SlideShare a Scribd company logo
1 of 4
Case Study - FastStats Marketing Database Solution delivered by In Two Weeks! The Problem mypetstop had three identical Access databases in three locations generated by their Pet Admin software. Each database had the same customer ids in common and no easy way of creating reports or analysis for individual locations or across all three. The analysis was needed to derive the Marketing Strategy with their newly-appointed agency Gratterpalm and to drive the business forward. There was also some duplication of records identified on the system.
Case Study - FastStats Marketing Database Solution delivered by In Two Weeks! The Approach Gratterpalm brought in specialists Database Marketing Ltd to design and build a new marketing database solution that would merge all three databases together, create a unique range of customer ids, clean up any duplicates and deliver back to mypetstop a solution that the marketing department would have on their desktop.  Non-technical users would be able to create their own counts, selections, reports and analysis.
Case Study - FastStats Marketing Database Solution delivered by In Two Weeks! The Solution Database Marketing Ltd first created a Sql Server database to manipulate the data to create a unique range of customer ids for each location, delete duplicate customer records but maintain all transactions for the deleted customer ids thus creating a Single Customer View.  All three cleaned databases were merged into one with flags created to enable analysis overall or by location. This was all done on systems at DBM with the clean database loaded back to mypetstop. No internal IT resource was needed at mypetstop other than to setup an automated ftp process to upload the Access databases to a secure ftp site and then to download the final processed database. FastStats was chosen as the Marketing Database software and a FastStats database designed and built. The solution included weekly updates. The FastStats software was installed and configured at mypetstop Head Office in Leeds and 4 staff members trained in the use of FastStats.  The whole system, including Sql programming, FastStats design & build, software installation and configuration, testing and training was completed within two weeks!
Case Study - FastStats Marketing Database Solution delivered by In Two Weeks! James Kundi - mypetstop “ I've been using Faststats for a month now and I'm very impressed with its user-friendly interfaces, broad range of reports and interactive graphical views.  Through interrogating our previously dormant database on FastStats, we quickly and efficiently gained valuable knowledge on our customer demographics, spending habits, average length of stay and most popular service. This information will help us to deliver a more effective, targeted marketing communication campaign in 2010.“

More Related Content

What's hot

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 FabricCambridge Semantics
 
Strayer cis 515 week 10 technical paper database administrator for department...
Strayer cis 515 week 10 technical paper database administrator for department...Strayer cis 515 week 10 technical paper database administrator for department...
Strayer cis 515 week 10 technical paper database administrator for department...shyaminfo40
 
Big Data - The 5 Vs Everyone Must Know
Big Data - The 5 Vs Everyone Must KnowBig Data - The 5 Vs Everyone Must Know
Big Data - The 5 Vs Everyone Must KnowBernard Marr
 
Introducing the Big Data Ecosystem with Caserta Concepts & Talend
Introducing the Big Data Ecosystem with Caserta Concepts & TalendIntroducing the Big Data Ecosystem with Caserta Concepts & Talend
Introducing the Big Data Ecosystem with Caserta Concepts & TalendCaserta
 
Analysis of big data in pandemic case
Analysis of big data in pandemic case Analysis of big data in pandemic case
Analysis of big data in pandemic case Muh Saleh
 
Smack presentation sneak_preview
Smack presentation sneak_previewSmack presentation sneak_preview
Smack presentation sneak_previewRelevantz
 
HBaseCon 2013: Evolving a First-Generation Apache HBase Deployment to Second ...
HBaseCon 2013: Evolving a First-Generation Apache HBase Deployment to Second ...HBaseCon 2013: Evolving a First-Generation Apache HBase Deployment to Second ...
HBaseCon 2013: Evolving a First-Generation Apache HBase Deployment to Second ...Cloudera, Inc.
 
