Agenda11:00
Las tendencias y los nuevos retos tecnológicos en el sector de la manufactura
Fernando Sancén, Director & CEO, Enki
11:30
Introducción a la virtualización de datos
Amanda Lleyda, Partner Sales & Channel Iberia & LATAM, Denodo
11:45
Casos de uso: ¿cómo las empresas manufactureras se están beneficiando de la virtualización de
datos para elevar su actividad al rango de industria 4.0?
Amanda Lleyda, Partner Sales & Channel Iberia & LATAM, Denodo
12:15
Demostración en vivo de la solución aplicada al análisis en tiempo real de datos industriales
Iván López Torres, Sales Engineer LATAM, Denodo
12:45
Sesión de preguntas y respuestas

¿Qué es?

¿Por qué está
sucediendo?

Tendencias
Decisiones orientadas
por datos
La cadena de suministros
se está reinventando
Producción automatizada
y democrática
Fábricas Autónomas e
inteligentes

BLOCKCHAIN

¿Cuál es su impacto?
Datos, Datos y más Datos
•Inteligenciaartificial
•IoTy5G
•BigData&CloudIndustrial

Losmayoresdesafíos parauna
manufacturabasadaendatos
•Sistemasdecontrolactivados poreventos
•Unmodelodedatosunificado
•Integracióndesistemasheredados
•Desafíosdeseguridad

Data Virtualization
Overview

10
Denodo
The Leader in Data Virtualization
DENODO OFFICES, CUSTOMERS, PARTNERS
Palo Alto, CA.
Global presence throughout North America,
EMEA, APAC, and Latin America.
LEADERSHIP
▪ Longest continuous focus on data
virtualization – since 1999
▪ Leader in 2018 Forrester Wave – Big
Data Fabric
▪ Winner of numerous awards
CUSTOMERS
~700 customers, including many F500 and
G2000 companies across every major industry
have gained significant business agility and ROI.
FINANCIALS
Backed by $4B+ private equity firm.
50+% annual growth; Profitable.

11
Very few companies are able to effectively use that data for growth or profitability
Manufacturing industry generates most of the world’s data
The digital revolution is knocking
manufacturers through innovations:
• IoT
• Machine learning and Artificial Intelligence
• cloud technology
• big data, other areas
“While the majority of manufacturing industry executives acknowledge the importance
of digital transformation, only 5% are satisfied with their current digital strategies.”
*Forbes’ Top 5 Digital Transformation Trends In Manufacturing

12
Many companies are investing in modern technologies and frameworks
Obstacles to Digital Transformation
An overwhelming 33% of respondents cited
readiness of process and systems as the
obstacle to digital transformation.
* SpencerStuart survey on the industrial sector.
• Many are challenged by petabyte-
scale volumes of machine generated
data and field data.
• For many manufacturing companies,
the data silos remain, a challenge to
data architects.
• These obstacles severely limit the
number of actionable insights that
can be gained from the manufacturing
and supply chain process.

13
¿Qué es la virtualización de datos?

14
Data Virtualization
The Solution – Data Abstraction Layer
Consume
in business applications
Combine
related data into views
Connect
to disparate data sources
2
3
1
DATA CONSUMERS
DISPARATE DATA SOURCES
Enterprise Applications, Reporting, BI, Portals, ESB, Mobile, Web, Users
Databases & Warehouses, Cloud/Saas Applications, Big Data, NoSQL, Web, XML, Excel, PDF, Word...
Analytical Operational
Less StructuredMore Structured
CONNECT COMBINE PUBLISH
Multiple Protocols,
Formats
Query, Search,
Browse
Request/Reply,
Event Driven
Secure
Delivery
SQL,
MDX
Web
Services
Big Data
APIs
Web Automation
and Indexing
CONNECT Normalized views of disparate data
COMBINE
CONSUME Share, Deliver, Publish, Govern, Collaborate
“Data virtualization
integrates disparate
data sources in real
time or near-real
time to meet
demands for
analytics and
transactional data.”
– Create a Road Map For A
Real-time, Agile, Self-
Service Data Platform,
Forrester Research, Dec 16,
2015
Discover, Transform, Prepare,
Improve Quality, Integrate

