Business success relies heavily on taking the right action, at the right time, all the time. And actions are dictated by data. But the batch-oriented, collect-store-contemplate model employed by Big Data Analytics technologies is incomplete because it does not make use of live data in real time. Without live, real-time data insights gathered are not up-to-date, and cannot accurately inform applications and services that would benefit from continuous, real-time context for time-sensitive decisions.
To thrive, businesses need to be able to use both live and historical data in their applications and services, continuously, concurrently, and correctly and the only technology currently capable of handling it is streaming analytics. Streaming analytics computes data right now, when it can be analyzed and put to good use to make applications of all kinds contextual and smarter.
This webinar held in collaboration with Forrester, Inc., showcased how streaming analytics applications can be built in minutes, to:
- Aggregate, enrich, and analyze a high throughput of data from multiple, disparate live data sources and in any format to identify patterns, detect opportunities, automate actions, and dynamically adapt
- Easily ingest streaming data from multiple disparate sources to multiple sources, within and between cloud and on-premises environments
- Analyze and act on data as it arrives, without needing to store, eliminating unnecessary security risks and storage costs
- Enable real-time analytics with existing business intelligence and data assets.
Streaming analytics webinar | 9.13.16 | Guest: Mike Gualtieri from Forrester
1. TWEET: during and after the webinar, please use #StreamingAnalytics for live discussions
DIRECT QUESTIONS: please use the box to the right of your screen
RECORDINGS: an edited version of the webinar recording will be emailed after the event
Real time for the bottom line webinar series
EPISODE I: How to stop wasting money on unactionable analytics
2. Perishable Insights – Stop Wasting Money
On Unactionable Insights
Mike Gualtieri, Principal Analyst
Twitter: @mgualtieri
5. For you For all For segments For you
Demographic
Relationships
Hyper-Personal,
Real-Time
Relationships
Personal
Relationships
Mass
Relationships
CustomerExperience
1800 1900 1950 2000 2015
10. • Learn individual device and
systems of devices
characteristics and
behaviors
• Detect context in real-time
• Adapt applications to
improve the applications
IoT applications must:
14. 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
Terabytes
29%
Internal
business
data
49%
External
source data
51%
What % of the data available is from internal
business applications (ERP and business
applications) versus external sources
(social, IoT)?
25. 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
TimetoAct
Perishability
Sub-second to
seconds
Seconds to
hours
Days to
weeks
Weeks 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
26. Batch analytics operations take too long
BusinessValue
Time To Action
Data
originated
Analytics
performed
Insights
gleaned
Action
taken
Outdated
insights
Impotent or
harmful
actions
PositiveNegative
Decision
made
Poor
decision
27. Compress analytics operations to maximize
the value of data
BusinessValue
Time To Action
PositiveNegative
Maximum
Business
Value
35. The data lake approach is insufficient
because it takes too long
Customer
Reference
Data Lake
Operational
Transactional
Analytics tools Insights
Data
Scientists
Business
intelligence
38. 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.
41. 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
Logic
Machine
learning
Learning
External
Actions
External
Context
From other data
sources of
applications
To other data
sources or
applications
42. How can you prevent this dude from fleecing
you right now?
43. What are movers and shakers saying about
equities that we cover right now?
44. How can you warn other drivers that the
road is slippery to avoid a crash right now?
45. How can you show an ad that this household
will find relevant right now?
47. How can an online retailer
sell more motorcycle
helmets and optimize
profits?
› Temporal pattern detection
› Time windows
› Business logic/rules
execution
› Action interfaces
58. 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.
60. Enterprises must act on a range of perishable
insights to get value from big data
Real-time
Insights
Strategic
Insights
Operational
Insights
Performance
Insights
TimetoAct
Perishability
Sub-second to
seconds
Seconds to
hours
Days to
weeks
Weeks to
years
Sub-second to
seconds
Seconds to
hours
Hours to
weeks
Weeks to
years
62. “An investment in
real-time knowledge
always pays the best
interest.”
- Benjamin Franklin
United States founding father,
inventor, and timeless thought
leader.
64. SQLstream: leading streaming analytics platform
-empowering people, services, and machines to take the next right action, continuously and in real time
ANALYZE
ACQUIRE
ACT
66. 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
67. REAL-TIME FOR THE BOTTOM LINE WEBINAR SERIES
COMING UP | EPISODE 2: Streaming ingest
October 2016
inquiries@sqlstream.com