Speaker: Tyler Brezler You Have to Start Somewhere: A Beginner’s Guide to Implementation Building your first Data Vault can be a bit intimidating, especially if you’re not a seasoned data professional. Collision codes, ghost keys, and PIT tables – oh my! Join Tyler as he examines his experience building a Data Vault for the first
New York University is as large as a major corporation, with 100,000 undergraduate students, 19,000 employees, and 18 schools in three countries. While they haven’t been collecting data for all 191 years of their existence, they have been building analytics to support various areas for many years. Their architecture has evolved through changing source systems,
Challenge: Single Person, Maximum Effectiveness Results: One person, < 3 months merged 2 systems, built a full EDW Gap Analysis Provided to Business Business Vault Generated via Automation Solution Business Vault and Information Layer Tailored to Business Needs Generated: 90% of the ETL code 100% of the Load and Staging Model What Did FinWise Achieve?
Challenge: Automated Build Out Results: 2 people in 2 weeks merged 3 systems, built a full EDW 5 star schemas 3 BI reports zero production errors Generated: 90% of the ETL code 100% of the Staging Data Model 75% of the finished EDW data Model 75% of the star schema data model The competing bid?
Challenge:Rapid Merger and Acuqisition Results: Merged 3 companies in 90 days (Circa 2001) ALL systems, ALL DATA! 125 people, multiple cultures Business Value produced during Due Diligence phase. JP Morgan Chase used the Data Vault model, methodology and architecture to perform due diligence, and ultimately to merge 3 companies in 90 days. I met with
Challenge: Minimized Re-Engineering and Full Auditability Results: “Data Vault 2.0 delivers full auditability by protecting the integrity and origin of the data.” “For us, the Data Vault 2.0 has provided significant benefits. We were able to change our data warehouse whilst minimizing the re-engineering costs. Now we have a system that is more closely aligned
Challenge: Government Agencies Sharing Data Results: CBS (central bureau of statistics) and Belastingdienst (Dutch tax authority) Share Data Between the Agencies By Leveraging Data Vault 2.0 Data Models And leveraging DV2.0 Processes Learn more about Data Vault 2.0 and how we can help securely and impactfully bring data together to produce more business value for
Challenge: Team Size Scalability / Elasticity… Scaling Team Sizes: (Circa 2009) Grew team to 60 people for two weeks Shrunk team to 20 critical staff for business delivery (6 weeks) Included Entry Level When Needed They had a need to increase the team size by leveraging local college students. These were interns to the IT
Challenge: Scalability with No Re-Engineering Produced Data Vault, Scaled to 3 Petabytes (circa 2003) still growing today Sub-second query responses Zero re-engineering to add more data, and expand This project was done to serve the needs of the public. They captured real-time streams that focused on image processing and image recognition. The premise is: capture
Challenge: Replace Legacy Solution Results: 5 Year Program, Finished in 4 years!(Circa 2011-2016) 1 year Early 1 year under-budget! “Balance within 1/10th of 1% Because of the Data Vault”, CFO This program was setup to be a 5 year program. One of the goals of this program was to replace a Legacy Logical Data
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Speaker: Tyler Brezler You Have to Start Somewhere: A Beginner’s Guide to Implementation Building your first Data Vault can be a bit intimidating, especially if you’re not a seasoned data professional. Collision codes, ghost keys, and PIT tables – oh my! Join Tyler as he examines his experience building a Data Vault for the first…
New York University is as large as a major corporation, with 100,000 undergraduate students, 19,000 employees, and 18 schools in three countries. While they haven’t been collecting data for all 191 years of their existence, they have been building analytics to support various areas for many years. Their architecture has evolved through changing source systems,…
Challenge: Single Person, Maximum Effectiveness Results: One person, < 3 months merged 2 systems, built a full EDW Gap Analysis Provided to Business Business Vault Generated via Automation Solution Business Vault and Information Layer Tailored to Business Needs Generated: 90% of the ETL code 100% of the Load and Staging Model What Did FinWise Achieve?…
Challenge: Automated Build Out Results: 2 people in 2 weeks merged 3 systems, built a full EDW 5 star schemas 3 BI reports zero production errors Generated: 90% of the ETL code 100% of the Staging Data Model 75% of the finished EDW data Model 75% of the star schema data model The competing bid?…
Challenge:Rapid Merger and Acuqisition Results: Merged 3 companies in 90 days (Circa 2001) ALL systems, ALL DATA! 125 people, multiple cultures Business Value produced during Due Diligence phase. JP Morgan Chase used the Data Vault model, methodology and architecture to perform due diligence, and ultimately to merge 3 companies in 90 days. I met with…
Challenge: Minimized Re-Engineering and Full Auditability Results: “Data Vault 2.0 delivers full auditability by protecting the integrity and origin of the data.” “For us, the Data Vault 2.0 has provided significant benefits. We were able to change our data warehouse whilst minimizing the re-engineering costs. Now we have a system that is more closely aligned…
Challenge: Government Agencies Sharing Data Results: CBS (central bureau of statistics) and Belastingdienst (Dutch tax authority) Share Data Between the Agencies By Leveraging Data Vault 2.0 Data Models And leveraging DV2.0 Processes Learn more about Data Vault 2.0 and how we can help securely and impactfully bring data together to produce more business value for…
Challenge: Team Size Scalability / Elasticity… Scaling Team Sizes: (Circa 2009) Grew team to 60 people for two weeks Shrunk team to 20 critical staff for business delivery (6 weeks) Included Entry Level When Needed They had a need to increase the team size by leveraging local college students. These were interns to the IT…
Challenge: Scalability with No Re-Engineering Produced Data Vault, Scaled to 3 Petabytes (circa 2003) still growing today Sub-second query responses Zero re-engineering to add more data, and expand This project was done to serve the needs of the public. They captured real-time streams that focused on image processing and image recognition. The premise is: capture…
Challenge: Replace Legacy Solution Results: 5 Year Program, Finished in 4 years!(Circa 2011-2016) 1 year Early 1 year under-budget! “Balance within 1/10th of 1% Because of the Data Vault”, CFO This program was setup to be a 5 year program. One of the goals of this program was to replace a Legacy Logical Data…