SlideShare ist ein Scribd-Unternehmen logo
1 von 13
Leveraging Data to Form  Closed-Loop Decision Processes Mayank Bawa, CEO Defrag November 4 th , 2008
The Social Web is Accelerating Data Volumes   Confidential and proprietary. Copyright © 2008 Aster Data Systems
Traditional Data Flows Lead to Slower Learning … Frontline Applications Frontline Applications Frontline Applications Confidential and proprietary. Copyright © 2008 Aster Data Systems  Move &  Batch  Load Analytic Data  Mart Sample Extract Enterprise Data  Warehouse Operational Data Store Record Transform & Cleanse Source OLTP  Database
Goal: Ability to Learn and Adapt Faster Applications BIG DATA Rapid Data Analysis Confidential and proprietary. Copyright © 2008 Aster Data Systems
Example 1: Recommendations at Retailers Confidential and proprietary. Copyright © 2008 Aster Data Systems
Example 2: Targeting in Advertising Networks Confidential and proprietary. Copyright © 2008 Aster Data Systems
Example 3: Increase Engagement at Publishers Confidential and proprietary. Copyright © 2008 Aster Data Systems
Candidate Architecture for Leveraging Data Users Applications Fresh Hourly Load Frontline Data Warehouse Reports and Analytics Rules Engine Updated Hourly Workload Management Confidential and proprietary. Copyright © 2008 Aster Data Systems
Paradigm: Frontline Data Analytics Front-office data analytics to power  revenue growth  or  decrease risk  … …  because to do nothing is much more  expensive Confidential and proprietary. Copyright © 2008 Aster Data Systems
Who is Leveraging Frontline Data Analytics? Confidential and proprietary. Copyright © 2008 Aster Data Systems
Examples of Frontline Data Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Confidential and proprietary. Copyright © 2008 Aster Data Systems
The Subtle Shift: Applications are using Data Frontline Data Warehouse … Frontline Applications Frontline Applications Frontline Applications Confidential and proprietary. Copyright © 2008 Aster Data Systems  Move &  Batch  Load Analytic Data  Mart Sample Extract Enterprise Data  Warehouse Operational Data Store Record Transform & Cleanse Source OLTP  Database Feedback
Our “Aha!” Contribution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Confidential and proprietary. Copyright © 2008 Aster Data Systems

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Building Resilient and Scalable Data Pipelines by Decoupling Compute and Storage
Building Resilient and Scalable Data Pipelines by Decoupling Compute and StorageBuilding Resilient and Scalable Data Pipelines by Decoupling Compute and Storage
Building Resilient and Scalable Data Pipelines by Decoupling Compute and Storage
 
Using a Semantic and Graph-based Data Catalog in a Modern Data Fabric
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricUsing a Semantic and Graph-based Data Catalog in a Modern Data Fabric
Using a Semantic and Graph-based Data Catalog in a Modern Data Fabric
 
Data in Motion vs Data at Rest
Data in Motion vs Data at RestData in Motion vs Data at Rest
Data in Motion vs Data at Rest
 
Big Data Fabric 2.0 Drives Data Democratization
Big Data Fabric 2.0 Drives Data DemocratizationBig Data Fabric 2.0 Drives Data Democratization
Big Data Fabric 2.0 Drives Data Democratization
 
Fighting Financial Crime with Artificial Intelligence
Fighting Financial Crime with Artificial IntelligenceFighting Financial Crime with Artificial Intelligence
Fighting Financial Crime with Artificial Intelligence
 
Pick a Winner: How to Choose a Data Warehouse
Pick a Winner: How to Choose a Data WarehousePick a Winner: How to Choose a Data Warehouse
Pick a Winner: How to Choose a Data Warehouse
 
Principles of System Observability
Principles of System Observability Principles of System Observability
Principles of System Observability
 
How to Choose a Data Warehouse
How to Choose a Data WarehouseHow to Choose a Data Warehouse
How to Choose a Data Warehouse
 
Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...
Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...
Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...
 
Healthbus deck
Healthbus deckHealthbus deck
Healthbus deck
 
Tropos - Data as a Service - Business analytics insight
Tropos - Data as a Service - Business analytics insightTropos - Data as a Service - Business analytics insight
Tropos - Data as a Service - Business analytics insight
 
Big Data Landscape 2016
Big Data Landscape 2016 Big Data Landscape 2016
Big Data Landscape 2016
 
Keynote: The Journey to Pervasive Analytics
Keynote: The Journey to Pervasive AnalyticsKeynote: The Journey to Pervasive Analytics
Keynote: The Journey to Pervasive Analytics
 
Delivering Self-Service Analytics using Big Data and Data Virtualization on t...
Delivering Self-Service Analytics using Big Data and Data Virtualization on t...Delivering Self-Service Analytics using Big Data and Data Virtualization on t...
Delivering Self-Service Analytics using Big Data and Data Virtualization on t...
 
