and the satellite ceiling, at the upper level. Easy to launch, cost effective, with international border zone clearance and having access to the ID card you are wearing to your office — these were the aspects briefed to us. However, with the potential I saw, the vast scope of data and information collection and super tech surveillance was the USP of the project. Given the survey or sample we seek in quantity, and the actual (physical or/and interactive) modes of collection and process, such a device — even if we start with the basic one — it is possible to be accurate about the actual information we are seeking.
As a child, I had been awestruck by my first use of the calculator and marvelled at what an aid it was. In the same fashion, the technology of collecting data, processing it and producing results is a vital aid for any organisation in today’s world, and should always be upgrading to better versions. Even though it is dynamic and complex, keeping it simple and user-friendly is the need of the hour — take, for example, the iPhone or the iPad and their usage across generations.
Therefore, as professionals, we need to move on from our classic ways to a contemporary outlook, by enhancing our understanding of technology, getting equipped with the skills required and having an open mind and learning to absorb and infer the data assessments with sound managerial knowledge and practices. This has become imperative in order to catch up with the ever-changing pace of the data world.
Big data is an artefact of the human individual as well as the collective intelligence, generated and shared mainly through the technological environment, where virtually anything and everything can be documented, measured, and captured digitally, and in doing so, transformed into data — a process that Mayer-Schönberger and Cukier (2013) also referred to as datafication.
There are big data analysing technologies (e.g. NoSQL Databases, BigQuery, MapReduce, Hadoop, WibiData and Skytree), which can give deeper insight to business strategy development and decision-making processes in critical sectors such as aviation, defence, economic productivity, energy futures and predicting natural catastrophes.
However, these technologies are costly to procure, and professional analysis requires tech-managers and tech-decision makers where there is a generation gap to be bridged. Thereby, its application has become a challenge. Big data analytics is still an option for most in business, rather than a basic necessity. Future managerial implications are vital and cannot be ruled out in all operations and business activities at the micro or/and macro levels. Nowadays, ascertaining and predicting is purely based on data assimilation and analysis.
As a nation, we have captured some, but mostly missed out on data capitalisation. The research opportunities across business domains has to be integral, specified and functional inside and outside its operational and functional areas, so that businesses are safeguarded against becoming obsolete and have tools for taking timely, informed, analysed, and data-based decisions for their survival and betterment. Data analysis has the potential to generate new knowledge and propose innovative and actionable insights for businesses. The various data warehouses and consultancies provide excellent expert services fitted to the requirements of business research. It has growing markets in India and great opportunities for startups to seize.
Though expensive now, with the great value of its impact being witnessed in new business developments across the world, data requirements and its importance are on the rise. The process may be delayed but a super capacity educational infrastructure and academics as well as brilliant human resources will emerge rapidly for the supply of the big data analysts needed right now. Producing more cost-friendly hardware equipment and enhancing cloud computing technologies with quality maintenance is also of prime importance to aid big data analysis.
Data challenges are related to the characteristics of the data itself, for example, data volume, variety, velocity, veracity, volatility, quality, discovery and dogmatism. Then there are the data process challenges, related to the series of techniques, capturing data, data integrations, data transformation, choosing the right model for analysis and projecting the results. Challenges regarding law and management are at the epicentre and need to be looked into to ensure smooth functioning pertaining to privacy, security, governance and ethical practices. Each of these challenges opens the door to newer opportunities for expert skilled work, research and policy framework.
To conclude with an overview of big data analytics, BDA has generated high value for data-enabled analytics but is still an unresolved issue for many organisations. New business development should realise this value BDA has created. It is essential for design, methodology or approach to develop maturity models based on literature analysis and semi-structured interviews with domain experts. The wholesome productivity of the models should be evaluated qualitatively by management practitioners. The application of the models should be scrutinised by big data experts, and finally, the findings should be proposed and the models should be exhausted by domain experts. This should help organisations to develop a more critical understanding and use the results to decide what is to be done next.
There is still a long way to go with big data analytics, but we have to double up with the speed of sound for data model integration in existing industry-developed maturity models into a single coherent big data maturity model that answers the reasons for research on big data, which is to abstract from technical issues and focus on the business implications of big data initiatives.
Captain Priti Sidharth Singh is a commercial pilot flying Boeing 737-700/800/900, logged seven thousand hours on JET serving as Senior Commander (Line Training Captain) at Air India Express, and is also active in Social and Development National Organisations. She is currently pursuing a PhD from IIT.