15
Six Essential Capabilities of Data Virtualization
4. Self-service data services
5. Centralized metadata, security
& governance
6. Location-agnostic architecture for
multi-cloud, hybrid acceleration
1. Data abstraction
2. Zero replication, zero relocation
3. Real-time information

16
Source: Gartner 2018 Data Virtualization Market Guide
“Through 2022, 60% of all organizations will implement data
virtualization as one key delivery style in their data integration
architecture”

17
Data virtualization has proven to be the most innovative and comprehensive data fabric
Data Virtualization Holds the Key
Many companies manage data that is scattered
across cloud and on-premises systems.
Data stakeholders use to streamline the
processes, increase manufacturing yield,
or improve manufacturing quality.
Helps connect data for varieties purposes:
• data analytics
• a single view of the
manufacturing process
• data services
• other applications
Stitch together the widest range of
• data sources
• in real time
• without physical data movement
The modern value chain involves
from highly structured data to
completely unstructured.

18
The Benefits of Data Virtualization for Manufacturing
Manifold increases in production yield and product time-to-market.
Improved product quality and customer satisfaction.
Improved security and compliance with regional rules, by avoiding replication.
Improvement in the preventative maintenance of parts, and revenue growth
from enhanced part sales.
Lower TCO and higher ROI, investments usually breaking even within a year.

Use Cases for Manufacturers

Problem Solution Results
Case Study
20
Schaeffler created a new Data Platform with a physical and virtual data hub
“Digital Agenda” to provide value to customers
by optimizing business processes by
establishing new data-driven business models.
• Multiple consuming applications for
reporting and self-service BI, monitor and
alert, data applications, data exploration
and analysis.
• Close to 20 different type of internal systems
Data sources were integrated on an ad hoc
basis depending on the requirement.
• Growing number of use cases that required
recent and fresh data without any latency.
• Ingest data into the Azure data lake that
formed the virtual data lake
• Manage the security related issues.
• Provide data with low latency for specific
business requirements that needed fresh
and recent data.
• Create virtual views with ease and also
saved a lot of development time and effort.
• Denodo formed the core of the Schaeffler
Cloud Data Platform and enabled data
integration and harmonization between the
physical and virtual data hub.
• The Denodo implementation was very easy
and lightweight and provided standard
connection interfaces like JDBC, ODBC, Rest
services etc. made it possible to connect to
multiple data sources.
• Denodo provided the single point of access
to enterprise data .
The Schaeffler Group is a global automotive and industrial supplier. Schaeffler provides
high-precision components and systems in engine, transmission, and chassis applications,
as well as rolling and plain bearing solutions for a large number of industrial applications.

21
Schaeffler
StatusQuo:DataSourcesintegratedin aper Use-Case/Applicationfashion
Use
Cases
MaQS
ProConnectBox
Media Center
ADLS
Find &
Understand
Monitor &
Alert
Report &
Self-ServiceBI
Explore&
Analyse
Build DataDriven
Applications
OfferDigital
Products
Data
Sources

22
Schaeffler
TheNewSchaefflerDataPlatformatscale
Use
Cases
MaQS
ProConnectBox
Media Center
ADLS
Find &
Understand
Monitor &
Alert
Report &
Self-ServiceBI
Explore&
Analyse
Build DataDriven
Applications
OfferDigital
Products
Data
Sources
Virtual DataHub
• Single point of access
• Lightweight implementation
• Standard interfaces
• Homogeneous Data Model

23
Schaeffler
DataDrivenProjects
The fair KPI for your data platform:
#SuccessfulProjects + #InnovationPoCThe fair KPI for your data platform
Success =
#TotalProjects

