Journey to
Cloud Analytics
How 3 Companies’ Analytics Challenges
Were Solved by Moving to the Cloud
Richmond Virtual CIO and IT Security Forum | May 25, 2021

T
Tom
Hoblitzell
Datavail
VP, Data
Management
Passion for solving complex global business challenges
through advanced technology and leading-edge digital
business intelligence
A forward-thinking strategist to drive outstanding
business results
Built and successfully grew analytics practices at major
systems integration firms leading growth from start-up to
mature practice with over $60 MM in revenue
Sold Practices as part of strategic acquisitions (Fujitsu,
Capgemini)
Acquired and integrated IT acquisitions into existing
practices to increase growth and round out capabilities
and competencies
Acts as a strategic advisor to key clients enabling growth
of analytics and digital transformation initiatives to
leverage data as a strategic asset
Over 30 years of experience
www.datavail.com 2

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A
Our
Agenda
Simply migrating to the
cloud with a “lift and
shift”' approach does not
result in innovation, but it
does add another level of
complexity to operations.
- Gartner
Why cloud for analytics?
New capabilities in the cloud
versus on prem
Challenges moving analytics
to the cloud
Maturity matters
Datavail’s approach
Players in the Cloud Analytics Space
Case Studies
Two major players to watch
• Snowflake
• RedShift
www.datavail.com 4

www.datavail.com 5
“IDC Forecasts Revenues for Big Data and Business Analytics Solutions Will Reach $189.1
Billion This Year(2019) with Double-Digit Annual Growth Through 2022 (to $274.3 billion)”
Companies are investing in Cloud Analytics
0
50
100
150
200
250
300
350
2019 2020 2021 2022
Billions
Year
Cloud Analytics Growth: 44% of Market in 2022
Cloud Analytics=32%
Annual Growth!
On-Prem=13%
Annual Growth

www.datavail.com 6
Why Cloud for Analytics?
The Cloud as a Driver of Analytics Innovation
Analytics Innovation
Brainstorming
Ideation Visibility
Constant
Refinement
Design
Thinking
Incubation
Focus
Source: Gartner

A
Cloud
Analytics
What Is It?
“Analytics is the process
of gathering, cleansing,
transforming, and
modeling data with the
goal of discovering useful
information to support
decision making.”
Source: Quantzig
The goal of Analytics is to
make data accessible,
useful, and actionable,
which leads to digital
transformation.
Cloud Analytics uses
modern cloud technologies
and approaches to achieve
the goal with lower costs,
faster scalability, and
agile implementation.
www.datavail.com 7

www.datavail.com 8
Analytics: New Capabilities in the Cloud vs on Prem –
Help Generate and Manage innovation
Source: Gartner
On-premises
Cloud
Attracting users
with emerging
capabilities
More prototypes
with greater
visibility
Metadata
powered
collaboration
Focus on high-
quality analytics
Desktop or web-
based training
Prototype with
limited audience
Discussion
Tired of
repetitive basic
analytics
Onboarding Prototype Pilot Production
Ideation Design Incubation Focus
Sandbox Constant refinement Elasticity/automation

www.datavail.com 9
Business needs self-service data
exploration and discovery-
oriented forms of advanced
analytics
Business needs data integrated into
a single, trusted data store
Want answers to any question
across business processes
Business wants both new and
traditional data, thereby enabling
analytics correlations across all data
Low total cost of ownership (TCO)
360° view of any person or
organization that touches the
company
Analytics systems should respond
quickly and cheaply to changes in
business conditions or acquisitions
Scalable
Fast response
What Organizations want from their Analytics

www.datavail.com 10
But Moving to the Cloud Can Be Challenging…
Top Internal Challenges Adopting Data & Analytics in the Cloud
Challenges with technology infrastructure
and/or architecture
Solving risk and governance issues (security,
ethics, privacy, data quality)
Adding more agility and flexibility to our data
and analytics initiatives
Integrating multiple data sources
Obtaining skills and capabilities needed
Making data and analytics more usable for
business consumers and front-line workers
0% 5% 10% 15% 20% 25% 30% 35%
33%
32%
29%
29%
27%
26%
n= 270, total respondents, excluding “don’t know” Source: Gartner

M
BI Maturity
Stages
Maturity is now critical
to company
competitiveness and
success.
www.datavail.com
Creating Market Agility and Differentiation
Fostering Innovation and People Productivity
Integrating Performance Management & the Business
Measuring and Monitoring the Business
Running the Business
1
2
3
4
5
11

A
Our
Approach
We provide a
consultative and advisory
service entrenched in
technology, people, and
process.
A fined-tuned service and flexible
solution with several successful
engagements under our belt
Solves a business problem (“pain
points”)
Includes Datavail developed
accelerators
Aims to be vendor software agnostic
Delivered as an “outcomes” based
solution with defined ‘Quick Wins’
www.datavail.com 12

