Big Data LDN 2018: REALISING THE PROMISE OF SELF-SERVICE ANALYTICS WITH DATA VIRTUALISATION
- 1. Mark Pritchard, Sales Engineering November, 2018 Realising the Promise of Self-Service Analytics with Data Virtualization
- 3. 2 Self-Service Heaven The Promised Land of Self-Service Initiatives § Let business users access the data that they need § Stop IT being a bottleneck That’s the vision promoted by many BI tool vendors § Give me the tools and access to the data and stand back J 2
- 4. 3 Liberate business users to perform analytics without the assistance of IT The Promised Land of Self-Service Analytics MarketingSales ExecutiveSupport Access to complete information: business entities and pre-integrated views Access to related information: discovery and self service Access data in real-time from different tools, applications and devices Customers Invoices Products Service Usage
- 5. 4 The Reality for Many Business Users and Consumers Tools are designed for data analysts & power users § Who are happy finding, wrangling, cleansing data § Who can create calculations, aggregations & transformations What about the other business users? § People who don’t want to spend hours fighting the spreadsheet… Spreadsheets and desktop tools are isolated § Sitting on one desktop or shared via email Ultimately, can you trust the numbers? § Where did the data come from? How has is been manipulated?
- 6. 5 Challenges of Delivering Self-Service Analytics Fragmented data spread across different sources, systems Multiple, high-maintenance data-integration initiatives Data delays from days to multiple months Poor Data Integrity Untraceable data lineage MarketingSales ExecutiveSupport Database Apps Warehouse Cloud Big Data Documents AppsNo SQL
- 7. 6 Rob van der Meulen, Gartner December 2015 https://www.gartner.com/smarterwithgartner/managing-the-data-chaos-of-self-service-analytics/ Gartner predicts that by 2018 most business users will have access to self-service tools, but that only one in 10 initiatives will be sufficiently well-governed to avoid data inconsistencies that negatively impact the business.
- 8. Building a Platform for Self-Service Analytics The Solution 7
- 9. 8 Self-Service with Guardrails Don’t build just for the ‘data cowboys’ Create a common and consistent semantic layer § Everyone is using the same definitions and metrics Create pre-integrated, pre-calculated data services § Save the user having to do this themselves § Ensures consistency of calculations, etc. But allow the cowboys to ‘roam and wrangle’ § Even the cowboys can only access ‘approved’ data sources
- 10. 9 Self-Service Platform Design A Few Simple Rules… 1. Remember users come in all shapes and sizes § Who are they? What data do they need? What flexibility do they want? 2. Connect to all of the data (but start with the most important) § What data is needed by the users? Open access or pre-aggregated and pre- calculated? 3. Use the language that the business understands § e.g. to Finance it’s an ‘account’, but to Customer Care it’s a ‘customer’. Don’t force people to change terminology…support multiple semantic mappings (to the language of the consumer)
- 11. Self Service Reference Architecture 10
- 12. 11 Faclitating the Self-Service Architecture Five Essential Capabilities of Data Virtualization 4. Self-service data services 5. Centralized metadata, security & governance 1. Data abstraction 2. Zero replication, zero relocation 3. Real-time information
- 13. 12 1. Data Abstraction Abstracts access to disparate data sources. Acts as a single virtual repository. Abstracts data complexities like location, format, protocols …hides data complexity for ease of data access by business Enterprise architects must revise their data architecture to meet the demand for fast data.” – Create a Road Map For A Real-time, Agile, Self-Service Data Platform, Forrester Research
- 14. 13 2. Zero Replication, Zero Relocation …reduces development time and overall TCO The Denodo Platform enables us to build and deliver data services, to our internal and external consumers, within a day instead of the 1 – 2 weeks it would take with ETL.” – Manager, DrillingInfo Leaves the data at its source; extracts only what is needed, on demand. Diminishes the need for effort-intensive ETL processes. Eliminates unnecessary data redundancy.
- 15. 14 3. Real-Time Information Provisions data in real-time to consumers Creates real-time logical views of data across many data sources. Supports transformations and quality functions without the latency, redundancy, and rigidity of legacy approaches …enables timely decision-making Data virtualization integrates disparate data sources in real time or near-real time to meet demands for analytics and transactional data.” – Create a Road Map For A Real-time, Agile, Self-Service Data Platform, Forrester Research, Dec 16, 2015
- 16. 15 4. Self-Service Data Services Facilitates access to all data, both internal and external Enables creation of universal semantic models reflecting business taxonomy Connects data silos to provide best available information to drive business decisions …enables information discovery and self-service Impressively quick turn around time to "unlock“ data from additional siloes and from legacy systems - Few vendors (if any) can compete with Denodo's support of the Restful/Odata standard - both to provide data (northbound) and to access data from the sources (southbound).” – Business Analyst, Swiss Re
- 17. 16 5. Centralized Metadata, Security & Governance Abstracts data source security models and enables single-point security and governance. Extends single-point control across cloud and on-premises architectures Provides multiple forms of metadata (technical, business, operational) to facilitate understanding of data. …simplifies data security, privacy, audit Our Denodo rollout was one of the easiest and most successful rollouts of critical enterprise software I have seen. It was successful in handling our initial, security, use case immediately, and has since shown a strong ability to cover additional use cases, in particular acting as a Data Abstraction Layer via it's web service functionality.” – Enterprise Architect, Asurion
- 18. 17 Denodo Data Virtualization Architecture
- 20. 19 Data Virtualization as the Unified Semantic Layer • Move data integration and semantic layer to independent Data Virtualization platform • Purpose built for supporting data access across multiple heterogeneous data sources • Separate layer provides semantic models for underlying data § Physical to logical mapping • Enforces common and consistent security and governance policies 19 Data Virtualization as Data Integration/Semantic Layer Data Virtualization EDW ODS
- 22. 21 Large Mutual Funds Company Unifed Semantic Layer with Data Virtualization • Lacked common and consistent view of key business metrics • Different answers depending upon which tool or report was used • Too much time discussing veracity of data and not addressing business issues • Management tasked IT with providing a consistent view of data used to drive the business – irrespective of channel used to access the data • Implemented a unified semantic layer using Data Virtualization
- 24. Realising the Promise of Self- Service Analytics Summary 23
- 25. 24 Summary Key Takeaways 1. Universal semantic model provides a common and consistent view of data across organization § No more arguments about data sources and veracity J 2. Data Virtualization allows you to build a flexible semantic model quickly and easily § Provides a platform for self-service with guardrails § Supports both ‘data cowboys’ (with limits) and regular business users 3. Accelerates self-service initiatives – no more analysis silos – while retaining control and governance 24
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