Watch full webinar here: https://bit.ly/3ksA0Lr
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, our experts have the advantage of seeing many of these changes happen.
For the first time in Romania, we are bringing together visionary leaders and technical experts for a unique virtual seminar to discuss and help redefine how data is reshaping the modern enterprise and driving digital business. Our guest speakers will share their stories, their business needs and challenges, the solutions explored and finally, the benefits they gained from choosing data virtualization.
Join us at this exclusive invite-only event to explore:
- 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.
Senior IT and business practitioners as well as leaders responsible for driving innovation with cloud and data can't afford to miss this informative event.
2. Speakers
Rick F. van der Lans
Industry Analyst, R20
Alberto Pan
CTO, Denodo
Calin Lupsan
Founder & CEO, Intelligence
3. Agenda
1. Welcome and Opening Remarks – Calin Lupsan, Intelligence
2. Why Has Data Virtualization Revolutionized Data and
Application Integration – Rick F. van der Lans, R20
3. Enabling Agile Analytics and Digital Transformation with a
Enterprise-Wide Data Fabric – Alberto Pan, Denodo
4. Q&A
4. WE SOLVE YOUR TECHNOLOGY PAIN
Calin Lupsan
Founder & CEO, Intelligence
40. 40
Source: Gartner 2018 Data Virtualization Market Guide
In 2020, organizations utilizing data virtualization will spend 45% less
on building and managing data integration processes.”
Through 2022, 60% of enterprises will implement some form of data
virtualization as one enterprise production option for data integration.
Source: Gartner 2018 Data Virtualization Market Guide
42. 42
Gartner Gives DV its Highest Maturity Rating
“Data Virtualization
can be deployed
with low risk and
effort to achieve
maximum value.”
43. 43
Source: Gartner Magic Quadrant for Data Integration, August 2018
Denodo continues to expand its leadership and mind share in data
virtualization, reaching almost 95% of Gartner client inquiries on the subject.”
Denodo grew at an impressive rate in 2018 and 2019... its leadership in
the Data Virtualization market is enabling its growth
Source: Gartner Market Share Analysis: Data Integration Worldwide, 2018 (published August 2019)
and 2019 (published April 2020)
44. 44
Customer Satisfaction
Why Customers Choose Denodo
▪ Gartner Peer Insights “Voice of the
Customer” (Jan 2019, Jan 2020)
▪ Both in 2019 and 2020, the only vendor
where 100% of reviewers would
recommend Denodo
▪ 125+ verified reviews with overall score of
4.7 out of 5
46. Current Challenges in Data Management
1. Faster & more complex demands for decision making
▪ Provide useful information for decision making at all organization levels
▪ New users with advanced analytical skills and needs: e.g. data scientists
▪ Solution? Self Service Initiatives lead by business users, etc. → Either too complex (direct
access) or too costly (specific data marts) , Governance and consistency problems
2. Regulations, enterprise-wide governance & data security
▪ Tens of new regulations worldwide: tax, finance, privacy, HR, environmental, etc.
▪ Ensure consistency in semantics of delivered data and data quality
▪ Enforce security policies
▪ Solution? Data Governance tools. Separate, static system for documentation→ get out of sync
easily, don’t enforce policies & don’t deliver data to users
3. Complexity of DM infrastructure: IT cost reduction
▪ Huge data growth, operation costs → IT is looking for cheaper and more flexible solutions
▪ Solution? Cloud, Data Lakes → Increase integration complexity in the short term. E.g. Gartner
says “83% of Data Lakes projects have failed”
47. 47
Denodo’s Logical Data Fabric Enables Information Self-Service
1. Single Access Point to all Data
at any location
2. Semantic Layer – Expose Data
in Business-Friendly form,
adapted to the needs of each
consumer
3. Up to 80% reduction in
integration costs, in terms of
resources and technology data
4. Consume data with any tool
and access technology (SQL,
REST, GraphQL, OData,…)
5. Single entry point to apply
security and governance
policies
48. 48
Gartner Data Fabric
Data Fabric Net
Compounds Customers Products Claims
RDBMS/OLTP Traditional Analytics/BI Data Lakes Cloud Data Stores Apps and Document
Repositories
Flat Files
Third Party
Legacy
Mart
Data Warehouse
Mart
ETL ETL
XML • JSON • PDF
DOC • WEB
▪ A data fabric is an architecture pattern for the delivery of data objects regardless of deployment platforms and
data location (hybrid, multi-cloud).