ML Infra @ Spotify: Lessons Learned - Romain Yon - NYC ML Meetup
ML Infra @ Spotify: Lessons Learned - Romain Yon -  NYC ML MeetupML Infra @ Spotify: Lessons Learned - Romain Yon -  NYC ML Meetup
ML Infra @ Spotify: Lessons Learned - Romain Yon - NYC ML MeetupRomain Yon
 
Introduction to Big Data Hadoop Training Online by www.itjobzone.biz
Introduction to Big Data Hadoop Training Online by www.itjobzone.bizIntroduction to Big Data Hadoop Training Online by www.itjobzone.biz
Introduction to Big Data Hadoop Training Online by www.itjobzone.bizITJobZone.biz
 
3 Ways Tableau Improves Predictive Analytics
3 Ways Tableau Improves Predictive Analytics3 Ways Tableau Improves Predictive Analytics
3 Ways Tableau Improves Predictive AnalyticsNandita Nityanandam
 
Differences between data lakes and datawarehouse
  Differences between data lakes and datawarehouse  Differences between data lakes and datawarehouse
Differences between data lakes and datawarehouseamarkayam
 
Creating an Enterprise AI Strategy
Creating an Enterprise AI StrategyCreating an Enterprise AI Strategy
Creating an Enterprise AI StrategyAtScale
 
Graph Grid by Atom Rain
Graph Grid by Atom RainGraph Grid by Atom Rain
Graph Grid by Atom RainMeg Vorland
 

What's hot (20)

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
 
What is Big Data ?
What is Big Data ?What is Big Data ?
What is Big Data ?
 
Strayer cis 515 week 10 technical paper database administrator for department...
Strayer cis 515 week 10 technical paper database administrator for department...Strayer cis 515 week 10 technical paper database administrator for department...
Strayer cis 515 week 10 technical paper database administrator for department...
 
Big Data - The 5 Vs Everyone Must Know
Big Data - The 5 Vs Everyone Must KnowBig Data - The 5 Vs Everyone Must Know
Big Data - The 5 Vs Everyone Must Know
 
Introducing the Big Data Ecosystem with Caserta Concepts & Talend
Introducing the Big Data Ecosystem with Caserta Concepts & TalendIntroducing the Big Data Ecosystem with Caserta Concepts & Talend
Introducing the Big Data Ecosystem with Caserta Concepts & Talend
 
Bigdata " new level"
Bigdata " new level"Bigdata " new level"
Bigdata " new level"
 
MicroStrategy at Badoo
MicroStrategy at BadooMicroStrategy at Badoo
MicroStrategy at Badoo
 
Analysis of big data in pandemic case
Analysis of big data in pandemic case Analysis of big data in pandemic case
Analysis of big data in pandemic case
 
Data warehouse presentation
Data warehouse presentationData warehouse presentation
Data warehouse presentation
 
Smack presentation sneak_preview
Smack presentation sneak_previewSmack presentation sneak_preview
Smack presentation sneak_preview
 
Introduction to BigData
Introduction to BigData Introduction to BigData
Introduction to BigData
 
HBaseCon 2013: Evolving a First-Generation Apache HBase Deployment to Second ...
HBaseCon 2013: Evolving a First-Generation Apache HBase Deployment to Second ...HBaseCon 2013: Evolving a First-Generation Apache HBase Deployment to Second ...
HBaseCon 2013: Evolving a First-Generation Apache HBase Deployment to Second ...
 
ML Infra @ Spotify: Lessons Learned - Romain Yon - NYC ML Meetup
ML Infra @ Spotify: Lessons Learned - Romain Yon -  NYC ML MeetupML Infra @ Spotify: Lessons Learned - Romain Yon -  NYC ML Meetup
ML Infra @ Spotify: Lessons Learned - Romain Yon - NYC ML Meetup
 
Introduction to Big Data Hadoop Training Online by www.itjobzone.biz
Introduction to Big Data Hadoop Training Online by www.itjobzone.bizIntroduction to Big Data Hadoop Training Online by www.itjobzone.biz
Introduction to Big Data Hadoop Training Online by www.itjobzone.biz
 
Big data
Big dataBig data
Big data
 
Build Better Data-Driven Insights
Build Better Data-Driven InsightsBuild Better Data-Driven Insights
Build Better Data-Driven Insights
 
3 Ways Tableau Improves Predictive Analytics
3 Ways Tableau Improves Predictive Analytics3 Ways Tableau Improves Predictive Analytics
3 Ways Tableau Improves Predictive Analytics
 
