SlideShare una empresa de Scribd logo
1 de 27
Descargar para leer sin conexión
What Happened of Note
in 1H 2020 in Enterprise
Advanced Analytics
Presented by: William McKnight
President, McKnight Consulting Group
williammcknight
www.mcknightcg.com
(214) 514-1444
#AdvAnalytics
William McKnight
President, McKnight Consulting Group
• Frequent keynote speaker and trainer internationally
• Consulted to Pfizer, Scotiabank, Fidelity, TD Ameritrade, Teva
Pharmaceuticals, Verizon, and many other Global 1000
companies
• Hundreds of articles, blogs and white papers in publication
• Focused on delivering business value and solving business
problems utilizing proven, streamlined approaches to
information management
• Former Database Engineer, Fortune 50 Information Technology
executive and Ernst&Young Entrepreneur of Year Finalist
• Owner/consultant: 2018 and 2017 Inc. 5000 strategy &
implementation consulting firm
• 30 years of information management and DBMS experience
2
McKnight Consulting Group Offerings
Strategy
Training
Strategy
 Trusted Advisor
 Action Plans
 Roadmaps
 Tool Selections
 Program Management
Training
 Classes
 Workshops
Implementation
 Data/Data Warehousing/Business
Intelligence/Analytics
 Master Data Management
 Governance/Quality
 Big Data
Implementation
3
COVID-19
4
Maslow’s Hierarchy of Needs
5
COVID-19
• Impacted worldwide operations
• Sudden, accelerated disruption
• No discontinuity event like this
• Impacts customer operations
• Impacts health & wellbeing
• Need to step back and plan
6
WFH Pros/Cons
• Lost personal touch
• Some feel more connected; meeting family, pets
• Life has slowed down, less “I don’t have time”
– Doing things never had time to do before: upgrades,
maintenance, processes, documentation, learning
• Working earlier, later
• Virtual hiring
• Remote conferences
7
COVID-19 impacts Data Protection
• Security concerns: people working in houses
• High-speed access issues
• Zoom…. Was great, then security concern
• Sharing confidential info
• Reconsidering tooling, balancing familiarity with
security
8
Preparing Offices for Return
• Different geographical situations
• Social distancing parameters
• Limited population, desks, stockpile sanitizer,
cleaning
• BUT distance is working. Surprise! Some % will
stay offsite
– Or multiple people to 1 seat arrangements
– Some projects done all remote
9
Keeping Focus
• Keep focus on how do the customers respond?
• Remove pressure from salesforces
• Help customers survive
• Resilient companies will come out ahead
10
Those Who Are Less Impacted
• Cloud-First
• Microservices-Based
• Data is a separate function
• Agile Development
• Master Data
11
Specific Events
12
Consortiums
• The COVID-19 High Performance Computing
Consortium
– Bringing together the Federal government, industry, and academic leaders to
provide access to the world’s most powerful high-performance computing
resources in support of COVID-19 research.
• Open Community
• 30+ Members
• 400+ Petaflops
• 100k+ Nodes
• 50+ Projects
Healthcare
• Microsoft + JAX labs for Healthcare AI
– Genomic medicine researchers at the laboratory
have been using artificial intelligence to help
manage the vast amount of research data needed
to power its precision oncology initiatives
• Virtual visits
• Tele-health
14
Cybersecurity
• Companies placed big bets on securing applications and
unmanaged IoT devices as well as risk and compliance in
the first half of 2020
• Amazon Web Services purchased cybersecurity software
company Sqrrl
– Advanced threat hunting capabilities were expected to align
well with Amazon GuardDuty
– Sqrrl analyzes big data to hunt cyberthreats, helping
companies identify and address them faster
– Utilizes linked data, machine learning, user and entity
behavior analysis, risk scoring, and big data technologies to
uncover malicious patterns and anomalies hidden within
security data sets
15
Transportation Technology
• Amazon acquires auto vendor Zoox
• Experts predict that Amazon will focus more on
integrating the technology into its distribution
network than building a fleet of cars.
16
In the Data Enterprise
17
Trends to Continue 2H20
• Graph Solutions
• Data Visualization
• Stream Processing
• Artificial Intelligence
18
Hot Projects
• Fraud Detection
• Supply Chain Optimization
• Preventive Maintenance
• Customer Churn
19
AI is disruptive
Data is the Foundation
Data’s New Highest Use is Training AI Algorithms
Data Lakes
• The Rise of the LakeHouse
• Explosion in Sensor-Based Time-Series Data and
Edge AI
• Leveraging Cloud Storage for Data Lakes
• Data Integration Automation
• Retaining structure in structured data
• Data quality additions
21
Realization that full BOB has a price
• Piecemeal architecture with a variety of tools from
a number of vendors
• If an organization desires to build their modern
data ecosystem using “best-of-breed” solutions,
the overwhelming challenges will be
interoperability, cost, and complexity—not to
mention time-to-value
– It is not all bad, as there are some interoperability
beacons of hope
• Understanding, predicting and managing costs is
difficult
• Complexity of the architecture
22
New Technology Stacks
23
Modern Platform Examples
Single Platform
example
Single Cloud –
Azure
Single Cloud –
AWS
Multi-vendor
example
Data Engineering CDP Data Hub Azure HDInsight
Amazon Elastic
Map Reduce
(EMR)
Qubole
Data Analytics
CDP Data
Warehouse
Azure Synapse Amazon Redshift Snowflake
Data Science
Cloudera Machine
Learning
Azure Machine
Learning
Amazon
SageMaker
Databricks
Data Catalog CDP Data Catalog
Azure Data
Catalog
AWS Glue Data
Catalog
Alation
Overarching
Workload
Management
CDP Workload
Manager
None None2 Not applicable
Data Movement
CDP Data
Replication
Azure Data
Factory (ADF)
AWS Glue or
Data Pipeline
Talend
Overarching
Deployment
CDP Management
Console
Azure Portal AWS Portal Not applicable1
Overarching Security CDP/SDX
Azure Active
Directory
Identity Access
Management
(IAM)
Not applicable1
24
2 A multi-vendor approach lacks a single overarching workload management, deployment, and security mechanisms. Each individual vendor
product will likely have its own means to deliver these features.
1 Not overarching—only individual applications have their own workload management features
MLOps
• MLOps applies DevOps principles to ML delivery
• The ML process primarily revolves around creating, training and deploying
models
• Once trained and validated, models are deployed into an architecture that
can deal with large quantities of (often streamed) data, to enable insights to
be derived
• Development of such models can benefit from an iterative approach, so the
domain can be better understood, and the models improved
• It also then needs a highly automated pipeline of tools, repositories to store
and keep track of models, code, data lineage and a target environment
which can be deployed into at speed
• The result is an ML-enabled application: MLOps requires data scientists to
work alongside developers, and can therefore be seen as an extension of
DevOps to encompass the data and models used for ML
25
Data Team Dynamics
• Business departments have clearly staked a claim in building their
architectures
– Still need dedicated technology professionals to do the work
– The notion of an "IT professional" is alive and well
– The reporting structure is more complicated than ever
• Acknowledgement of the need for data deployments to be near the
business unit in organization charts
• Strategists and implementors are seeing a reduction in the challenges
posed by internal grist and resistance to change
– Dependence on certain individuals is lessened with the cloud, and
many are declaring their organization unshackled from resistance
to progress
– Acceleration of acceptance and some challenging personnel
moments inside the data apparatus in organizations
26
Second Thursday of Every
Month, at 2:00 ET
Presented by: William McKnight
President, McKnight Consulting Group
www.mcknightcg.com (214) 514-1444
#AdvAnalytics