▪ It utilizes AI/ML to provide actionable insights and recommendations.
▪ This results in faster and, in some cases, completely automated data access and sharing
▪ Supports both analytics and services orchestration, with integrated governance and security
51. 51
Spectrum Health (Michigan)
Regional Healthcare System (Hospitals, Physicians and
Plans)
• 170 service sites, including hospitals, urgent care centers,
primary care physician offices, community clinics,
rehabilitation, outpatient facilities and elderly care.
• Revenue $6.9 billion with 39,000 employees and volunteers
• Health plan with 1 million members
Primary Challenges
• Integrating multiple analytical data sources quickly
• Reconciling provider data from multiple sources accurately
(business impact)
52. 52
Spectrum Health 1st Project – COVID-19 Dashboard
COMPONENTS:
Tableau, Denodo, Oracle and SQL Server,
10+ other sources
TEAM:
1 Tableau developer, 2 Denodo
developers, 1 Denodo admin
DEVELOPMENT TIME:
• 2 days - Prototype
• 2 weeks – Production*server available
CHALLENGES:
• Very short timeframe
• No formal Denodo training
• Understanding performance
optimization (queries from hours to
less than a minute)
“Overall, I felt the team did an amazing job
and the platform did help us deliver value
much quicker than we would have been able
to going the traditional ETL route. It would
have take us at least 6 weeks.”
- Senior Information Architect
54. 54
Data Platform – Large Commercial Bank
• CIT Group: Large commercial bank grew through acquisitions
• One West Bank, Direct Capital Corporation (DCC)
• Breached SIFI threshold in 2013
• ‘Too big to fail’ financial institution
• Subjected to more scrutiny from federal regulators
• Participate in CCAR (‘stress tests’)
• Needs to provide a complete view of risk across complete organization
• Market, credit, third-party, …
• Used Data Virtualization to expose data to downstream applications and reporting
59. 59
Single Project to Start Their Journey
DV as HR Services Layer
• Single point of entry for HR data consumption
• Scalable to on-premise and cloud data sources
• Seamless support for data source migrations
HR IT’s Worker Capability Migration:
• HR IT recently migrated and consolidated their HR
application layer and moved to consolidated data
warehouse environment.
• As an early adopter of data virtualization, HR IT was
able to easily repoint their business views/interfaces
to the new integrated views, preserving their logical
layer and preventing service disruption due to the
migration.
• Data virtualization has also allowed HR IT to easily
integrate cloud applications to fill the gaps in its
services portfolio.
