Smart Data
1
Somewhat or More Familiar 59% Extremely Somewhat Not Very 8% Very 16% 35% 41% WHAT’S TO GAIN? DESCRIPTIVE WHAT HAPPENED? Get in touch with reality, a single source of truth. Largely Reporting PREDICTIVE WHAT WILL HAPPEN? Understand the most likely future scenario, and it’s business implications PRESCRIPTIVE WHAT SHOULD WE DO ABOUT IT? Collaborate for maximum business value, informed by advanced analytics External Supply chain optimization isn’t linear, but the 1st & 2nd generation of planning solutions are built that way Concurrent optimization in combination with cognitive learning delivers greater insights and flexibility, but no proven ROI VOLUME+VELOCITY+VERACITY+VALUE Because of DATA Managers can now measure, and learn more about their business, in order to translate the new known knowledge into better and improved decision making More data at a faster rate than ever before are being emitted through forms of: messages, updates, images, social networking, readings from sensors, GPS signals, cell phone usage, and more. SMART DATA EQUALS MORE REVENUE Top ranking companies in their industry using data-driven decision making are on average, 5% more productive and 6% more profitable THAN their competitors OPTIMIZATION LEARNING REPORTING YIELDS BETTER... Disparate systems make getting to data difficult – 5-7 ERPs solutions and 2-3 instances IT budgets – tighter than ever Struggling with massive data complexity Internal Product traceability data 61% 38% 55% 36% 45% 27% 42% 26% 36% 28% 33% 20% 30% 23% 21% 24% 39% 33% 36% 20% 36% 21% Supply chain visibility Geo-location and mapping data Mobile applications RFID transmission Unstructured data in warranty Temperature and product streaming Voice and video data Internet of things (e.g. sensory devices Sentiment data from user-generated Sentiment data from socialmedia on equipment, vending machines. etc. and quality logs comments on ratings, reviews and blogs (e.g. Facebook, Twitter, etc.) DATA MOVING FORWARD ONLY 20% of companies feel their supply chains are working well in the Supply Chain SMART Initiative in the Supply Chain Data Rules Engines Deployment Process Flows Database Structures Structured Simple rules: single “ifs” to single “thens” Optimization based on a “known” function or mathematical outcome Within the organization Inside-out with a focus on the efficient response Relational Structured and unstructured Adaptive rules: multiple “ifs” to multiple “thens” through cognitive learning Concurrent optimization, cognitive learning and combinational math to drive discovery and new insights Cloud-based to synchronize inter-and-intra- enterprise flows Outside-in with a focus on sensing and adaptation Relational and non-relational to mine data in lakes, streams and clouds Current State Evolving Techniques COGNITIVE WHERE IS THE NEXT STEP-CHANGE IN DRIVING AN INTELLIGENT RESPONSE? Highly automated optimization solutions that get smarter over time CURRENT VS. EVOLVING WHERE ARE THE IMPROVEMENT OPPORTUNITIES SOURCE: http://www.information-age.com/technology/information-management/123458486/big-data-dead-rise-smart-data http://supplychaininsights.com/big-data-and-analytics-the-new-underpinning-for-supply-chain-success/ https://hbr.org/2012/10/big-data-the-management-revolution/ar HOW TO OBTAIN SMART DATA Data-driven decisions are better decisions PERFORMANCE USING TYPES OF DATA =BIG DATA DATA FAMILIARITY WITH DATA WHO SEES IT AS AN OPPORTUNITY? >$500M revenue 79% SMART DATA The purpose of smart data (veracity & value) is to filter out the noise and hold the valuable data, and when done effectively can be used by the enterprise to solve business problems. <$500M revenue 40% are manufacturers 58% are retailers 42% DATA OUTLOOK Data Initiative No Initiative If a businesss simply uses volume & velocity, that qualifies as a BIG DATA problem. However, a lot of this data comprises noise.
Transcript of Smart Data
Somewhat or More Famil iar
59%
Extremely
Somewhat
Not Ver y
8%
Ver y16%
35%
41%
WHAT’S TO GAIN?
