Future of Data Strategy
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The document discusses the future of data strategy with a focus on the role of machine learning and AI in simplifying data management challenges, enhancing data virtualization, and improving business analytics. It highlights trends towards hybrid cloud integration, the adoption of APIs for data monetization, and the necessity for adaptive data architectures to accommodate emerging technologies. Furthermore, it emphasizes the importance of using data virtualization to streamline processes and facilitate analysis in various contexts, including predictive analytics and supply chain management.
Future of Data Strategy
- 1. WEBINAR Future of Data Strategy
- 2. Speaker Paul Moxon SVP Data Architecture & Chief Evangelist Denodo
- 3. 3 Attributed to Niels Bohr (Bulletin of the Atomic Scientist, 1971) …It’s Difficult to Make Predictions, Especially About the Future.”
- 4. 4 Analysts: “Predict” The Future By Looking At The Present
- 5. 5 But The Future Can Hold Surprises… Motorola Razr 2007 Apple iPhone 2007
- 6. 6 ML and AI as to Simplify Data Management Challenges
- 7. 7 ML and AI to Simplify Data Management Challenges ▪ Data science practices are already common in many companies to produce better insights that enable business decisions ▪ Data Scientists have been one of the most popular jobs in recent years ▪ Currently common practice for resource allocation, supply chain management, fraud detection, predictive analytics, etc. ▪ Denodo is already frequently used in this scenarios as a way to simplify and accelerate data exploration and analysis https://www.denodo.com/en/webinar/customer-keynote-data-virtualization-modernize-and-accelerate-analytics-prologis
- 8. 8 Artificial Intelligence in Data Management ▪ Software vendors have started to incorporate similar techniques to analyze their data and automate all kind of tedious tasks ▪ These techniques can provide actions and expertise that otherwise required manual intervention of a human expert • Scales to process large data volumes • Reduces the workload of repetitive tasks on skilled profiles ▪ In the data management space, one of the first successful applications of these techniques is helping to identify data quality issues and potentially sensitive data ▪ Many vendors now incorporate some form of AI tagging, automatic classification, ML security assessment, etc. https://www.wsj.com/articles/how-data-management-helps-companies-deploy-ai-11556530200
- 9. 9 Application in Data Virtualization ▪ Enhance data discovery ▪ Dataset recommendations based on your profile ▪ Simplify data modeling ▪ Relationship discovery based on usage analysis ▪ Suggestions for filters ▪ Improve performance ▪ Tuning recommendations ▪ Self healing bottlenecks
- 10. 10 Welcome to a Hybrid World
- 11. 11 Denodo Global Cloud Survey 2020 • More than 75% of companies already have projects in cloud • Over 15% are Cloud-First and/or are in “advanced” state • Only 3.97% do not have plans for Cloud in the short term • More than 53% have hybrid integration needs • Key Use Cases include: Analytics (50%), Data Lake (31%), AI/ML (28%) • Less than 9% of on-prem systems are decommissioned (Forrester estimates 8%) • Key Technologies in Cloud Journey: Cloud Platform Tools (56%), Data Virtualization (49.5%), Data Lake Technology (48%) Source: Denodo Global Cloud Survey 2020
- 12. 12 Avoid Hybrid/Multi-Cloud Point-to-Point Connections Source: By Unknown author - Tekniska museet, Public Domain, https://commons.wikimedia.org/w/index.php?curid=3877011
- 14. 14 Voice Control and NLP
- 15. 15 Voice Control and NLP ▪ Voice control has already taken over our homes ▪ Siri, Alexa, Google Home can give you the weather, read the daily news, control lights and thermostats, etc. ▪ In BI and Analytics, systems are starting to adopt natural language as a way to query the system by non technical users ▪ As this technologies progress, business users and sales reps in the field will be able to ask for complex information from their phones and tablets
- 16. 16 Voice Computing: Humanizing Data Insights Natural Language Processing enabled business users to post a question to a chatbot and receive an answer with data insights that are completely humanized “The total Q3 sales for Product A in Mexico totaled $200.4 M, a 15% increase from Q2” “What are the Q3 sales trends for Product A in Mexico?”