HR DW1 HR DW2 HR DW3
Worker Business View
HR DW4
BaseViewBaseViewBaseViewBaseView
Int. ViewIntegrated View
HR Apps HR Apps HR Apps New HR App
HR Data Consumers
60. 60
Expanding the Vision
DV as Digital Transformation Accelerator
• Fast data integration
• Easy transformation and mapping
• Ensure consistency with internal glossaries
• Flexible output channels
Federated approach:
• Central team manages the platform, ensures performance
and sets release guidelines
• “Stewards” team provides access to commonly used virtual
views
• Independent teams in every department / LOB create their
own views from common + specific views
• Unified security and governance layer for all data
consuming applications (human and apps)
M&A HR DW
MD Mapping Table
HR Data
Denodo VDP
SvcManagementDB Worker DB
HR DW
M&A Worker View
Intel Worker View
Intel Departements
Intel Worker LocationM&A Translator
CompanyCd Mapping
CostCenterMapping
M&A CC Extract
M&A Cost Ctr Detail
Intel Directory
Users
Groups
iPaaS
Worker Orchestration
ICAPP SQL DBaaS
Working Storage
24 HourTrigger
ICAPP PaaS
ID Reconcilliation
User Driven UI
61. 61
Rapid Enterprise-wide Deployment
61
• 2013 – Initial purchase for HR project
• 2016 – 3 year ELA; multiple projects
• 2013 – <10 data sources, single server
• 2019 – 260+ data sources, 128 core in
production across multiple data centers
• 2013 – Single project team
• 2019 – Intel DV CoE guiding
18/26 BU’s in DV Project Use
• 2013 – 10 DV trained staff
• 2019 – 800+ DV trained staff
62. 62
Benefits of Denodo
Value Driver Metric Goal Actual
Time to Develop Time to develop data service in days 50% 90%
Time to Deploy Time to Deploy data service in days 50% 90%
TTM Overall time it takes to make data service
available for use
60% 90%
Time to Engage Time it takes for business to engage with IT 75% 75%
Performance Performance of data services 50% 60%
Impact Analysis How fast can we perform impact analysis 50% 90%
Enterprise Architectural Alignment Ease at which data from disparate sources can
be integrated
Security, data classification High
64. Problem Solution Results
Case Study
64
Visa accelerates reporting and analytics time-to-market
using data virtualization
Visa is the worlds 2nd largest card payment organization facilitating Visa branded credit and debit cards.
With it’s 8000 worldwide employees, Visa earns $10B in yearly revenue and is headquartered in Foster
City, California. Visa’s global network processes $6.5 trillion or 100B transactions a year.Industry: Financial Services
▪ Visa’s revenue and pricing business unit was
looking for an agile data integration solution to
easily onboard new data sources, as
heterogeneous data proliferated throughout
Visa.
▪ Because of growing volume and complexity of
data, they also wanted a solution that can
provide unified view of enterprise data with
higher performance and scalability.
▪ Visa wanted a solution with low TCO, higher
flexibility and faster time-to-market, to provide
relevant information to business users.
▪ Visa deployed Denodo Platform for data
virtualization to virtually integrate data from
disparate sources, and restructure them to
meet the need without data replication.
▪ Visa wanted to leverage their existing DW, data
marts and data lake for historical data, while
providing real-time-access to information with
data virtualization.
▪ Reduced reporting and analytical turnaround
time by as much as 90% (from 3-15 months to
0,5-3 months on average)
▪ Provided 10x faster turnaround time for
strategic and operational intelligence, while
enhancing information with newer sources of
data.
▪ Increased efficiency in information
management practices, through better data
quality, data governance, data security and data
lineage.
65. 65
II. Business Problem
Revenue & Pricing: Competitiveness | Analytics on Data Lake: Faster Time-to-market
A. Previous Solution:
▪ Physical data marts materialized
▪ Microstrategy on top containing all the semantics and reporting logic
▪ 30K reports SQL-based semantic layer built-into tool – “nightmare to manage”
B. Needs:
▪ Faster response to new information needs
▪ Manage Semantic Layer at Enterprise Level instead of Tool Level - reuse
▪ Integrate Heavy Analytics on 5 PB of data stored in data lake (Hadoop, DB2, Hive)
▪ Single version of truth needed – too many competing views / sources within Visa
66. 66
Credit Cards Company – Reporting & Data Monetization
Project goals:
• Improve data reliability by defining certified data
(sources and metrics) in a logical layer
• Simplify business self-service – hide complexity of back-
end (complex snowflake schema, DB2/Hive dichotomy)
• Lower back-end data cost (move cold data to Hive)
Solution: DV / Denodo Layer above analytical systems:
• Business metadata to document, tag & classify datasets
• Easy-to-use business models across entire domain
• Complex models w/ 150+ joins and 1000s of columns
• Massive data sets with petabytes of information
• Reduced turnaround time by as much as 90% (from 3-
15 months to 0,5-3 months on average)