Direct
Efforts With
A Focused
Business
Vision
Goal is to enable
Systems of Insight to
drive business value
and efficiencies.
3
2
1
Data Foundation
Approach to delivering on a well architected journey
for tools and framework to drive data integration
and analytics through an execution roadmap and
timeline in accordance with compliance.
Transform Information
Raise the bar on operational excellence and
corporate success by converting data into
actionable insights, along with ability to dynamically
adjust to reporting requirements and compliance.
Modern Analytics
Guided, actionable analytics, providing self-service,
distributed analytics, dashboards, and future-proof
scaling of data to information integration.
www.datavail.com 13

www.datavail.com 14
Players in the Cloud Analytics Space
ETL
MDM
Cloud
Providers
Database
Reporting/
Analytics
Cloud &
Big Data

www.datavail.com 15
Sample of our Analytics Clients

C
Case
Studies
From small business to
large enterprises, see
how we’ve helped our
clients gain value from
their organizational data.
Major Media Company
Retail Company
National Broadcasting
Company
www.datavail.com 16

C
Case
Studies
From small business to
large enterprises, see
how we’ve helped our
clients gain value from
their organizational data.
Major Media Company
Retail Company
National Broadcasting
Company
www.datavail.com 17

www.datavail.com 18
Challenge
Client’s IT Staff was dedicated to providing custom
reports based on client requirements that required two
to three dedicated resources.
Cost of Database Software license was becoming
prohibitive
Basic problem with the on-prem existing analytics
solution:
• Didn’t scale
• Costly (licenses and VM Servers)
• IT Bottleneck (required for each dataset developed)
• Dependence on Affinity ERP email capability (performance
and file-size limitations)
• Dependency on internal staff for report customization
Solution
Proposed solution was to take
advantage of the AWS Cloud
Analytics services.
Serverless solution reduced
cost (pay as you go)
Scaled easily
Provide Self Service data
visualization and data set
delivery
Automation of data movement
and processing
Case Study: Major Media Company

www.datavail.com 19
Media Company – New Architecture
SQLServer
DB OLTP
OLTP
1. Existing Data Source
bucket with
incremental
data
Stage - S3
2. Stage Data 3. Data Marts
RDS RDBMS
Amazon
RDS
4. Self Service
Analysis
Analysis
Lambda
function
Amazon
CloudWatch
AWS Data
Pipeline
AWS Glue
Amazon
QuickSight
Internal User
External User
7. REST API for
Data Integration
Data Integration Services
Business
Intelligence
bucket with
data sets
Data Set Delivery -S3
5. Build and Deliver
Data Sets
AWS Glue
6. Deliver Reports –
signed URL in email

C
Case
Studies
From small business to
large enterprises, see
how we’ve helped our
clients gain value from
their organizational data.
Major Media Company
Retail Company
National Broadcasting
Company
www.datavail.com 20

www.datavail.com 21
Challenge
Existing vendor solution was not
providing the reporting and analytics
environment required to manage the
business.
Technology was obsolete
Support was minimal “keep the lights on”
Needed to expand from B2B to include
B2C Sales and Operational Data
Expand to include additional data
sources
Solution
Determined that an AWS “Data Lake”
solution to bring both structured and
unstructured data into the Data Lake for
processing to drive analytics for the
business.
Utilized AWS Data Lab and POC to prove
solution addressed business needs
A support model was established so that
Datavail was in a Build/Run opportunity to
provide support for the new solution – from
data loads, to reporting, to governance and
managing the environment
Case Study: International Retail Company

www.datavail.com 22
Existing Business Environment
Agility for Today’s and Tomorrow’s Business Needs – Cloud
Flexibility and Speed - Time to Deliver Updates and Data Availability
Proactive Control of Data Quality

www.datavail.com 23
The Solution: Automated Data
Profiling/Reporting
Analysts
CSV or
Other
Files
On-Prem
S3 Bucket AWS
Athena
Data Catalog
Glue Crawler
Glue Crawler
Profiler Metrics
Repository
Data Profiler on
EMR

C
Case
Studies
From small business to
large enterprises, see
how we’ve helped our
clients gain value from
their organizational data.
Major Media Company
Retail Company
National Broadcasting
Company
www.datavail.com 24

www.datavail.com 25
National
Broadcasting
Company
Challenges
Broadcasting Company has an existing data
warehouse that is not meeting the user’s needs and
they want to re-engineer this warehouse to meet the
functional and analytical requirements of the user
The existing DW has obscure field names which forces
all reporting requests to go through a Data Scientist vs.
enabling the user to create their own reports
External data is not integrated into the warehouse for
trend analysis or for other types of market analysis
Improving the frequency of digital advertising data will
improve and enhance fund raising campaigns and
pledge drives
The existing DW environment:
• SQL Server
• Tableau and Microsoft BI for reporting
• Alteryx as the ETL tool

