Data warehouses. Who needs them anymore…right? Today’s self-service analytics tools such as Tableau and Cognos Analytics V11 have put tremendous power in the hands of the departmental business user. And gone are the days of 65,000 row limits in Microsoft Excel.
Business managers can directly tap large information sources throughout the organization to create insight-rich visualizations and dashboards, often without the need to wait for IT resources to deliver data. Vendors of new technologies such as Hadoop often tout large cost savings by doing away with traditional ETL tools and data warehouse environments.
So why would an organization still want to invest the time and energy (and dollars) to create a centralized repository of data? And what processing and transformations are still prudent, if not mandatory, to do in a centralized fashion?
In this recorded webinar, Senturus CEO John Peterson discusses the modern data warehouse with 29+ reasons why you still need to properly pre-process and stage your data. He touches on topics ranging from security to performance to universal definitions to blending data sources to snap-shots and more.
Presenter
John Peterson
CEO and Co-Founder
John is the company’s thought leader and visionary. John directs the delivery of all projects with Senturus, providing the bridge of technical and business understanding.
Read morePresentation outline
- Analytics Critical Success Factors
- Architectures and data transformation
- BI tools
- Methodologies and techniques
- People and processes
- Goals of Business Analytics
- Help monitor, analyze, plan and predict
- Support and improve decision-making throughout the organization
- Drive competitive advantage
- Business Intelligence Drives Competitive Advantage
- Universal BI System Requirements
- Deliver a stable and user-friendly data structure
- Provide fast performance
- Handle multiple sources of data
- Deliver high quality, validated data
- Maintain historical data in a common format
- Provide additional ways to “roll-up” data
- Provide field, table and measure names that make sense to business users
- Enable pre-calculations for commonly used measures
- Provide user- and role-based security
- Technical Solution
- Separate intensive query and reporting tasks from servers and disks used by transaction processing (OLTP) systems
- Create data models and technologies optimized for query and reporting that are NOT appropriate for transaction processing
- Transform data and embed knowledge, roll-ups and business logic into the data structures so that non-IT users can perform self-service BI
- Create a single location where information from multiple source systems can be accessed and combined for reporting purposes
- Provide a validated repository of data that has been cleaned of inaccurate or spurious data quality issues
- Maintain a repository of historical data gathered from prior and legacy sources and data that would otherwise be purged from the current transaction processing system(s)
- Allow for secured access to data for analytics without opening up access to systems where data might inadvertently be modified or transaction processing performance hindered
- Provide a stable platform on which end-users can build customized reports, dashboards and analytics
- The Complete Solutions
- Properly staged data
- Good tools to consume and use the information
- Create a Data Warehouse
- Definition: Data Warehouse
- What We Will Not Cover
- Data warehouses and business analytics systems can be built with a dizzying array of technologies and tools
- … And they are changing daily
- The variety of technical options has exploded (as value of data increases)
- No fancy new paradigms to shift
- No logical, physical, virtual mumbo-jumbo
- Data Warehouses are not Universal Panaceas
- 29+ Specific, Pragmatic Reasons Why
- Consolidates multiple sources of data
- Retains history when changing/upgrading systems
- Captures snapshots and realigns data
- Consolidates data from cloud and on-premise
- Provides persistent storage of critical data
- Increases efficiency by storing data only once
- Cleanses data
- Handles and fixes NULL values
- Applies universal, one-time filters
- Improves performance (end-user reports)
- Eliminates expensive and incorrect joins
- Transforms complex source data into usable facts
- Captures strategic business-centric metrics
- Consolidates and simplifies disparate attribute data
- Adds mandatory business dimensional richness
- Simplifies complex data relationships
- Applies logic to complete and align data
- Facilitates allocation and attribution
- Provides insulating layer from source systems
- Eliminates complex logic needed in BA layer(s)
- Allows for slow-changing dimensions
- Captures slow-changing facts
- Eliminates live connections to source data
- Eliminates spreadmarts and local databases
- Reduces risk of trained person(s) leaving
- Eliminates incorrect calculations by end-users
- Provides security and controlled access
- Helps reduce software license fees
- Enables consolidated dashboards and aligned metrics
- Enables better dashboards thru context
- Takeaways
- To make data truly usable and valuable, it needs to be transformed and enriched
- Transformation needs to take place somewhere between the raw source and the final report/analysis
- Question of where and how you handle that transformation
- Not addressing this simply forces business end-users to tackle it themselves
- Which leads to: Excel hell Access aggravation and complex, slow and inaccurate self-service dashboards and reports
- A properly architected data warehouse has its place… and can help
- Benefits of Properly Staged Data
- Better decisions
- Faster actions
- Unified strategic direction – what gets measured, gets managed
- Greater efficiency – less time in Excel hell
- Less redundancy and waste
- Fewer errors – some can cost millions of dollars
- Happier business users
- Greater user adoption
- Competitive advantage and higher ROIC