Más contenido relacionado

La actualidad más candente

DAS Slides: Building a Data Strategy – Practical Steps for Aligning with Busi...
DAS Slides: Building a Data Strategy – Practical Steps for Aligning with Busi...DAS Slides: Building a Data Strategy – Practical Steps for Aligning with Busi...
DAS Slides: Building a Data Strategy – Practical Steps for Aligning with Busi...DATAVERSITY
 
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
 
DAS Slides: Data Virtualization – Separating Myth from Reality
DAS Slides: Data Virtualization – Separating Myth from RealityDAS Slides: Data Virtualization – Separating Myth from Reality
DAS Slides: Data Virtualization – Separating Myth from RealityDATAVERSITY
 
DAS Slides: Data Architect vs. Data Engineer vs. Data Modeler
DAS Slides: Data Architect vs. Data Engineer vs. Data ModelerDAS Slides: Data Architect vs. Data Engineer vs. Data Modeler
DAS Slides: Data Architect vs. Data Engineer vs. Data ModelerDATAVERSITY
 
RWDG Slides: Operationalize Data Governance for Business Outcomes
RWDG Slides: Operationalize Data Governance for Business OutcomesRWDG Slides: Operationalize Data Governance for Business Outcomes
RWDG Slides: Operationalize Data Governance for Business OutcomesDATAVERSITY
 
Data Quality Strategies
Data Quality StrategiesData Quality Strategies
Data Quality StrategiesDATAVERSITY
 
The Value of Metadata
The Value of MetadataThe Value of Metadata
The Value of MetadataDATAVERSITY
 
ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...
ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...
ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...DATAVERSITY
 
Drive your business with predictive analytics
Drive your business with predictive analyticsDrive your business with predictive analytics
Drive your business with predictive analyticsThe Marketing Distillery
 
RWDG Slides: Data Governance and Three Levels of Metadata Management
RWDG Slides: Data Governance and Three Levels of Metadata ManagementRWDG Slides: Data Governance and Three Levels of Metadata Management
RWDG Slides: Data Governance and Three Levels of Metadata ManagementDATAVERSITY
 
Cloud and Analytics -- 2020 sparksummit
Cloud and Analytics -- 2020 sparksummitCloud and Analytics -- 2020 sparksummit
Cloud and Analytics -- 2020 sparksummitMing Yuan
 
Everybody is a Data Steward – Get Over It!
Everybody is a Data Steward – Get Over It!Everybody is a Data Steward – Get Over It!
Everybody is a Data Steward – Get Over It!DATAVERSITY
 
Do you know where your databases are?
Do you know where your databases are?Do you know where your databases are?
Do you know where your databases are?DATAVERSITY
 
The Key to Big Data Modeling: Collaboration
The Key to Big Data Modeling: CollaborationThe Key to Big Data Modeling: Collaboration
The Key to Big Data Modeling: CollaborationEmbarcadero Technologies
 
RWDG Slides: Metadata Governance for Catalogs, Glossaries, Dictionaries, and ...
RWDG Slides: Metadata Governance for Catalogs, Glossaries, Dictionaries, and ...RWDG Slides: Metadata Governance for Catalogs, Glossaries, Dictionaries, and ...
RWDG Slides: Metadata Governance for Catalogs, Glossaries, Dictionaries, and ...DATAVERSITY
 
RWDG Slides: How to Govern Data Lakes
RWDG Slides: How to Govern Data LakesRWDG Slides: How to Govern Data Lakes
RWDG Slides: How to Govern Data LakesDATAVERSITY
 
Do-It-Yourself (DIY) Data Governance Framework
Do-It-Yourself (DIY) Data Governance FrameworkDo-It-Yourself (DIY) Data Governance Framework
Do-It-Yourself (DIY) Data Governance FrameworkDATAVERSITY
 