25
IoT data drives Predictive Maintenance
Caterpillar

26
Supply Chain Planning
Challenges around KPIs
• Supply Chain Planning side of Logistics
• Challenges in Logistics Planning include Demand,
Supply, Inventory, Delivery, Fulfillment including
manufacturing and outsourcing, Strategic
sourcing managing the supplier/vendor base.
• Collaboration is required with the customer base
Suppliers, Logistic partners or other external
entities is also a close match to this use case
• Difficult to calculate Supply Chain Planning KPI’s
• Difficult to extract data elements for Supply chain
KPI calculation

27
Supply Chain Planning KPI’s
Considerations to calculate the KPI’s

28
Business Need Solution Benefits
Case Study McCormick used Denodo data virtualization to improve quality
assessment of their product
• AI and ML project required data spread across
all McCormick's internal systems spread across
4 different continents and in spreadsheet.
• Portions of data that were shared with
McCormick's research partner firms needed to
be masked and at the same time unmasked
when shared internally.
• Create a data service to simplify the process of
data access and data sharing and also be used
by the analytics teams for their ML projects.
• Denodo used as a semantic and data
discovery layer. integrates data from systems
and spreadsheets to create a data service for
business and analytics users.
• Denodo semantic layer was used to connect
to the API management and runtime layer to
provide data for the ML and analytics projects.
• Denodo also used to implement a centralized
data governance and security layer over all of
McCormick's enterprise data.
• ML learning applications were able to access
refreshed, validated and indexed data in real time
without any replication from Denodo enterprise
data service.
• Enterprise data service gave the business users
the capability to compare data in multiple
systems.
• Denodo used to populate the spreadsheets based
on the gaps in information and also determine the
quality of proposed data and services.
McCormick & Company is an American food company that manufactures, markets, and
distributes spices, seasoning mixes, condiments, and other flavouring products for the industrial,
restaurant, institutional, and home markets.Industry: Food and Beverage

29
McCormicK Semantic Layer
Data Services
• Information is directly in
application
• Timely Information
• No replication of information
• No need to validate information
• Consistent searching
• Better staging for learning

31
BI enablement via Logical DataWarehouse
Intel – Extreme DW

32
HR use of DV as Logical Layer
Intel – Single view of Employee

33
HR use of DV as Logical Layer
Intel – Single view of Support M&A

34
Data Service Layer for streamlining business processes in the value chain
Intel – Supplier Master Data
Use Case
Process key role
• Supplier Master Data gathers information
about companies
• These are companies that Intel purchases
from, pays, outsource manufactures with
• Choosing a Supplier is the point of entry to
many business process.
• If it fails or is slow, it impacts all 70+
downstream consumers
Source: Intel EDW 2015

35
Data Service Layer for streamlining business processes in the value chain
Intel – MySamples
Use Case
Process key role
• MySamples
• Need to show the latest status of samples
requests.
• Customer information from MySamplesapp
• Samples request information (if requested) from
the ERP system
• Samples shipment status (if shipped) from the
Event Management system
Source: Intel EDW 2015

36
Data Service Layer for streamlining business processes in the value chain
Intel – Cloud CRM Integration
Use Case
Process key role
• Integrate several data sources and expose
it as service.
• Data Sources refer to customer info hold
on premise
• Published services are used by Cloud CRM
Source: Intel EDW 2015

37
ROI and TCO of Data Virtualization
Intel - Metrics
Value Driver Metric Goal Actual
Time to Develop Time to develop web service in days 50% 90%
Time to Deploy Time to Deploy web service in days 50% 90%
TTM Time to make web service available 60% 90%
Time to Engage Time for business to engage with IT 75% 75%
Performance Performance of web services 50% 60%
Impact Analysis How fast to perform impact analysis 50% 90%
Enterprise Architectural
Alignment
Ease at which data from disparate
sources can be integrated
Security, data
classification
High
Savings:
• Time-to-Market
• Development
• Test Cost

Architecture

39
Logical Data Warehouse Reference Architecture
Reporting
Analytics
Data Science
Data Market Place
Data Monetization
AI/MM
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
Logical Data Warehouse

40
Denodo in a Multi-Location Multi-Cloud Architecture

Demo

Demo Scenario

43
What’s the demo scenario
We have a traditional Data Warehouse in Oracle.
External database objects can be accessed as virtual tables within
SAP HANA database.
SAP BW is SAP’s multidimensional engine for enterprise analytics.
Need to easily build reports using data coming from these sources.

