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.