NTXISSACSC3 - Why Enterprise Information Management is the Key to GRC by Mika...
NTXISSACSC3 - Why Enterprise Information Management is the Key to GRC by Mika...NTXISSACSC3 - Why Enterprise Information Management is the Key to GRC by Mika...
NTXISSACSC3 - Why Enterprise Information Management is the Key to GRC by Mika...North Texas Chapter of the ISSA
 
Business Value Metrics for Data Governance
Business Value Metrics for Data GovernanceBusiness Value Metrics for Data Governance
Business Value Metrics for Data GovernanceDATAVERSITY
 
RWDG Slides: Achieving Data Quality with Data Governance
RWDG Slides: Achieving Data Quality with Data GovernanceRWDG Slides: Achieving Data Quality with Data Governance
RWDG Slides: Achieving Data Quality with Data GovernanceDATAVERSITY
 

La actualidad más candente (20)

DAS Slides: Building a Data Strategy – Practical Steps for Aligning with Busi...
DAS Slides: Building a Data Strategy – Practical Steps for Aligning with Busi...DAS Slides: Building a Data Strategy – Practical Steps for Aligning with Busi...
DAS Slides: Building a Data Strategy – Practical Steps for Aligning with Busi...
 
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
 
DAS Slides: Data Virtualization – Separating Myth from Reality
DAS Slides: Data Virtualization – Separating Myth from RealityDAS Slides: Data Virtualization – Separating Myth from Reality
DAS Slides: Data Virtualization – Separating Myth from Reality
 
DAS Slides: Data Architect vs. Data Engineer vs. Data Modeler
DAS Slides: Data Architect vs. Data Engineer vs. Data ModelerDAS Slides: Data Architect vs. Data Engineer vs. Data Modeler
DAS Slides: Data Architect vs. Data Engineer vs. Data Modeler
 
RWDG Slides: Operationalize Data Governance for Business Outcomes
RWDG Slides: Operationalize Data Governance for Business OutcomesRWDG Slides: Operationalize Data Governance for Business Outcomes
RWDG Slides: Operationalize Data Governance for Business Outcomes
 
Data Quality Strategies
Data Quality StrategiesData Quality Strategies
Data Quality Strategies
 
The Value of Metadata
The Value of MetadataThe Value of Metadata
The Value of Metadata
 
ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...
ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...
ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...
 
Drive your business with predictive analytics
Drive your business with predictive analyticsDrive your business with predictive analytics
Drive your business with predictive analytics
 
RWDG Slides: Data Governance and Three Levels of Metadata Management
RWDG Slides: Data Governance and Three Levels of Metadata ManagementRWDG Slides: Data Governance and Three Levels of Metadata Management
RWDG Slides: Data Governance and Three Levels of Metadata Management
 
Cloud and Analytics -- 2020 sparksummit
Cloud and Analytics -- 2020 sparksummitCloud and Analytics -- 2020 sparksummit
Cloud and Analytics -- 2020 sparksummit
 
Everybody is a Data Steward – Get Over It!
Everybody is a Data Steward – Get Over It!Everybody is a Data Steward – Get Over It!
Everybody is a Data Steward – Get Over It!
 
Do you know where your databases are?
Do you know where your databases are?Do you know where your databases are?
Do you know where your databases are?
 
The Key to Big Data Modeling: Collaboration
The Key to Big Data Modeling: CollaborationThe Key to Big Data Modeling: Collaboration
The Key to Big Data Modeling: Collaboration
 
RWDG Slides: Metadata Governance for Catalogs, Glossaries, Dictionaries, and ...
RWDG Slides: Metadata Governance for Catalogs, Glossaries, Dictionaries, and ...RWDG Slides: Metadata Governance for Catalogs, Glossaries, Dictionaries, and ...
RWDG Slides: Metadata Governance for Catalogs, Glossaries, Dictionaries, and ...
 
RWDG Slides: How to Govern Data Lakes
RWDG Slides: How to Govern Data LakesRWDG Slides: How to Govern Data Lakes
RWDG Slides: How to Govern Data Lakes
 
Do-It-Yourself (DIY) Data Governance Framework
Do-It-Yourself (DIY) Data Governance FrameworkDo-It-Yourself (DIY) Data Governance Framework
Do-It-Yourself (DIY) Data Governance Framework
 
NTXISSACSC3 - Why Enterprise Information Management is the Key to GRC by Mika...
NTXISSACSC3 - Why Enterprise Information Management is the Key to GRC by Mika...NTXISSACSC3 - Why Enterprise Information Management is the Key to GRC by Mika...
NTXISSACSC3 - Why Enterprise Information Management is the Key to GRC by Mika...
 
Business Value Metrics for Data Governance
Business Value Metrics for Data GovernanceBusiness Value Metrics for Data Governance
Business Value Metrics for Data Governance
 
RWDG Slides: Achieving Data Quality with Data Governance
RWDG Slides: Achieving Data Quality with Data GovernanceRWDG Slides: Achieving Data Quality with Data Governance
RWDG Slides: Achieving Data Quality with Data Governance
 

Similar a ADV Slides: What Happened of Note in 1H 2020 in Enterprise Advanced Analytics

Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Precisely
 
When and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data ArchitectureWhen and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
 
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
 
How to Capitalize on Big Data with Oracle Analytics Cloud
How to Capitalize on Big Data with Oracle Analytics CloudHow to Capitalize on Big Data with Oracle Analytics Cloud
How to Capitalize on Big Data with Oracle Analytics CloudPerficient, Inc.
 