Study: #Big Data in #Austria
Study: #Big Data in #AustriaStudy: #Big Data in #Austria
Study: #Big Data in #Austria
 
From Batch to Real Time: Overstock’s Journey Towards Unifying Analytics Acros...
From Batch to Real Time: Overstock’s Journey Towards Unifying Analytics Acros...From Batch to Real Time: Overstock’s Journey Towards Unifying Analytics Acros...
From Batch to Real Time: Overstock’s Journey Towards Unifying Analytics Acros...
 
Transforming Data Management and Time to Insight with Anzo Smart Data Lake®
Transforming Data Management and Time to Insight with Anzo Smart Data Lake®Transforming Data Management and Time to Insight with Anzo Smart Data Lake®
Transforming Data Management and Time to Insight with Anzo Smart Data Lake®
 
ING's Customer-Centric Data Journey from Community Idea to Private Cloud Depl...
ING's Customer-Centric Data Journey from Community Idea to Private Cloud Depl...ING's Customer-Centric Data Journey from Community Idea to Private Cloud Depl...
ING's Customer-Centric Data Journey from Community Idea to Private Cloud Depl...
 
From Data Lakes to the Data Fabric: Our Vision for Digital Strategy
From Data Lakes to the Data Fabric: Our Vision for Digital StrategyFrom Data Lakes to the Data Fabric: Our Vision for Digital Strategy
From Data Lakes to the Data Fabric: Our Vision for Digital Strategy
 
Strata+hadoop data kitchen-seven-steps-to-high-velocity-data-analytics-with d...
Strata+hadoop data kitchen-seven-steps-to-high-velocity-data-analytics-with d...Strata+hadoop data kitchen-seven-steps-to-high-velocity-data-analytics-with d...
Strata+hadoop data kitchen-seven-steps-to-high-velocity-data-analytics-with d...
 

Ähnlich wie Aster Defrag 2008 97

Data Analytics를 통한 비지니스 혁신::Craig Stries::AWS Summit Seoul 2018
Data Analytics를 통한 비지니스 혁신::Craig Stries::AWS Summit Seoul 2018Data Analytics를 통한 비지니스 혁신::Craig Stries::AWS Summit Seoul 2018
Data Analytics를 통한 비지니스 혁신::Craig Stries::AWS Summit Seoul 2018
Amazon Web Services Korea
 

Ähnlich wie Aster Defrag 2008 97 (20)

雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)
雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)
雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)
 
Better Business From Exploring Ideas - AWS Summit Sydney 2018
Better Business From Exploring Ideas - AWS Summit Sydney 2018Better Business From Exploring Ideas - AWS Summit Sydney 2018
Better Business From Exploring Ideas - AWS Summit Sydney 2018
 
Better Business from Exploring Ideas - Modern Data Architectures on AWS
Better Business from Exploring Ideas - Modern Data Architectures on AWSBetter Business from Exploring Ideas - Modern Data Architectures on AWS
Better Business from Exploring Ideas - Modern Data Architectures on AWS
 
Big Data for Product Managers
Big Data for Product ManagersBig Data for Product Managers
Big Data for Product Managers
 
Pemahaman Pelanggan & Machine Learning (Level 200 – 300) | Kenali Pelanggan A...
Pemahaman Pelanggan & Machine Learning (Level 200 – 300) | Kenali Pelanggan A...Pemahaman Pelanggan & Machine Learning (Level 200 – 300) | Kenali Pelanggan A...
Pemahaman Pelanggan & Machine Learning (Level 200 – 300) | Kenali Pelanggan A...
 
Data Analytics를 통한 비지니스 혁신::Craig Stries::AWS Summit Seoul 2018
Data Analytics를 통한 비지니스 혁신::Craig Stries::AWS Summit Seoul 2018Data Analytics를 통한 비지니스 혁신::Craig Stries::AWS Summit Seoul 2018
Data Analytics를 통한 비지니스 혁신::Craig Stries::AWS Summit Seoul 2018
 
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...
 
When SAP alone is not enough
When SAP alone is not enoughWhen SAP alone is not enough
When SAP alone is not enough
 
Data Privacy & Governance in the Age of Big Data: Deploy a De-Identified Data...
Data Privacy & Governance in the Age of Big Data: Deploy a De-Identified Data...Data Privacy & Governance in the Age of Big Data: Deploy a De-Identified Data...
Data Privacy & Governance in the Age of Big Data: Deploy a De-Identified Data...
 
