What is the future of data strategy?
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Watch here: https://bit.ly/3i2iJbu You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen. Join us for an exciting session that will cover: - The most interesting trends in data management. - Our predictions on how those trends will change the data management world. - How these trends are shaping the future of data virtualization and our own software.
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What is the future of data strategy?
- 1. DATA VIRTUALIZATION APAC WEBINAR SERIES Sessions Covering Key Data Integration Challenges Solved with Data Virtualization
- 2. The Future of Data Management Five trends that will shape what’s coming next Pablo Alvarez Director Product Management Denodo
- 3. 3 …It’s Difficult to Make Predictions, Especially About the Future.” Attributed to Niels Bohr (Bulletin of the Atomic Scientist, 1971)
- 4. 4 Analysts: “predict” the future by looking at the present
- 5. 5 ML and AI as to simplify data management challenges
- 6. 6 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
- 7. 7 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
- 8. 8 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
- 9. 9 Welcome to a Hybrid World
- 10. 10 Denodo Customers Cloud Survey - 2019 • More than 60% of companies already have multiple projects in cloud • 25% are Cloud-First and/or are in “advanced” state • Only 4.5% do not have plans for Cloud in the short term • More than 46% have hybrid integration needs, more than 35% are already multi-cloud • Key Use Cases include: Analytics (49%), Data Lake (45%), Cloud Data Warehouse (40%) • 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 Cloud Survey 2019, N = 200. https://www.denodo.com/en/document/whitepaper/denodo-global-cloud-survey-2019
- 12. 12 Data Fabrics will be pervasive
- 13. 13 Data fabric is a hot, emerging market that delivers a unified, intelligent, and integrated end-to-end platform to support new and emerging use cases. The sweet spot is its ability to deliver use cases quickly by leveraging innovation in dynamic integration, distributed and multicloud architectures, graph engines, and distributed in-memory and persistent memory platforms. Data fabric focuses on automating the process integration, transformation, preparation, curation, security, governance, and orchestration to enable analytics and insights quickly for business success. The Forrester Wave: Enterprise Data Fabric, Q2 2020 Noel Yuhana
- 14. 14 Can we just have a repository for all data? • Loss of capabilities: data lake capabilities may differ from those of original sources, e.g. quick access by ID in operational RDBMS • Huge up-front investment: creating ingestion pipelines for all company datasets into the lake is costly • Questionable ROI as a lot of that data may never be used • Replicate the EDW? Replace it entirely? • Large recurrent maintenance costs: those pipelines need to be constantly modified as data structures change in the sources • Risk of inconsistencies: data needs to be frequently synchronized to avoid stale datasets COST GOVERNANCE Can’t we put all company data in a single super repository? Would that be possible? Is that realistic?
- 15. 15 Gartner – The Evolution of Analytical Environments This is a Second Major Cycle of Analytical Consolidation Operational Application Operational Application Operational Application IoT Data Other NewData Operational Application Operational Application Cube Operational Application Cube ? Operational Application Operational Application Operational Application IoT Data Other NewData 1980s Pre EDW 1990s EDW 2010s2000s Post EDW Time LDW Operational Application Operational Application Operational Application Data Warehouse Data Warehouse Data Lake ? LDW Data Warehouse Data Lake Marts ODS Staging/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
- 16. 16 Gartner – Logical Architectures “Adopt the Logical Data Warehouse Architecture to Meet Your Modern Analytical Needs”. Henry Cook, Gartner April 2018 DATA VIRTUALIZATION
- 17. 17 Gartner: Five Key Pillars of a Modern Data Fabric Design Data Consumers Data Sources Final Data Integration and Orchestration Layer Insights and Automation Layer Active Metadata Knowledge Graph Enriched With Semantics Augmented Data Catalog Data Consumers Data Sources Data Fabric
- 18. 18 Voice control and NLP
- 19. 19 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
- 20. 20 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?”
- 21. 21 Data monetization and the API economy
- 22. 22 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/
- 23. 23 DrillingInfo APIs Enable Data Monetization
- 24. 24 Using APIs to add a competitive edge
- 25. 25 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
- 26. Q&A
- 27. 27 Next Steps Access Denodo Platform in the Cloud! Take a Test Drive today! https://bit.ly/2AouQLQ GET STARTED TODAY
- 28. 28 Denodo’s 2020 Global Cloud Survey Webinar
- 29. Next session Virtualization for Business Users with Denodo’s Data Catalog Sushant Kumar Product Marketing Manager, Denodo Chris Day Director, APAC Sales Engineeing, Denodo
- 30. Thanks! www.denodo.com [email protected] © Copyright Denodo Technologies. All rights reserved Unless otherwise specified, no part of this PDF file may be reproduced or utilized in any for or by any means, electronic or mechanical, including photocopying and microfilm, without prior the written authorization from Denodo Technologies.