Webinar: The 5 Most Critical Things to Understand About Modern Data Integration
Webinar: The 5 Most Critical Things to Understand About Modern Data IntegrationWebinar: The 5 Most Critical Things to Understand About Modern Data Integration
Webinar: The 5 Most Critical Things to Understand About Modern Data IntegrationSnapLogic
 
Top Trends and Challenges in the Cloud
Top Trends and Challenges in the CloudTop Trends and Challenges in the Cloud
Top Trends and Challenges in the CloudPrecisely
 
Big Data and Semantic Web in Manufacturing
Big Data and Semantic Web in ManufacturingBig Data and Semantic Web in Manufacturing
Big Data and Semantic Web in ManufacturingNitesh Khilwani
 
2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics2023 Trends in Enterprise Analytics
2023 Trends in Enterprise AnalyticsDATAVERSITY
 
Accelerate Cloud Migrations and Architecture with Data Virtualization
Accelerate Cloud Migrations and Architecture with Data VirtualizationAccelerate Cloud Migrations and Architecture with Data Virtualization
Accelerate Cloud Migrations and Architecture with Data VirtualizationDenodo
 
¿En qué se parece el Gobierno del Dato a un parque de atracciones?
¿En qué se parece el Gobierno del Dato a un parque de atracciones?¿En qué se parece el Gobierno del Dato a un parque de atracciones?
¿En qué se parece el Gobierno del Dato a un parque de atracciones?Denodo
 
Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...
Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...
Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...Precisely
 
Presented digital transformation 20181011 new technology expo matt allen
Presented  digital transformation 20181011 new technology expo matt allenPresented  digital transformation 20181011 new technology expo matt allen
Presented digital transformation 20181011 new technology expo matt allenmattallen59
 
Modernizing Integration with Data Virtualization
Modernizing Integration with Data VirtualizationModernizing Integration with Data Virtualization
Modernizing Integration with Data VirtualizationDenodo
 
¿Cómo las manufacturas están evolucionando hacia la Industria 4.0 con la virt...
¿Cómo las manufacturas están evolucionando hacia la Industria 4.0 con la virt...¿Cómo las manufacturas están evolucionando hacia la Industria 4.0 con la virt...
¿Cómo las manufacturas están evolucionando hacia la Industria 4.0 con la virt...Denodo
 
Neo4j GraphTalks - Einführung in Graphdatenbanken
Neo4j GraphTalks - Einführung in GraphdatenbankenNeo4j GraphTalks - Einführung in Graphdatenbanken
Neo4j GraphTalks - Einführung in GraphdatenbankenNeo4j
 
ICP for Data- Enterprise platform for AI, ML and Data Science
ICP for Data- Enterprise platform for AI, ML and Data ScienceICP for Data- Enterprise platform for AI, ML and Data Science
ICP for Data- Enterprise platform for AI, ML and Data ScienceKaran Sachdeva
 
DAS Slides: Cloud-Based Data Warehousing – What’s New and What Stays the Same
DAS Slides: Cloud-Based Data Warehousing – What’s New and What Stays the SameDAS Slides: Cloud-Based Data Warehousing – What’s New and What Stays the Same
DAS Slides: Cloud-Based Data Warehousing – What’s New and What Stays the SameDATAVERSITY
 
Modern Data Challenges require Modern Graph Technology
Modern Data Challenges require Modern Graph TechnologyModern Data Challenges require Modern Graph Technology
Modern Data Challenges require Modern Graph TechnologyNeo4j
 
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...Denodo
 

Similar a ADV Slides: What Happened of Note in 1H 2020 in Enterprise Advanced Analytics (20)

Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...
 
When and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data ArchitectureWhen and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data Architecture
 
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...
 
How to Capitalize on Big Data with Oracle Analytics Cloud
How to Capitalize on Big Data with Oracle Analytics CloudHow to Capitalize on Big Data with Oracle Analytics Cloud
How to Capitalize on Big Data with Oracle Analytics Cloud
 
Webinar: The 5 Most Critical Things to Understand About Modern Data Integration
Webinar: The 5 Most Critical Things to Understand About Modern Data IntegrationWebinar: The 5 Most Critical Things to Understand About Modern Data Integration
Webinar: The 5 Most Critical Things to Understand About Modern Data Integration
 
Manufactures whats keeping you up
Manufactures   whats keeping you upManufactures   whats keeping you up
Manufactures whats keeping you up
 
Top Trends and Challenges in the Cloud
Top Trends and Challenges in the CloudTop Trends and Challenges in the Cloud
Top Trends and Challenges in the Cloud
 
Big Data and Semantic Web in Manufacturing
Big Data and Semantic Web in ManufacturingBig Data and Semantic Web in Manufacturing
Big Data and Semantic Web in Manufacturing
 
2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics
 
Accelerate Cloud Migrations and Architecture with Data Virtualization
Accelerate Cloud Migrations and Architecture with Data VirtualizationAccelerate Cloud Migrations and Architecture with Data Virtualization
Accelerate Cloud Migrations and Architecture with Data Virtualization
 
¿En qué se parece el Gobierno del Dato a un parque de atracciones?
¿En qué se parece el Gobierno del Dato a un parque de atracciones?¿En qué se parece el Gobierno del Dato a un parque de atracciones?
¿En qué se parece el Gobierno del Dato a un parque de atracciones?
 
Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...
Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...
Introducing Trillium DQ for Big Data: Powerful Profiling and Data Quality for...
 
Presented digital transformation 20181011 new technology expo matt allen
Presented  digital transformation 20181011 new technology expo matt allenPresented  digital transformation 20181011 new technology expo matt allen
Presented digital transformation 20181011 new technology expo matt allen
 
Modernizing Integration with Data Virtualization
Modernizing Integration with Data VirtualizationModernizing Integration with Data Virtualization
Modernizing Integration with Data Virtualization
 
¿Cómo las manufacturas están evolucionando hacia la Industria 4.0 con la virt...
¿Cómo las manufacturas están evolucionando hacia la Industria 4.0 con la virt...¿Cómo las manufacturas están evolucionando hacia la Industria 4.0 con la virt...
¿Cómo las manufacturas están evolucionando hacia la Industria 4.0 con la virt...
 
