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Real time for the bottom line webinar series
EPISODE I: How to stop wasting money on unactionable analytics
Perishable Insights – Stop Wasting Money
On Unactionable Insights
Mike Gualtieri, Principal Analyst
Twitter: @mgualtieri
© 2015 Forrester Research, Inc. Reproduction Prohibited 4
52%
53%
53%
54%
58%
64%
64%
65%
66%
73%
75%
0% 10% 20% 30% 40% 50% 60% 70% 80%
Better leverage big data and analytics in business decision-making
Create a comprehensive strategy for addressing digital technologies like mobile,social & smart products
Create a comprehensive digital marketing strategy
Better comply with regulations and requirements
Improve differentiation in the market
Increase influence and brand reach in the market
Address rising customer expectations
Improve our ability to innovate
Reduce costs
Improve our products /services
Improve the experience of our customers
Customer experience and product innovation are top priorities.
› Base: 3,005 global data and analytics decision-makers
› Source: Global Business Technographics Data And Analytics Online Survey, 2015
For you For all For segments For you
Demographic
Relationships
Hyper-Personal,
Real-Time
Relationships
Personal
RelationshipsMass
Relationships
Cu
sto
me
r E
xp
eri
en
ce
1800 1900 1950 2000 2015
• Learn individual customer
characteristics and
behaviors
• Detect customer needs and
desires in real-time
• Adapt applications to serve
an individual customer
Customer experiences must:
• Learn individual device and
systems of devices
characteristics and
behaviors
• Detect context in real-time
• Adapt applications to
improve the applications
IoT applications must:
Using your best estimate, what is the size of
all data stored within your company?
Source: Forrester Research, September 2015
Base: 100 US Managers and above currently using Hadoop for processing and analyzing data.
Enterprises have plenty of data from both internal and
external sources
10-49 Terabytes
5% 50-99 Terabytes
12%
100-500 Terabytes
54%
Greater than 500
Terabytes29%
Internal business
data49%
External source data
51%
What % of the data available is from internal business applications (ERP and business
applications) versus external sources (social, IoT)?
22© 2016 Forrester Research, Inc. Reproduction Prohibited
“As you look to improve your data processing and analytics capabilities, what aspect of
the implementation is most important to your business? Please select one.”
11%
11%
12%
16%
24%
25%
Quick turnaround on customer requests
More data availability
Expanded access to more business users (i.e., self-service)
Low cost
Advanced analytics capabilities (e.g. predictive. prescriptive,streaming)
Faster performance (time to value)
Faster time to value and advanced analytics
is most important to business
Base: 100 data science and data analytics leaders at enterprises within the US
Source: A commissioned study conducted by Forrester Consulting, April 2016
Real-time
insights
Operational
insights
Performance
insights
Strategic
insights
Insight: Shopping for
furniture
Action: Recommend
cleaning supplies
Insight: Profit lower than
goal
Action: Optimize price
Insight: Demand forecast
strong
Action: Increase inventory
Insight: Furniture demand
high
Action: Expand product line
Tim
e t
o A
ct
Perishability
Sub-second to
secondsSeconds to
hours
Days to
weeksWeeks to
years
Sub-second to
seconds
Seconds to
hours
Hours to
weeks
Weeks to
years
Enterprises must act on a range of perishable insights to get value from data and analytics
Batch analytics operations take too long
Bu
sin
ess
Valu
e
Time To Action
Data
originated
Analytics
performed
Insights
gleaned
Action
taken
Outdated
insights
Impotent or
harmful
actions
Po
sitiv
eN
eg
ative
Decision
madePoor
decision
Compress analytics operations to maximize the value of data
Bu
sin
ess
Valu
e
Time To Action
Po
sitiv
eN
eg
ative
Maximum
Business
Value
34© 2016 Forrester Research, Inc. Reproduction Prohibited
“What are the technological challenges impeding you from processing and analyzing data more
effectively? Select all that apply.”
6%
18%
18%
22%
27%
29%
35%
35%
37%
We have no technical challenges
Lack of analytical tools
Lack of data management tools
Difficulty in creating data models and/or preparing data for analytics
Too many data formats to integrate effectively
Data is difficult to access from multiple sources
Difficulty integrating data from multiple sources
Time it takes to assemble data for analysis
Data volume is too large
Top technological challenges
Base: 100 data science and data analytics leaders at enterprises within the US
Source: A commissioned study conducted by Forrester Consultin, April 2016
The data lake approach is insufficient because it takes too long
Customer
Reference
Data Lake
Operational
Transactional
Analytics tools Insights
Data
Scientists
Business
intelligence
DEFINITION
FORRESTERStreaming analytics filter, aggregate, enrich,
and analyze a high throughput of data from
disparate live data sources to identify patterns,
detect urgent situations, and automate
immediate actions in real-time.
