Top MBA Colleges in Delhi NCR and across India have a new fashion fad. While it may seem like a hyperbole to call big data analytics a mere fashion fad, it makes enormous good sense to assert that corporations in India are highly unwilling to jump onto an IT investment cycle bandwagon at the earliest. The tight rope walk that Indian companies prefer to do while balancing operating and financial risks, the imitation lag hypothesis, the currently existing bear phase of the global economy thanks to the slash in commodity pricing and a set of conservative people unwilling to accept even the change in position of office furniture add up to the effect of non-acceptance of the next big thing in IT. Needless to say business schools in Delhi NCR and across India are only slower to incorporate the emerging trends of business automation in their academic curriculum and follow the footprints of their corporate peers.
Achieving a Transformation in the Approach towards Digitization
Achieving a turnaround in the state of business process automation is a question of necessity. It is not a question of compulsion. At Ishan Institute of Management & Technology, the top MBA College in Delhi NCR, the academicians in their interactions with students and business executives emphasise on the assessment of opportunity costs of letting go the digitization paradigm. Most corporations have tremendous quantities of data locked in legacy IT systems and legacy applications. This untapped data that is unused and locked in hard copies of files, servers, personal computers and legacy software apps are goldmines of intrinsic knowledge that can be and should be used for faster, effective and most importantly efficient decision making. Yes, there is conclusive evidence on the fact that digitization in its very primitive incarnation of intrinsic knowledge management can pay rich dividends. A research publication from the business consulting giant McKinsey asserts that corporations drive up profitability and productivity by up to 6%.
An Agenda for the Top Management Team for a Digitization Drive
In our lecture sessions on strategic management we have done 5 case studies and supplemented that with an insight again published by McKinsey. Based on these cases studies and the research publication of McKinsey we have reaffirmed our faith in the following 6 point agenda for the top management team of corporations to follow:
· Transform people towards a gradual adoption of big data analytics
· Streamlining a data analytics strategy
· Assessing what to build, borrow, rent or purchase
· Securing big data analytics expertise
· Mobilizing resources
· Building frontline capabilities
Engineering a Big Data Analytics Plan for Beginners
Once the top management team of a corporation paves the way for the adoption of big data analytics it is crucial to have a strategic plan of action and identify the key requirements of the project while also defining the business goals that the project seeks to achieve and identifying metrics for the measurement of returns on investment across different stages of the IT project lifecycle. Putting in place an analytics plan for corporations that are about to start off the first time should ideally consist of three components: data, tools and analytics models.
The creation of a sustainable and accurate data repository calls for systems analysis and design to streamline the flow and funnelling of data through different layers and reengineering the data architecture. Data governance standards may be required to be put in place with implementation of margins of error in data creation, maintenance and collection. More over it is wise to distil transactions from analytical reports.
The second part of the plan is to identify the relevant analytics models and assess the merits of each of the analytics models before being out to use. An analytics modelling factory may play a crucial role here by diagnosing which data analytics model works best for optimization of data so that it may be scaled for different business functions and units across the company.
The third part of the plan consists of tools meant for integrating the complex outputs of data modelling into the business processes that are being carried out in a routine basis. Tools that offer an interface between the output generated by data analytics modelling and business practices of managers complete the link between data scientists and analysts on one hand and managers and employees on the other hand. Many companies fail to institutionalize the big data analytics revolution because they fail to integrate the last two steps.
Three Big Data Analytics ROI Patterns
There are three clear investment patterns that are likely to emerge from a big data analytics project that is undertaken. These three patterns are as follows.
· First, early bird catches the worm. There is crystal clear evidence that corporations that start early get the time cushion to learn by trial and error and thus adapt to the digitization strategy faster.
· Second, investment in IT and big data analytics expertise offers a better payoff than just investment in the technology. The coordination between IT skills and IT infrastructure creation is decisive in improving productivity and profitability.
· Third, investing in big data analytics talent at scale holds the key. Big data analytics professionals are the ultimate knowledge workers of the 21st century and are beyond an iota of doubt in short supply. It is critical for the HR department to build a large talent pool for driving the big data analytics project. There has to be an investment in hiring top IT talent at scale.