DESCRIPTIVEWHAT HAPPENED?
Get in touch with reality, a single source of truth.
Largely Reporting
PREDICTIVEWHAT WILL HAPPEN?
Understand the most likely future scenario, and it’s
business implications
PRESCRIPTIVEWHAT SHOULD WE DO ABOUT IT?
Collaborate for maximum business value, informed
by advanced analytics
ExternalSupply chain optimization
isn’t l inear, but the 1st & 2nd
generation of planning
solutions are built that way
Concurrent optimization in
combination with cognit ive
learning delivers greater
insights and f lexibi l i ty, but no
proven ROI
VOLUME+VELOCIT Y+VERACIT Y+VALUE
Because of DATA
Managers can now measure, and learn more about their business, in order to translate the new known knowledge into better and improved decision making
More data at a faster rate than ever before are being emitted through forms of: messages, updates, images, social networking, readings from sensors, GPS signals, cell
phone usage, and more.
SMART DATA EQUALS MORE REVENUE
Top ranking companies in their industr y using data-driven decision making
are on average, 5% more productive and 6% more prof itable
THAN their competitors
OPTIMIZATION
LEARNING
REPORTING
YIELDS BETTER...
Disparate systems make
gett ing to data dif f icult – 5-7
ERPs solutions and 2-3
instances
IT budgets – t ighter than ever
Struggling with massive data
complexity
Internal
Product traceabil i ty data 61%38%
55%36%
45%27%
42%26%
36%28%
33%20%
30%23%
21%24%
39%33%
36%20%
36%21%
Supply chain visibi l i ty
Geo-location and mapping data
Mobile applications
RFID transmission
Unstructured data in warranty
Temperature and product s treaming
Voice and video data
Internet of things (e.g. sensor y devices
Sentiment data from user -generated
Sentiment data from socialmedia
on equipment, vending machines. etc.
and quality logs
comments on ratings, reviews and blogs
(e.g. Facebook , Twitter, etc.)
DATA MOVING
FORWARD
ONLY 20% of companies feel their supply chains are working well
in the Supply Chain
SMART
Initiative in the Supply Chain
Data
Rules
Engines
Deployment
Process Flows
Database Structures
Structured
Simple rules: single “ifs” to single “thens”
Optimization based on a “known” function or mathematical outcome
Within the organization
Inside-out with a focus on the efficient response
Relational
Structured and unstructured
Adaptive rules: multiple “ifs” to multiple “thens”through cognitive learning
Concurrent optimization, cognitive learning and combinational math to drive discovery and new insights
Cloud-based to synchronize inter-and-intra- enterprise flows
Outside-in with a focus on sensing and adaptation
Relational and non-relational to mine data in lakes, streams and clouds
Current State Evolving Techniques
COGNITIVEWHERE IS THE NEXT STEP-CHANGE IN DRIVING AN INTELLIGENT RESPONSE?
Highly automated optimization solutions that get smarter over
time
CURRENT VS. EVOLVINGWHERE ARE THE IMPROVEMENT OPPORTUNITIES
SOURCE: http://www.information-age.com/technology/information-management/123458486/big-data-dead-rise-smart-datahttp://supplychaininsights.com/big-data-and-analytics-the-new-underpinning-for-supply-chain-success/https://hbr.org/2012/10/big-data-the-management-revolution/ar
HOW TO OBTAIN SMART DATA
Data-driven decisions are better decisions
PERFORMANCE USING T YPES OF DATA
=BIG DATA
DATA
FAMILIARIT Y WITH DATA
WHO SEES IT AS AN OPPORTUNIT Y?
>$500M revenue79%
SMART DATA
The purpose of smart data (veracity & value) is to filter out the noise and hold the valuable data, and when done effectively can be used
by the enterprise to solve business problems.
<$500M revenue40%
are manufacturers58% are retai lers
42%
DATA OUTLOOK
Data Init iat iveNo Init iative
If a businesss simply uses volume & velocity, that qualifies as a BIG DATA problem. However, a lot of this data comprises noise.