© 2015 Forrester Research, Inc. Reproduction Prohibited 39
Source: Forrester Research
Streaming analytics adoption is rightly surging
“What is your firm's/business unit's current use of the following technologies?”
Source: Forrester's Global Business Technographics Data And Analytics Survey, 2015 and 2014
Base: 1805 (2015), 1063 (2014)
19%
19%
24%
31%
34%
22%
22%
35%
31%
43%
53%
54%
50%
50%
69%
39%
42%
42%
42%
42%
43%
43%
46%
48%
52%
54%
55%
56%
57%
69%
Non modeled data exploration and discovery
Search/interactive discovery
Streaming analytics
Metadata generated analytics
OLAP
Advanced visualization
Text analytics
Location analytics
Predictive analytics
Process analytics
Embedded analytics
Web analytics
Dashboards
Performance analytics
Reporting
2015
2014
Modern applications infuse analytics to respond in real-time and become smarter
Streaming data
Application
interface
App Logic
Applications
Context
Actions
Real-time
Context
Programmed
Logic
Learned
LogicMachine
learning Learning
External
Actions
External
Context
From other data
sources of
applications
To other data
sources or
applications
© 2015 Forrester Research, Inc. Reproduction Prohibited 46
Thinking in streams is different…
› Ingest
› Filter
› Transform
› Normalize
› Link
› Enrich
› Correlate
› Location/motion (geofencing)
› Time windows
› Temporal pattern detection (CEP)
› Business logic/rules execution
› Action interfaces
Continuous Ingestion Continuous Analytics
How can an online retailer
sell more motorcycle
helmets and optimize
profits?
› Temporal pattern detection
› Time windows
› Business logic/rules
execution
› Action interfaces
© 2015 Forrester Research, Inc. Reproduction Prohibited 48
Streaming analytics enables an entirely new real-time selling model
› Analytic: When has this user
viewed at least three
motorcycle safety products
including at least one helmet?
› Action: Display most profitable
motorcycle helmets.
› Analytic: What is the real-time
daily total sales of motorcycle
helmets?
› Action: If sales trending lower
than usual, then dynamically
lower price.
Temporal Pattern Detection Time Window
Architecture
• Workload scalability
• Ingestion throughput
• Analytical throughput
• Analytical latency
• Fault tolerance
• Operational management
• Deployment options (cloud)
Stream/event handling
• Event sequencing
• Enrichment
• Business logic
Analytical operators
• Transformation
• Aggregation
• Correlation
• Time windows
• Pattern matching
Applications dev.
• Development tools
• Data connectors
• Extensibility
• Dynamic deployment
Evaluate streaming analytics technology based on these criteria
110010011
01100
1
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11011
00
1
01001100
11011
0
1
0100100
11011
00
1
His
torica
l
Tra
nsactions
Custo
mer
data
Security
Ability to ingest structured and unstructured
from multiple sources in real-time.
The Forrester Wave™: Big Streaming Analytics Platforms, Q1 2016
Source: Forrester Research
15 vendor solutions for fast data ingestion, analysis,
and action.Mng: Let me know if
you want this slide in
here.
Enterprises must act on a range of perishable insights to get value from big data
Real-time
Insights
Strategic
Insights
Operational
Insights
Performance
Insights
Tim
e t
o A
ct
Perishability
Sub-second to
secondsSeconds to
hours
Days to
weeksWeeks to
years
Sub-second to
seconds
Seconds to
hours
Hours to
weeks
Weeks to
years
“An investment in
real-time knowledge
always pays the best
interest.”
- Benjamin Franklin
United States founding father,
inventor, and timeless thought
leader.
SQLstream: leading streaming analytics platform -empowering people, services, and machines to take the next right action, continuously and in real time
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StreamLab: development environment--from raw data to streaming apps in minutes
User selected
suggestion to execute
Immediately see the
live data & results
Build dashboards with
queries running
Auto generates useful
analytic suggestions
REAL-TIME FOR THE BOTTOM LINE WEBINAR SERIES
COMING UP | EPISODE 2: Streaming ingest
October 2016