Some common pitfalls of data projects - and how to mitigate the risks. Recorded at Big Data World London 2019, Jay Benedetti, Solutions Director from CloverDX illustrates some examples and best practices using three successful data projects we've worked on.

Common pitfalls of data projects:
- Making the project too large
- Not defining the project scope well and getting alignment between business and technical teams
- Lacking a champion
- No clear, agreed definition of success
- Selecting the wrong approach or tools

Example 1: A global reinsurance company overcoming scaling challenges to build an enterprise data warehouse in Azure. The project resulted in an automated devops/deployment strategy in Azure, automating code deployment between dev, testing and production cycles

Example 2: A global logistics company overcoming siloed teams and an unclear strategy to building a data warehouse for better collaboration between teams, automated data ingestion, and more efficient invoicing and reporting across the business.

Example 3: A company migrating 800TB of structured and unstructured data from on-premise to cloud (AWS). The project involved carefully defining the cloud strategy and mitigating the challenges that come from extracting, moving and validating such a large volume of data.

00:00 - Introduction
01:52 - Common pitfalls - why projects fail
05:26 - Example 1 - building an enterprise data warehouse in Azure
09:47 - Example 2 - building a data warehouse to enable automated data ingestion, invoicing and reporting
13:48 - Example 3 - migrating 800TB of data from on-premise to AWS

Read more:
- Data migration with the CloverDX data platform: [ Ссылка ]
- Data warehousing with CloverDX: [ Ссылка ]
- Request a demo: [ Ссылка ]

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