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Five Forces shaping the Analytics Landscape
▪ Storage capacity expanding
▪ Cost falling
▪ Processing power increasing
▪ Cost falling
▪ High-impact innovations
▪ Open Source collaboration
▪ Shorter time-to-market
▪ Increasing adoption
▪ Growing needs
▪ Expanding possibilities
▪ Volume, variety and velocity of
▪ Cost of data creation falling
Data Enrichment ushers in better
forecasts and sharper insights
-- Forecasting Analytics is (still) the
critical path to planning and operational
-- Internal systems (e.g. CRM, EDW) do
not contain all the pieces needed to form a
full picture of business scenario.
-- External data from public sources
enriches internal data and enables more
Unstructured data opens new
pathways to business value creation
2 -- Data no longer needs to be
structured, formatted and linked to
be useful. Intelligence can be gleaned
from virtually any type of data e.g.
text, image, audio, video.
-- Text Analytics powers several
Speech and Voice Analytics come
into their own
-- Following the footsteps of Text
Analytics to enable better business
Data Lakes begin to fill up and yield
4 -- Hyper-massive datasets and the
powerful business intelligence they
provide are steering corporations
towards Data Lakes in the Cloud.
-- Cloud offers a compelling option to
both store and process massive
amounts of data efficiently.
Big Data and Internet-Of-Things
(IOT) Analytics make great strides
-- Big Data is not a passsing fad - it is here
-- Enormous amount of data generation is
happening without human involvement or
-- Sensor data, machine-to-machine data
and network data are emerging as data
-- Analytics-of-Things taking of as a field
Analytic tools proliferate at a rapid
6 -- Expanding data storage options e.g.
CRM, EDW, Cloud, calls for a wider set of
-- New tools offer advantages of speed,
scale and flexibility.
Open Source Tools steal a march over paid
-- Cost is not the only advantage of Open Source.
-- Open Source tools are often more modular and
-- Provide cutting-edge data processing, analytic and
-- BUT . . . documentation is scant and there is no
support other than online communities.
Selection of right analytic tools is a difficult
decision for companies
-- Problems of plenty calling for good alignment of
business needs, system constraints and tool
New visualization tools carve out
their own niches in the market
. . . even as MS Excel rules Visual
8 -- Interactive visualizations are
replacing static reporting.
-- Unifocal insights are giving way to
What-If Analysis and Multi-criteria
Automation gets more
-- Data Preparation i.e. pulling, merging,
cleansing and aggregating, remains the greatest
bottleneck to analytics.
-- Prompts a move away from adhoc-ism and
-- End-to-end automation of data extraction,
preparation, analytics and reporting boosts
productivity of analytics teams.
-- Enables users to spend less time in getting
analytic insights and more in putting them to
New Age Decision Systems
-- Early Warning Systems help businesses
anticipate and adapt to change.
-- Real time, self-learning systems powered by
artificial intelligence have low latency feedback,
automated recalibration and large-scale
-- Test and Learn systems enable controlled
experiments in the market. New ideas are tried
out on a limited basis and their impact is
measured and confirmed before full-scale
Director & Head – Innovation Lab
Tiger Analytics Overview
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