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Top 10 Analytics Trends 2016
1. The Analytics Landscape:
Top 10 Trends
Niranjan Krishnan
Director & Head - Innovation Lab
Tiger Analytics
Contact: info@tigeranalytics.com
May 1, 2016
4. Five Forces shaping the Analytics Landscape
Analytics
Data
Storage
Capacity
Computing
Power
New
Methods
& Tools
Business
Usage
Data
Growth
▪ 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
data increasing
▪ Cost of data creation falling
6. Data Enrichment ushers in better
forecasts and sharper insights
1
-- Forecasting Analytics is (still) the
critical path to planning and operational
excellence.
-- 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
robust predictions.
7. 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
business decisions.
8. 3
Speech and Voice Analytics come
into their own
-- Following the footsteps of Text
Analytics to enable better business
decisions.
9. Data Lakes begin to fill up and yield
prize catches
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.
10. Big Data and Internet-Of-Things
(IOT) Analytics make great strides
5
-- Big Data is not a passsing fad - it is here
to stay.
-- Enormous amount of data generation is
happening without human involvement or
touchpoints.
-- Sensor data, machine-to-machine data
and network data are emerging as data
goldmines.
-- Analytics-of-Things taking of as a field
11. Analytic tools proliferate at a rapid
pace
6 -- Expanding data storage options e.g.
CRM, EDW, Cloud, calls for a wider set of
tools.
-- New tools offer advantages of speed,
scale and flexibility.
12. Open Source Tools steal a march over paid
licences
7
-- Cost is not the only advantage of Open Source.
-- Open Source tools are often more modular and
scalable.
-- Provide cutting-edge data processing, analytic and
visualization techniques.
-- 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
capabilities.
13. New visualization tools carve out
their own niches in the market
. . . even as MS Excel rules Visual
Analytics
8 -- Interactive visualizations are
replacing static reporting.
-- Unifocal insights are giving way to
What-If Analysis and Multi-criteria
Decision Making.
14. 9
Full-pipeline Analytics
Automation gets more
popular
-- Data Preparation i.e. pulling, merging,
cleansing and aggregating, remains the greatest
bottleneck to analytics.
-- Prompts a move away from adhoc-ism and
towards automation.
-- 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
work.
15. 10
New Age Decision Systems
gain clout
-- 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
deployment.
-- 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
deployment.
17. Tiger Analytics Overview
Global
Delivery 50+ 120+ 150+
HQ : Santa Clara, CA
Delivery: Chennai, India
Additional presence in:
Chicago, Portland,
Minneapolis
Clients across
Industries
Data Scientists &
Data Engineers
Projects