Neo4j GraphTalks - Einführung in Graphdatenbanken
Neo4j GraphTalks - Einführung in GraphdatenbankenNeo4j GraphTalks - Einführung in Graphdatenbanken
Neo4j GraphTalks - Einführung in Graphdatenbanken
 
ICP for Data- Enterprise platform for AI, ML and Data Science
ICP for Data- Enterprise platform for AI, ML and Data ScienceICP for Data- Enterprise platform for AI, ML and Data Science
ICP for Data- Enterprise platform for AI, ML and Data Science
 
DAS Slides: Cloud-Based Data Warehousing – What’s New and What Stays the Same
DAS Slides: Cloud-Based Data Warehousing – What’s New and What Stays the SameDAS Slides: Cloud-Based Data Warehousing – What’s New and What Stays the Same
DAS Slides: Cloud-Based Data Warehousing – What’s New and What Stays the Same
 
Modern Data Challenges require Modern Graph Technology
Modern Data Challenges require Modern Graph TechnologyModern Data Challenges require Modern Graph Technology
Modern Data Challenges require Modern Graph Technology
 
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...
 

Más de DATAVERSITY

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
 
Data at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceData at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
 
Exploring Levels of Data Literacy
Exploring Levels of Data LiteracyExploring Levels of Data Literacy
Exploring Levels of Data LiteracyDATAVERSITY
 
Building a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsBuilding a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
 
Make Data Work for You
Make Data Work for YouMake Data Work for You
Make Data Work for YouDATAVERSITY
 
Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
 
Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
 
Data Modeling Fundamentals
Data Modeling FundamentalsData Modeling Fundamentals
Data Modeling FundamentalsDATAVERSITY
 
Showing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectShowing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectDATAVERSITY
 
How a Semantic Layer Makes Data Mesh Work at Scale
How a Semantic Layer Makes  Data Mesh Work at ScaleHow a Semantic Layer Makes  Data Mesh Work at Scale
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
 
Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?DATAVERSITY
 
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
 
Data Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsData Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
 
Data Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayData Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
 
Data Strategy Best Practices
Data Strategy Best PracticesData Strategy Best Practices
Data Strategy Best PracticesDATAVERSITY
 
Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?DATAVERSITY
 
Data Management Best Practices
Data Management Best PracticesData Management Best Practices
Data Management Best PracticesDATAVERSITY
 
MLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageMLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
 
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
 

Más de DATAVERSITY (20)

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
 
Data at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceData at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and Governance
 
Exploring Levels of Data Literacy
Exploring Levels of Data LiteracyExploring Levels of Data Literacy
Exploring Levels of Data Literacy
 
Building a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsBuilding a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business Goals
 
Make Data Work for You
Make Data Work for YouMake Data Work for You
Make Data Work for You
 
Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?
 
Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?
 
Data Modeling Fundamentals
Data Modeling FundamentalsData Modeling Fundamentals
Data Modeling Fundamentals
 
Showing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectShowing ROI for Your Analytic Project
Showing ROI for Your Analytic Project
 
How a Semantic Layer Makes Data Mesh Work at Scale
How a Semantic Layer Makes  Data Mesh Work at ScaleHow a Semantic Layer Makes  Data Mesh Work at Scale
How a Semantic Layer Makes Data Mesh Work at Scale
 
Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?
 
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?
 
Data Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsData Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and Forwards
 
Data Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayData Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement Today
 
Data Strategy Best Practices
Data Strategy Best PracticesData Strategy Best Practices
Data Strategy Best Practices
 
Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?
 
Data Management Best Practices
Data Management Best PracticesData Management Best Practices
Data Management Best Practices
 
MLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageMLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive Advantage
 
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...
 

Último

Aspirational Block Program Block Syaldey District - Almora
Aspirational Block Program Block Syaldey District - AlmoraAspirational Block Program Block Syaldey District - Almora
Aspirational Block Program Block Syaldey District - AlmoraGovindSinghDasila
 
如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样
如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样
如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样wsppdmt
 
7. Epi of Chronic respiratory diseases.ppt
7. Epi of Chronic respiratory diseases.ppt7. Epi of Chronic respiratory diseases.ppt
7. Epi of Chronic respiratory diseases.pptibrahimabdi22
 
Top profile Call Girls In Begusarai [ 7014168258 ] Call Me For Genuine Models...
Top profile Call Girls In Begusarai [ 7014168258 ] Call Me For Genuine Models...Top profile Call Girls In Begusarai [ 7014168258 ] Call Me For Genuine Models...
Top profile Call Girls In Begusarai [ 7014168258 ] Call Me For Genuine Models...nirzagarg
 
Vadodara 💋 Call Girl 7737669865 Call Girls in Vadodara Escort service book now
Vadodara 💋 Call Girl 7737669865 Call Girls in Vadodara Escort service book nowVadodara 💋 Call Girl 7737669865 Call Girls in Vadodara Escort service book now
Vadodara 💋 Call Girl 7737669865 Call Girls in Vadodara Escort service book nowgargpaaro
 
Reconciling Conflicting Data Curation Actions: Transparency Through Argument...
Reconciling Conflicting Data Curation Actions:  Transparency Through Argument...Reconciling Conflicting Data Curation Actions:  Transparency Through Argument...
Reconciling Conflicting Data Curation Actions: Transparency Through Argument...Bertram Ludäscher
 