- 17. 17 Data Monetization and the API Economy
- 18. 18 Data Monetization and the API Economy ▪ The market for data applications is predicted to have the largest growth by segment in coming years ▪ Application to application communication is done via APIs, and therefore APIs have become the cornerstone of many digital transformation initiatives ▪ API access (vs direct access through their website) already accounts for a significant portion of the revenue of Internet giants ▪ There is also a significant market of companies that use data as their main asset, and their business model is to “sell APIs” ▪ In addition, traditional companies have started to use their data as an additional asset https://www.statista.com/statistics/255970/global-big-data-market-forecast-by-segment/
- 19. 19 DrillingInfo APIs Enable Data Monetization
- 20. 20 Denodo Data Services ▪ Data virtualization enables API access to any data connected to the virtual layer, with zero coding ▪ It includes security controls to show different data depending on the user/role ▪ You can add complex workload management policies, including quotas (e.g. 100 queries/hour) ▪ Denodo supports a wide range of protocols and options ▪ GraphQL ▪ GeoJSON (geospatial APIs) ▪ OData 4 ▪ OAuth 2.0, SAML and SPNEGO authentication ▪ OpenAPI (pka Swagger) documentation
- 21. 21 Data Fabrics and Adaptive Data Architectures
- 22. 22 Data fabric focuses on automating the process integration, transformation, preparation, curation, security, governance, and orchestration to enable analytics and insights quickly for business success. It minimizes complexity by automating processes, workflows, and pipelines, generating code and streamlining data to accelerate various use cases such as customer 360, data science, fraud detection, internet-of-things (IoT) analytics, risk analytics, and healthcare insights.” The Forrester Wave™: Enterprise Data Fabric, Q2 2020
- 23. 23 Gartner – The Evolution of Analytical Environments Logical Architectures are a Second Major Cycle of Analytical Consolidation Operational ApplicationOperational Application Operational ApplicationOperational Application Operational ApplicationOperational Application IoT DataIoT Data Other NewDataOther NewData Operational Application Operational Application Operational Application Operational Application CubeCube Operational Application Operational Application CubeCube ?? Operational ApplicationOperational Application Operational ApplicationOperational Application Operational ApplicationOperational Application IoT DataIoT Data Other NewDataOther NewData 1980s Pre EDW 1990s EDW 2010s2000s Post EDW Time LDW Operational Application Operational Application Operational Application Operational Application Operational Application Operational Application Data Warehouse Data Warehouse Data Warehouse Data Warehouse Data Lake Data Lake ?? LDWLDW Data WarehouseData Warehouse Data LakeData Lake MartsMarts ODSODS Staging/IngestStaging/Ingest Unified analysis › Consolidated data › "Collect the data" › Single server, multiple nodes › More analysis than any one server can provide ©2018 Gartner, Inc. Unified analysis › Logically consolidated view of all data › "Connect and collect" › Multiple servers, of multiple nodes › More analysis than any one system can provide ID: 342254 Fragmented/ nonexistent analysis › Multiple sources › Multiple structured sources Fragmented analysis › "Collect the data" (Into › different repositories) › New data types, › processing, requirements › Uncoordinated views “Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs”. Henry Cook, Gartner April 2018
- 24. 24 Adaptive Data Architectures • Organizations need an adaptive data architecture • An architecture that can flex and adapt to new technologies, new data sources, new formats, new protocols, etc. while minimizing the impact on the consumers • Future-proofs the architecture • Despite the preceding slides…we can’t predict what technologies will emerge in next 3-5 years (or 5-10 years), but we can build architectures that will accommodate them • Allows users to access new data, new technologies using existing, familiar tools • e.g. read data from a Parquet file using Excel (via the Data Virtualization Platform) • A Data Fabric – built on Data Virtualization – provides this adaptability and protects your existing technology investments and de-risks the adoption of new, emerging technologies
- 25. 25 Adaptive Data Architecture Reporting Analytics Data Science Data Market Place Data Monetization AI/ML iPaaS Kafka ETL CDC Sqoop Flume RawDataZoneStagingArea CuratedDataZoneCoreDWHmodel Data Warehouse Data Lake Data Virtualization Platform Analytical Views Data Science Views λ Views Real-Time Views DWH Views Hybrid Views Cloud Views UniversalCatalogofDataServices CentralizedAccessControl Enterprise Data Fabric
- 27. 27 Next Steps Access Denodo Platform in the Cloud! Take a Test Drive today! www.denodo.com/TestDrive GET STARTED TODAY
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