44
Example
Detail of clients that have
received orders in 2020?
▪ Deliveries managed by an
external system that feeds data
into Oracle.
▪ Sales data consumed by SAP BW.
▪ Customer details table, store in
SAP HANA. Sources
Combine,
Transform
&
Integrate
Consume
Base View
Source
Abstraction
join
group by
customer
join
Deliveries Sales Material Customers

How does execution work

46
What is the scenario?
The DV system only stores Metadata
Data is external
• Needs to travel through the Network
• To address: Minimize network traffic
Data is distributed in multiple systems
• Needs to be integrated in the virtual layer
• Some sources have processing capabilities
• To address: Maximize processing at sources to reduce load in virtualization layer

47
Why is this so important?
SELECT c.name, AVG(s.amount)
FROM customers c JOIN sales_material s
ON c.id = s.customer_id
GROUP BY c.name
How Denodo works compared with other federation engines
System Execution Time Data Transferred Optimization Technique
Denodo 9 sec. 4 M Aggregation push-down
Others 125 sec. 302 M None: full scan
300 M 2 M
Sales Material Customers
join
group by
2 M
2 M
Customers
join
group by id
group by
customer
To maximize push
down to the EDW
the aggregation is
split in 2 steps:
• 1st by customer_id
• 2nd by name
This significantly
reduces network
Traffic and processing
In Denodo
Sales Material

Access & Consumption

49
How to access the Denodo data model?
SQL Based access
▪ JDBC, ODBC and ADO.NET
• Integration with reporting tools: Tableau, MicroStrategy, PowerBI, BO,
Cognos, Looker, OBIEE, etc.
• Custom built applications
Web Services
▪ Multiple formats
• RESTful
• SOAP
• OData 4.0
• GraphQL
▪ Compliance with modern standards: OAuth, JWT, SAML, OpenAPI
Denodo’s Data Catalog
▪ Web-based tool for exploration and discovery by business users

Denodo Data Catalog

51
The Role of Denodo’s Data Catalog
Catalog of views and web services
▪ Browse and search for existing views and services
▪ See descriptions, relationships and data lineage
Preview and find data
▪ Quick look at data
▪ Search based on content
Consume
▪ Customize existing views for particular needs
▪ “My queries” for personal use & share with other users
▪ Export to local file
▪ Propose new standard business / canonical views

Governance

Security

54
Overview
Security in Denodo
Authentication
• Pass-through authentication
• Service accounts
Authentication
• User/password
• Kerberos and Windows SSO
• Web Service security: SAML, OAuth, SPNEGO
LDAP
Active Directory
Role based Authentication
Guest, employee, corporate
Schema-wide Permissions
Data Specific Permissions
(Row, Column level, Masking)
Policy Based Security
Data in motion
• TLSv1.2
Data in motion
• TLS v1.2
Encrypted
data at rest
• Cache
• Swap

55
Assigning Privileges to Role

56
Assigning Column Privileges

57
Assigning Restrictions

58
Key Takeaways
Conclusion
Source Abstraction
• Hides complexity for ease of data access by business.
Semantic Data Modeling
• Business Entities and pre-aggregated views and reports.
Flexible Publication Options
• Multiple options that adapt to the needs of the consumer.
Development and Operations
• Simplifies data security, privacy and audit
Enable self-service
• Simplifies data exploration and ability to handle metadata

Q&A

Fernando Sancén Amanda Lleyda Iván Torres López
Director & Co-Founder
Enki
Partner Channel & Sales
Denodo
Sales Engineer
Denodo
www.denodo.com
info.la@denodo.com
(+34) 912 77 58 55
www.enki.mx
CONTACTO@ENKI.MX
(+52) 598 517 82
¡Gracias por su participación!