Computer science Sql cheat sheet.pdf.pdf
Computer science Sql cheat sheet.pdf.pdfComputer science Sql cheat sheet.pdf.pdf
Computer science Sql cheat sheet.pdf.pdfSayantanBiswas37
 
Digital Transformation Playbook by Graham Ware
Digital Transformation Playbook by Graham WareDigital Transformation Playbook by Graham Ware
Digital Transformation Playbook by Graham WareGraham Ware
 
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...Valters Lauzums
 
Gulbai Tekra * Cheap Call Girls In Ahmedabad Phone No 8005736733 Elite Escort...
Gulbai Tekra * Cheap Call Girls In Ahmedabad Phone No 8005736733 Elite Escort...Gulbai Tekra * Cheap Call Girls In Ahmedabad Phone No 8005736733 Elite Escort...
Gulbai Tekra * Cheap Call Girls In Ahmedabad Phone No 8005736733 Elite Escort...gragchanchal546
 
Top profile Call Girls In Chandrapur [ 7014168258 ] Call Me For Genuine Model...
Top profile Call Girls In Chandrapur [ 7014168258 ] Call Me For Genuine Model...Top profile Call Girls In Chandrapur [ 7014168258 ] Call Me For Genuine Model...
Top profile Call Girls In Chandrapur [ 7014168258 ] Call Me For Genuine Model...gajnagarg
 
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...Klinik kandungan
 
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK
DATA SUMMIT 24  Building Real-Time Pipelines With FLaNKDATA SUMMIT 24  Building Real-Time Pipelines With FLaNK
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNKTimothy Spann
 
怎样办理圣地亚哥州立大学毕业证(SDSU毕业证书)成绩单学校原版复制
怎样办理圣地亚哥州立大学毕业证(SDSU毕业证书)成绩单学校原版复制怎样办理圣地亚哥州立大学毕业证(SDSU毕业证书)成绩单学校原版复制
怎样办理圣地亚哥州立大学毕业证(SDSU毕业证书)成绩单学校原版复制vexqp
 
Top profile Call Girls In dimapur [ 7014168258 ] Call Me For Genuine Models W...
Top profile Call Girls In dimapur [ 7014168258 ] Call Me For Genuine Models W...Top profile Call Girls In dimapur [ 7014168258 ] Call Me For Genuine Models W...
Top profile Call Girls In dimapur [ 7014168258 ] Call Me For Genuine Models W...gajnagarg
 
Top profile Call Girls In Indore [ 7014168258 ] Call Me For Genuine Models We...
Top profile Call Girls In Indore [ 7014168258 ] Call Me For Genuine Models We...Top profile Call Girls In Indore [ 7014168258 ] Call Me For Genuine Models We...
Top profile Call Girls In Indore [ 7014168258 ] Call Me For Genuine Models We...gajnagarg
 
Top profile Call Girls In Vadodara [ 7014168258 ] Call Me For Genuine Models ...
Top profile Call Girls In Vadodara [ 7014168258 ] Call Me For Genuine Models ...Top profile Call Girls In Vadodara [ 7014168258 ] Call Me For Genuine Models ...
Top profile Call Girls In Vadodara [ 7014168258 ] Call Me For Genuine Models ...gajnagarg
 
Top Call Girls in Balaghat 9332606886Call Girls Advance Cash On Delivery Ser...
Top Call Girls in Balaghat  9332606886Call Girls Advance Cash On Delivery Ser...Top Call Girls in Balaghat  9332606886Call Girls Advance Cash On Delivery Ser...
Top Call Girls in Balaghat 9332606886Call Girls Advance Cash On Delivery Ser...kumargunjan9515
 
Sonagachi * best call girls in Kolkata | ₹,9500 Pay Cash 8005736733 Free Home...
Sonagachi * best call girls in Kolkata | ₹,9500 Pay Cash 8005736733 Free Home...Sonagachi * best call girls in Kolkata | ₹,9500 Pay Cash 8005736733 Free Home...
Sonagachi * best call girls in Kolkata | ₹,9500 Pay Cash 8005736733 Free Home...HyderabadDolls
 

Último (20)

Aspirational Block Program Block Syaldey District - Almora
Aspirational Block Program Block Syaldey District - AlmoraAspirational Block Program Block Syaldey District - Almora
Aspirational Block Program Block Syaldey District - Almora
 
如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样
如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样
如何办理英国诺森比亚大学毕业证(NU毕业证书)成绩单原件一模一样
 
7. Epi of Chronic respiratory diseases.ppt
7. Epi of Chronic respiratory diseases.ppt7. Epi of Chronic respiratory diseases.ppt
7. Epi of Chronic respiratory diseases.ppt
 
Top profile Call Girls In Begusarai [ 7014168258 ] Call Me For Genuine Models...
Top profile Call Girls In Begusarai [ 7014168258 ] Call Me For Genuine Models...Top profile Call Girls In Begusarai [ 7014168258 ] Call Me For Genuine Models...
Top profile Call Girls In Begusarai [ 7014168258 ] Call Me For Genuine Models...
 
Vadodara 💋 Call Girl 7737669865 Call Girls in Vadodara Escort service book now
Vadodara 💋 Call Girl 7737669865 Call Girls in Vadodara Escort service book nowVadodara 💋 Call Girl 7737669865 Call Girls in Vadodara Escort service book now
Vadodara 💋 Call Girl 7737669865 Call Girls in Vadodara Escort service book now
 
Reconciling Conflicting Data Curation Actions: Transparency Through Argument...
Reconciling Conflicting Data Curation Actions:  Transparency Through Argument...Reconciling Conflicting Data Curation Actions:  Transparency Through Argument...
Reconciling Conflicting Data Curation Actions: Transparency Through Argument...
 