Better Business from Exploring Ideas - Modern Data Architectures on AWS
Better Business from Exploring Ideas - Modern Data Architectures on AWSBetter Business from Exploring Ideas - Modern Data Architectures on AWS
Better Business from Exploring Ideas - Modern Data Architectures on AWS
 
What's New with Amazon Redshift ft. McDonald's (ANT350-R1) - AWS re:Invent 2018
What's New with Amazon Redshift ft. McDonald's (ANT350-R1) - AWS re:Invent 2018What's New with Amazon Redshift ft. McDonald's (ANT350-R1) - AWS re:Invent 2018
What's New with Amazon Redshift ft. McDonald's (ANT350-R1) - AWS re:Invent 2018
 
CS-Op Analytics
CS-Op AnalyticsCS-Op Analytics
CS-Op Analytics
 
Four Key Considerations for your Big Data Analytics Strategy
Four Key Considerations for your Big Data Analytics StrategyFour Key Considerations for your Big Data Analytics Strategy
Four Key Considerations for your Big Data Analytics Strategy
 
Driving Machine Learning and Analytics Use Cases with AWS Storage (STG302) - ...
Driving Machine Learning and Analytics Use Cases with AWS Storage (STG302) - ...Driving Machine Learning and Analytics Use Cases with AWS Storage (STG302) - ...
Driving Machine Learning and Analytics Use Cases with AWS Storage (STG302) - ...
 
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...
 
Modern data integration expert sessions
Modern data integration expert sessionsModern data integration expert sessions
Modern data integration expert sessions
 
Modern Data Integration Expert Session Webinar
Modern Data Integration Expert Session Webinar Modern Data Integration Expert Session Webinar
Modern Data Integration Expert Session Webinar
 
Delivering Mission Assurance in the Federal Government through Cloud Technology
Delivering Mission Assurance in the Federal Government through Cloud TechnologyDelivering Mission Assurance in the Federal Government through Cloud Technology
Delivering Mission Assurance in the Federal Government through Cloud Technology
 
BI & Analytics
BI & AnalyticsBI & Analytics
BI & Analytics
 
Limitless Data, Rapid Discovery, Powerful Insight: How to Connect Cloudera to...
Limitless Data, Rapid Discovery, Powerful Insight: How to Connect Cloudera to...Limitless Data, Rapid Discovery, Powerful Insight: How to Connect Cloudera to...
Limitless Data, Rapid Discovery, Powerful Insight: How to Connect Cloudera to...
 

Kürzlich hochgeladen

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 

Kürzlich hochgeladen (20)

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot ModelMcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Mcleodganj Call Girls 🥰 8617370543 Service Offer VIP Hot Model
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 

Aster Defrag 2008 97

  • 1. Leveraging Data to Form Closed-Loop Decision Processes Mayank Bawa, CEO Defrag November 4 th , 2008
  • 2. The Social Web is Accelerating Data Volumes Confidential and proprietary. Copyright © 2008 Aster Data Systems
  • 3. Traditional Data Flows Lead to Slower Learning … Frontline Applications Frontline Applications Frontline Applications Confidential and proprietary. Copyright © 2008 Aster Data Systems Move & Batch Load Analytic Data Mart Sample Extract Enterprise Data Warehouse Operational Data Store Record Transform & Cleanse Source OLTP Database
  • 4. Goal: Ability to Learn and Adapt Faster Applications BIG DATA Rapid Data Analysis Confidential and proprietary. Copyright © 2008 Aster Data Systems
  • 5. Example 1: Recommendations at Retailers Confidential and proprietary. Copyright © 2008 Aster Data Systems
  • 6. Example 2: Targeting in Advertising Networks Confidential and proprietary. Copyright © 2008 Aster Data Systems
  • 7. Example 3: Increase Engagement at Publishers Confidential and proprietary. Copyright © 2008 Aster Data Systems
  • 8. Candidate Architecture for Leveraging Data Users Applications Fresh Hourly Load Frontline Data Warehouse Reports and Analytics Rules Engine Updated Hourly Workload Management Confidential and proprietary. Copyright © 2008 Aster Data Systems
  • 9. Paradigm: Frontline Data Analytics Front-office data analytics to power revenue growth or decrease risk … … because to do nothing is much more expensive Confidential and proprietary. Copyright © 2008 Aster Data Systems
  • 10. Who is Leveraging Frontline Data Analytics? Confidential and proprietary. Copyright © 2008 Aster Data Systems
  • 11.
  • 12. The Subtle Shift: Applications are using Data Frontline Data Warehouse … Frontline Applications Frontline Applications Frontline Applications Confidential and proprietary. Copyright © 2008 Aster Data Systems Move & Batch Load Analytic Data Mart Sample Extract Enterprise Data Warehouse Operational Data Store Record Transform & Cleanse Source OLTP Database Feedback
  • 13.

Hinweis der Redaktion

  1. Most data generated on the Internet gets thrown away because it has traditionally been viewed as too expensive or low value to store and analyze. While the data businesses capture is now “growing out,” the smartest companies also recognize that the use of data is “growing up.” These companies are the ones who monetize the insights they gain by taming and analyzing those data sets, and feeding the insights back into operational purposes.