Differences between data lakes and datawarehouse
  Differences between data lakes and datawarehouse  Differences between data lakes and datawarehouse
Differences between data lakes and datawarehouse
 
Creating an Enterprise AI Strategy
Creating an Enterprise AI StrategyCreating an Enterprise AI Strategy
Creating an Enterprise AI Strategy
 
Graph Grid by Atom Rain
Graph Grid by Atom RainGraph Grid by Atom Rain
Graph Grid by Atom Rain
 

Similar to Case Study mypetstop

5 Things You Should Know About Data Products
5 Things You Should Know About Data Products5 Things You Should Know About Data Products
5 Things You Should Know About Data ProductsScribble Data
 
Become Data Driven With Hadoop as-a-Service
Become Data Driven With Hadoop as-a-ServiceBecome Data Driven With Hadoop as-a-Service
Become Data Driven With Hadoop as-a-ServiceMammoth Data
 
HyperconvergedFantasyAnalytics
HyperconvergedFantasyAnalyticsHyperconvergedFantasyAnalytics
HyperconvergedFantasyAnalyticsJerry Jermann
 
Aginity "Big Data" Research Lab
Aginity "Big Data" Research LabAginity "Big Data" Research Lab
Aginity "Big Data" Research Labkevinflorian
 
The book of elephant tattoo
The book of elephant tattooThe book of elephant tattoo
The book of elephant tattooMohamed Magdy
 
Big Data: Its Characteristics And Architecture Capabilities
Big Data: Its Characteristics And Architecture CapabilitiesBig Data: Its Characteristics And Architecture Capabilities
Big Data: Its Characteristics And Architecture CapabilitiesAshraf Uddin
 
How In-memory Computing Drives IT Simplification
How In-memory Computing Drives IT SimplificationHow In-memory Computing Drives IT Simplification
How In-memory Computing Drives IT SimplificationSAP Technology
 
Innovaccer service capabilities with case studies
Innovaccer service capabilities with case studiesInnovaccer service capabilities with case studies
Innovaccer service capabilities with case studiesAbhinav Shashank
 
Stream Meets Batch for Smarter Analytics- Impetus White Paper
Stream Meets Batch for Smarter Analytics- Impetus White PaperStream Meets Batch for Smarter Analytics- Impetus White Paper
Stream Meets Batch for Smarter Analytics- Impetus White PaperImpetus Technologies
 
GERSIS INDUSTRY CASES
GERSIS INDUSTRY CASESGERSIS INDUSTRY CASES
GERSIS INDUSTRY CASESSergej Markov
 
Harness the power of data
Harness the power of dataHarness the power of data
Harness the power of dataHarsha MV
 
Ab cs of big data
Ab cs of big dataAb cs of big data
Ab cs of big dataDigimark
 
Data Warehouses & Deployment By Ankita dubey
Data Warehouses & Deployment By Ankita dubeyData Warehouses & Deployment By Ankita dubey
Data Warehouses & Deployment By Ankita dubeyAnkita Dubey
 
MAALBS Big Data agile framwork
MAALBS Big Data agile framwork MAALBS Big Data agile framwork
MAALBS Big Data agile framwork balvis_ms
 

Similar to Case Study mypetstop (20)

Big data
Big dataBig data
Big data
 
5 Things You Should Know About Data Products
5 Things You Should Know About Data Products5 Things You Should Know About Data Products
5 Things You Should Know About Data Products
 
Become Data Driven With Hadoop as-a-Service
Become Data Driven With Hadoop as-a-ServiceBecome Data Driven With Hadoop as-a-Service
Become Data Driven With Hadoop as-a-Service
 
HyperconvergedFantasyAnalytics
HyperconvergedFantasyAnalyticsHyperconvergedFantasyAnalytics
HyperconvergedFantasyAnalytics
 
Aginity "Big Data" Research Lab
Aginity "Big Data" Research LabAginity "Big Data" Research Lab
Aginity "Big Data" Research Lab
 
The book of elephant tattoo
The book of elephant tattooThe book of elephant tattoo
The book of elephant tattoo
 
Machine Data Analytics
Machine Data AnalyticsMachine Data Analytics
Machine Data Analytics
 