Computer science Sql cheat sheet.pdf.pdf
Computer science Sql cheat sheet.pdf.pdfComputer science Sql cheat sheet.pdf.pdf
Computer science Sql cheat sheet.pdf.pdf
 
Digital Transformation Playbook by Graham Ware
Digital Transformation Playbook by Graham WareDigital Transformation Playbook by Graham Ware
Digital Transformation Playbook by Graham Ware
 
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...
 
Gulbai Tekra * Cheap Call Girls In Ahmedabad Phone No 8005736733 Elite Escort...
Gulbai Tekra * Cheap Call Girls In Ahmedabad Phone No 8005736733 Elite Escort...Gulbai Tekra * Cheap Call Girls In Ahmedabad Phone No 8005736733 Elite Escort...
Gulbai Tekra * Cheap Call Girls In Ahmedabad Phone No 8005736733 Elite Escort...
 
Top profile Call Girls In Chandrapur [ 7014168258 ] Call Me For Genuine Model...
Top profile Call Girls In Chandrapur [ 7014168258 ] Call Me For Genuine Model...Top profile Call Girls In Chandrapur [ 7014168258 ] Call Me For Genuine Model...
Top profile Call Girls In Chandrapur [ 7014168258 ] Call Me For Genuine Model...
 
Abortion pills in Jeddah | +966572737505 | Get Cytotec
Abortion pills in Jeddah | +966572737505 | Get CytotecAbortion pills in Jeddah | +966572737505 | Get Cytotec
Abortion pills in Jeddah | +966572737505 | Get Cytotec
 
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...
 
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK
DATA SUMMIT 24  Building Real-Time Pipelines With FLaNKDATA SUMMIT 24  Building Real-Time Pipelines With FLaNK
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK
 
怎样办理圣地亚哥州立大学毕业证(SDSU毕业证书)成绩单学校原版复制
怎样办理圣地亚哥州立大学毕业证(SDSU毕业证书)成绩单学校原版复制怎样办理圣地亚哥州立大学毕业证(SDSU毕业证书)成绩单学校原版复制
怎样办理圣地亚哥州立大学毕业证(SDSU毕业证书)成绩单学校原版复制
 
Top profile Call Girls In dimapur [ 7014168258 ] Call Me For Genuine Models W...
Top profile Call Girls In dimapur [ 7014168258 ] Call Me For Genuine Models W...Top profile Call Girls In dimapur [ 7014168258 ] Call Me For Genuine Models W...
Top profile Call Girls In dimapur [ 7014168258 ] Call Me For Genuine Models W...
 
Top profile Call Girls In Indore [ 7014168258 ] Call Me For Genuine Models We...
Top profile Call Girls In Indore [ 7014168258 ] Call Me For Genuine Models We...Top profile Call Girls In Indore [ 7014168258 ] Call Me For Genuine Models We...
Top profile Call Girls In Indore [ 7014168258 ] Call Me For Genuine Models We...
 
Top profile Call Girls In Vadodara [ 7014168258 ] Call Me For Genuine Models ...
Top profile Call Girls In Vadodara [ 7014168258 ] Call Me For Genuine Models ...Top profile Call Girls In Vadodara [ 7014168258 ] Call Me For Genuine Models ...
Top profile Call Girls In Vadodara [ 7014168258 ] Call Me For Genuine Models ...
 
Top Call Girls in Balaghat 9332606886Call Girls Advance Cash On Delivery Ser...
Top Call Girls in Balaghat  9332606886Call Girls Advance Cash On Delivery Ser...Top Call Girls in Balaghat  9332606886Call Girls Advance Cash On Delivery Ser...
Top Call Girls in Balaghat 9332606886Call Girls Advance Cash On Delivery Ser...
 
Sonagachi * best call girls in Kolkata | ₹,9500 Pay Cash 8005736733 Free Home...
Sonagachi * best call girls in Kolkata | ₹,9500 Pay Cash 8005736733 Free Home...Sonagachi * best call girls in Kolkata | ₹,9500 Pay Cash 8005736733 Free Home...
Sonagachi * best call girls in Kolkata | ₹,9500 Pay Cash 8005736733 Free Home...
 

