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The Analytics Landscape:
Top 10 Trends
Niranjan Krishnan
Director & Head - Innovation Lab
Tiger Analytics
Contact: info@tigeranalytics.com
May 1, 2016
1. The 5 Forces
2. The Top 10 Trends
Agenda
Analytics Landscape:
The Five Forces
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
Top 10 Analytics Trends
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.
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.
3
Speech and Voice Analytics come
into their own
-- Following the footsteps of Text
Analytics to enable better business
decisions.
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.
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
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.
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.
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.
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.
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.
Contact:
Niranjan Krishnan
Director & Head – Innovation Lab
info@tigeranalytics.com
www.tigeranalytics.com
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

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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
  • 2. 1. The 5 Forces 2. The Top 10 Trends Agenda
  • 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.
  • 16. Contact: Niranjan Krishnan Director & Head – Innovation Lab info@tigeranalytics.com www.tigeranalytics.com
  • 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