Big Data: Its Characteristics And Architecture Capabilities
Big Data: Its Characteristics And Architecture CapabilitiesBig Data: Its Characteristics And Architecture Capabilities
Big Data: Its Characteristics And Architecture Capabilities
 
How In-memory Computing Drives IT Simplification
How In-memory Computing Drives IT SimplificationHow In-memory Computing Drives IT Simplification
How In-memory Computing Drives IT Simplification
 
Unlocking big data
Unlocking big dataUnlocking big data
Unlocking big data
 
Innovaccer service capabilities with case studies
Innovaccer service capabilities with case studiesInnovaccer service capabilities with case studies
Innovaccer service capabilities with case studies
 
Stream Meets Batch for Smarter Analytics- Impetus White Paper
Stream Meets Batch for Smarter Analytics- Impetus White PaperStream Meets Batch for Smarter Analytics- Impetus White Paper
Stream Meets Batch for Smarter Analytics- Impetus White Paper
 
The ABCs of Big Data
The ABCs of Big DataThe ABCs of Big Data
The ABCs of Big Data
 
GERSIS INDUSTRY CASES
GERSIS INDUSTRY CASESGERSIS INDUSTRY CASES
GERSIS INDUSTRY CASES
 
Harness the power of data
Harness the power of dataHarness the power of data
Harness the power of data
 
Ab cs of big data
Ab cs of big dataAb cs of big data
Ab cs of big data
 
MuthulakshmiRajendran
MuthulakshmiRajendranMuthulakshmiRajendran
MuthulakshmiRajendran
 
Unit 5
Unit 5 Unit 5
Unit 5
 
Data Warehouses & Deployment By Ankita dubey
Data Warehouses & Deployment By Ankita dubeyData Warehouses & Deployment By Ankita dubey
Data Warehouses & Deployment By Ankita dubey
 
MAALBS Big Data agile framwork
MAALBS Big Data agile framwork MAALBS Big Data agile framwork
MAALBS Big Data agile framwork
 

Case Study mypetstop

  • 1. Case Study - FastStats Marketing Database Solution delivered by In Two Weeks! The Problem mypetstop had three identical Access databases in three locations generated by their Pet Admin software. Each database had the same customer ids in common and no easy way of creating reports or analysis for individual locations or across all three. The analysis was needed to derive the Marketing Strategy with their newly-appointed agency Gratterpalm and to drive the business forward. There was also some duplication of records identified on the system.
  • 2. Case Study - FastStats Marketing Database Solution delivered by In Two Weeks! The Approach Gratterpalm brought in specialists Database Marketing Ltd to design and build a new marketing database solution that would merge all three databases together, create a unique range of customer ids, clean up any duplicates and deliver back to mypetstop a solution that the marketing department would have on their desktop. Non-technical users would be able to create their own counts, selections, reports and analysis.
  • 3. Case Study - FastStats Marketing Database Solution delivered by In Two Weeks! The Solution Database Marketing Ltd first created a Sql Server database to manipulate the data to create a unique range of customer ids for each location, delete duplicate customer records but maintain all transactions for the deleted customer ids thus creating a Single Customer View. All three cleaned databases were merged into one with flags created to enable analysis overall or by location. This was all done on systems at DBM with the clean database loaded back to mypetstop. No internal IT resource was needed at mypetstop other than to setup an automated ftp process to upload the Access databases to a secure ftp site and then to download the final processed database. FastStats was chosen as the Marketing Database software and a FastStats database designed and built. The solution included weekly updates. The FastStats software was installed and configured at mypetstop Head Office in Leeds and 4 staff members trained in the use of FastStats. The whole system, including Sql programming, FastStats design & build, software installation and configuration, testing and training was completed within two weeks!
  • 4. Case Study - FastStats Marketing Database Solution delivered by In Two Weeks! James Kundi - mypetstop “ I've been using Faststats for a month now and I'm very impressed with its user-friendly interfaces, broad range of reports and interactive graphical views. Through interrogating our previously dormant database on FastStats, we quickly and efficiently gained valuable knowledge on our customer demographics, spending habits, average length of stay and most popular service. This information will help us to deliver a more effective, targeted marketing communication campaign in 2010.“