ADV Slides: What Happened of Note in 1H 2020 in Enterprise Advanced Analytics

  • 1. What Happened of Note in 1H 2020 in Enterprise Advanced Analytics Presented by: William McKnight President, McKnight Consulting Group williammcknight www.mcknightcg.com (214) 514-1444 #AdvAnalytics
  • 2. William McKnight President, McKnight Consulting Group • Frequent keynote speaker and trainer internationally • Consulted to Pfizer, Scotiabank, Fidelity, TD Ameritrade, Teva Pharmaceuticals, Verizon, and many other Global 1000 companies • Hundreds of articles, blogs and white papers in publication • Focused on delivering business value and solving business problems utilizing proven, streamlined approaches to information management • Former Database Engineer, Fortune 50 Information Technology executive and Ernst&Young Entrepreneur of Year Finalist • Owner/consultant: 2018 and 2017 Inc. 5000 strategy & implementation consulting firm • 30 years of information management and DBMS experience 2
  • 3. McKnight Consulting Group Offerings Strategy Training Strategy  Trusted Advisor  Action Plans  Roadmaps  Tool Selections  Program Management Training  Classes  Workshops Implementation  Data/Data Warehousing/Business Intelligence/Analytics  Master Data Management  Governance/Quality  Big Data Implementation 3
  • 6. COVID-19 • Impacted worldwide operations • Sudden, accelerated disruption • No discontinuity event like this • Impacts customer operations • Impacts health & wellbeing • Need to step back and plan 6
  • 7. WFH Pros/Cons • Lost personal touch • Some feel more connected; meeting family, pets • Life has slowed down, less “I don’t have time” – Doing things never had time to do before: upgrades, maintenance, processes, documentation, learning • Working earlier, later • Virtual hiring • Remote conferences 7
  • 8. COVID-19 impacts Data Protection • Security concerns: people working in houses • High-speed access issues • Zoom…. Was great, then security concern • Sharing confidential info • Reconsidering tooling, balancing familiarity with security 8
  • 9. Preparing Offices for Return • Different geographical situations • Social distancing parameters • Limited population, desks, stockpile sanitizer, cleaning • BUT distance is working. Surprise! Some % will stay offsite – Or multiple people to 1 seat arrangements – Some projects done all remote 9
  • 10. Keeping Focus • Keep focus on how do the customers respond? • Remove pressure from salesforces • Help customers survive • Resilient companies will come out ahead 10
  • 11. Those Who Are Less Impacted • Cloud-First • Microservices-Based • Data is a separate function • Agile Development • Master Data 11
  • 13. Consortiums • The COVID-19 High Performance Computing Consortium – Bringing together the Federal government, industry, and academic leaders to provide access to the world’s most powerful high-performance computing resources in support of COVID-19 research. • Open Community • 30+ Members • 400+ Petaflops • 100k+ Nodes • 50+ Projects
  • 14. Healthcare • Microsoft + JAX labs for Healthcare AI – Genomic medicine researchers at the laboratory have been using artificial intelligence to help manage the vast amount of research data needed to power its precision oncology initiatives • Virtual visits • Tele-health 14
  • 15. Cybersecurity • Companies placed big bets on securing applications and unmanaged IoT devices as well as risk and compliance in the first half of 2020 • Amazon Web Services purchased cybersecurity software company Sqrrl – Advanced threat hunting capabilities were expected to align well with Amazon GuardDuty – Sqrrl analyzes big data to hunt cyberthreats, helping companies identify and address them faster – Utilizes linked data, machine learning, user and entity behavior analysis, risk scoring, and big data technologies to uncover malicious patterns and anomalies hidden within security data sets 15
  • 16. Transportation Technology • Amazon acquires auto vendor Zoox • Experts predict that Amazon will focus more on integrating the technology into its distribution network than building a fleet of cars. 16
  • 17. In the Data Enterprise 17
  • 18. Trends to Continue 2H20 • Graph Solutions • Data Visualization • Stream Processing • Artificial Intelligence 18
  • 19. Hot Projects • Fraud Detection • Supply Chain Optimization • Preventive Maintenance • Customer Churn 19
  • 20. AI is disruptive Data is the Foundation Data’s New Highest Use is Training AI Algorithms
  • 21. Data Lakes • The Rise of the LakeHouse • Explosion in Sensor-Based Time-Series Data and Edge AI • Leveraging Cloud Storage for Data Lakes • Data Integration Automation • Retaining structure in structured data • Data quality additions 21
  • 22. Realization that full BOB has a price • Piecemeal architecture with a variety of tools from a number of vendors • If an organization desires to build their modern data ecosystem using “best-of-breed” solutions, the overwhelming challenges will be interoperability, cost, and complexity—not to mention time-to-value – It is not all bad, as there are some interoperability beacons of hope • Understanding, predicting and managing costs is difficult • Complexity of the architecture 22
  • 24. Modern Platform Examples Single Platform example Single Cloud – Azure Single Cloud – AWS Multi-vendor example Data Engineering CDP Data Hub Azure HDInsight Amazon Elastic Map Reduce (EMR) Qubole Data Analytics CDP Data Warehouse Azure Synapse Amazon Redshift Snowflake Data Science Cloudera Machine Learning Azure Machine Learning Amazon SageMaker Databricks Data Catalog CDP Data Catalog Azure Data Catalog AWS Glue Data Catalog Alation Overarching Workload Management CDP Workload Manager None None2 Not applicable Data Movement CDP Data Replication Azure Data Factory (ADF) AWS Glue or Data Pipeline Talend Overarching Deployment CDP Management Console Azure Portal AWS Portal Not applicable1 Overarching Security CDP/SDX Azure Active Directory Identity Access Management (IAM) Not applicable1 24 2 A multi-vendor approach lacks a single overarching workload management, deployment, and security mechanisms. Each individual vendor product will likely have its own means to deliver these features. 1 Not overarching—only individual applications have their own workload management features
  • 25. MLOps • MLOps applies DevOps principles to ML delivery • The ML process primarily revolves around creating, training and deploying models • Once trained and validated, models are deployed into an architecture that can deal with large quantities of (often streamed) data, to enable insights to be derived • Development of such models can benefit from an iterative approach, so the domain can be better understood, and the models improved • It also then needs a highly automated pipeline of tools, repositories to store and keep track of models, code, data lineage and a target environment which can be deployed into at speed • The result is an ML-enabled application: MLOps requires data scientists to work alongside developers, and can therefore be seen as an extension of DevOps to encompass the data and models used for ML 25
  • 26. Data Team Dynamics • Business departments have clearly staked a claim in building their architectures – Still need dedicated technology professionals to do the work – The notion of an "IT professional" is alive and well – The reporting structure is more complicated than ever • Acknowledgement of the need for data deployments to be near the business unit in organization charts • Strategists and implementors are seeing a reduction in the challenges posed by internal grist and resistance to change – Dependence on certain individuals is lessened with the cloud, and many are declaring their organization unshackled from resistance to progress – Acceleration of acceptance and some challenging personnel moments inside the data apparatus in organizations 26
  • 27. Second Thursday of Every Month, at 2:00 ET Presented by: William McKnight President, McKnight Consulting Group www.mcknightcg.com (214) 514-1444 #AdvAnalytics