Connecting Data in a Data Fabric for Modern Business Analytics
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This document discusses challenges with traditional data integration approaches and how a graph database can help address these challenges. Traditional approaches involve building static conformed data models, complex ETL processes, and focus on integrating data rather than analyzing it. This separates the data model from business needs and delays getting data into use. A graph database allows modeling just what is needed, mapping sources flexibly, and making data available sooner through connectors. This keeps the data model aligned with business needs and allows continuous enrichment without reloading.
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Connecting Data in a Data Fabric for Modern Business Analytics
- 1. C o n n e c t i n g D a t a i n a D a t a F a b r i c f o r M o d e r n B u s i n e s s A n a l y t i c s Pete Aven, Director Sales Engineering
- 2. • A centralized, near real-time, standardized view of operational data remains a high priority for many industries • Solutions start by bringing data together from disparate sources into a unified repository or view • IT system delivery speed and solution inflexibility is severely hampering deployments
- 3. • “We sell food products.” Business Perspective IT Perspective
- 4. • “Customers can buy our products at stores or online.” Business Perspective IT Perspective
- 5. • “We have offices worldwide.” Business Perspective IT Perspective
- 7. • Silos exist for political, economic, technical, and security reasons • Build your layers, hubs, and lakes • But do something else for those entities and relationships that matter
- 8. • Above the silos of data, are silos of people • In Enterprise Architecture, relationships are lost • Programmatic strategies to data integration exclude business from the conversation
- 9. • AI / Machine Learning • Third party sources of data
- 12. • Create a ‘conformed’ data model for the central database. • For each source of data create ETL that applies business rules to transform data to fit the ‘conformed’ model • Make the ‘conformed’ data model available through SQL. • The hard work is ‘front-loaded’ • It starts with data modeling and code • Data not available until conformed model and ETL complete • Work spread across multiple code modules • Focus is on integrating data and NOT asking questions of the data
- 13. • Data Models are static in an RDBMS • Conformed Data Models require extensive analysis of all sources and knowledge of all consumption use cases to determine the ‘best common denominator’ design. • ETL Transformations are complex • Lots of data migrations, snapshots to accommodate consumer projects • Data NOT in use until populated in “completed” model 1. Starts by cataloguing data sources 2. Create conformed model 3. Churn on ETL 4. Reports to business become a mapping exercise from a complicated model 5. Business + Data disconnect remains
- 14. • Create a whiteboard model for the central database based on key entities and relationships important to business today • For each source of data, create mapping to map sources to the conceptual model • Make data model available through BI Tools, API’s, or the language of underlying database. • The work is ‘back-loaded’ and easily manageable in one location. • Focus of effort now is on algorithmic analysis and asking questions of connected data, not on the never-ending task of integrating the data.
- 15. • Data Model is flexible in graph • You only model and map what you need as you need it and append, enrich. • Mapping sources is simple and can be done through drag and drop and visual interfaces. • Connectors allows you to easily query connected data through BI tools • Data in use much sooner 3. Pull in what you need as you need it. 2. Map sources to model 4. Reports easily available through BI tools 1. Starts with the Business creating Whiteboard Model Graph Database
- 20. • If a system asset goes down, how much money am I going to miss per minute in lost transactions?
- 25. TIME
- 26. DATA IN USE GRAPH DATABASE DEPLOY ETLDATA MODELINGCURRENT STATE SNAPSHOT Model Map Connect DEPLOY TRADITIONAL APPROACH Graph Approach DATA IN USE Model Map Connect DEPLOY Model Map MORE DATA IN USE
- 28. TERADATA CUSTOMER DATASMART ORACLE CALL CENTER DATABASE POS SYSTEM ALLIANCE DATA CREDIT CARD INFO LUMINOSO STORE SCORE DATABASE E-COMMERCE DATABASE & CUSTOMER FILE
- 33. • Conceptual and Physical models closely aligned • Relationships are first class citizens • Entities encapsulated in a format that more easily grokked by humans • Append and enrich data without having to reload, re-index • Top Down Approach to data integration better equipped to fit well within enterprise architecture frameworks • Fewer interfaces needed • Reduced time and effort to data in use • Improved communication across stakeholders
- 34. • A unified view of business • Improved communication across stakeholders • A data model that can adapt and incorporate new data at the speed of business in a format business understands • Faster time to data in use • Manage projects by business objectives instead of technical milestones • Relationships present new opportunities for analytic insights • Understanding what happens between transactions • Illuminate interests, interactions, intents, behaviors, and events
- 35. P E T E A V E N D I R E C T O R , S A L E S E N G I N E E R I N G P E T E @ F A C T G E M . C O M 4 3 5 . 6 4 0 . 4 3 7 0 T W I T T E R : @ P E T E A V E N