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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.
In this webinar we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Join us for an exciting session that will cover:
- The most interesting trends in data management
- How to build a logical data fabric architecture?
- How to manage your data integration strategy in the new hybrid world?
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of the voice computing in the future of data analytics?
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But The Future Can Hold Surprises…
Motorola Razr 2007 Apple iPhone 2007
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ML and AI as to Simplify Data
Management Challenges
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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
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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
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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
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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
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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
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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
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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?”
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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/
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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
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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
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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
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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
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