www.datavail.com 26
Solution
Considerations
Improve the flexibility, scalability and overall capabilities of
the warehouse to support business reporting and
analytics while providing data to the data science team to
focus on analysis that is external to Broadcasting
Company
Improve and reduce the support structure to make the
solution easily supportable by the existing support team
including technical training, knowledge transfer, etc.
Protect PCI and PII data in a secure manner
Leverage the cloud to take advantage of potentially lower
costs assuming security can be maintained
Provide an approach to start with Broadcasting
Company’s Digital business while extending the solution
to other lines of business

www.datavail.com 27
A Modern Data Lake Architecture
INGEST MODEL ANALYZE REPORTING
STAGE & STORE
DATA SOURCES
Azure
Data
Factory
Azure Data Lake
Power BI
Service
Snowflake DB
SaaS
Other Data
Sources
Prayer Data
Streaming
Data
Web Site
Data Ad-hoc Reporting
and Analysis
Standard
Reporting

Snowflake/
RedShift

www.datavail.com 29
Snowflake
Snowflake’s Data Cloud is a Software-as-a-Service
(SaaS) data platform that enables data storage,
processing, and analytical solutions that are faster,
easier to use, and more flexible than traditional
analytics offerings
Snowflake combines a new SQL query engine with an
innovative architecture that is natively designed for the
cloud
Snowflake runs on the following cloud platforms:
• Azure, AWS, Google
Snowflake processes queries using MPP (Massively
Parallel Processing) compute clusters storing a portion
of the entire data set locally to offer data management
simplicity of a shared-disk architecture but with the
performance and scale-out benefits of a shared-noting
architecture
Snowflake stores data in a columnar format with the
data only accessible through SQL query operations
AWS Redshift
Redshift can be described as a fully-managed, cloud-ready
petabyte-scale data warehouse service that can be seamlessly
integrated with business intelligence (BI) tools.
An Amazon Redshift data warehouse is an enterprise-class
relational database query and management system.
Amazon Redshift integrates with various data loading and ETL
(extract, transform, and load) tools and business intelligence (BI)
reporting, data mining, and analytics tools. Amazon Redshift is
based on industry-standard PostgreSQL.
Amazon Redshift supports client connections with many types of
applications, including business intelligence (BI), reporting, data,
and analytics tools.
When you execute analytic queries, you are retrieving, comparing,
and evaluating large amounts of data in multiple-stage operations to
produce a final result.
Amazon Redshift achieves efficient storage and optimum query
performance through a combination of massively parallel
processing, columnar data storage, and very efficient, targeted data
compression encoding schemes.
Two Key Players to Watch and Learn From

www.datavail.com 30
AWS Data Hub – with Snowflake
ERP
On-Prem Data
Sources
Data Hub
BI Tool(s)
Data as a
Service (data
sets, 360
search, API,
Web apps,
predictive
models)
Information
Delivery:
Amazon Athena
AWS Glue
ETL
Amazon Elasticsearch
Service
Crawler
AWS Database Migration
Service
Data Lake (S3)
Data Catalog
Landing Tier
Analytics 2 Tier
Analytics 1 Tier
Machine Learning
Algorithms
Data Source
Model
Train Data
Test Data
- Csv data files
- Delta only (some full)
- Granular level data
- No transformations
- Parquet/ORC files
- Partitioned
- Coelescing Partitions
- Optimized for Analytics
- Domain Level
- Org by Use
Cases
- Optimized special
analysis
Views
Accommodat
e Updates
and Deletes
AWS Glue
ETL
AWS
Lambda
AWS
Lambda
Amazon SageMaker
Amazon EMR
Snowflake DB
SaaS

www.datavail.com 31
AWS Data Hub – with Redshift
ERP
On-Prem Data
Sources
Data Hub
BI Tool(s)
Data as a
Service (data
sets, 360
search, API,
Web apps,
predictive
models)
Information
Delivery:
Amazon Athena
AWS Glue
ETL
Amazon Elasticsearch
Service
Crawler
AWS Database Migration
Service
Data Lake (S3)
Data Catalog
Landing Tier
Analytics 2 Tier
Analytics 1 Tier
Machine Learning
Algorithms
Data Source
Model
Train Data
Test Data
- Csv data files
- Delta only (some full)
- Granular level data
- No transformations
- Parquet/ORC files
- Partitioned
- Coelescing Partitions
- Optimized for Analytics
- Domain Level
- Org by Use
Cases
- Optimized special
analysis
Views
Accommodat
e Updates
and Deletes
AWS Glue
ETL
AWS
Lambda
AWS
Lambda
Amazon SageMaker
Amazon EMR

What’s
Next?

www.datavail.com 33
Get a clear view of your cloud
strategy – and align
• Expected Benefits of moving to the cloud
• Cloud data strategy
• XaaS strategy
• Constraints
• Roadmap
Assess your current state
Use the cloud for experimentation
Set the right migration approach
based on your priorities
Put analytics wherever the data is
Utilize the power of the cloud to
scale
Use multiple clouds depending on
your purpose
Enable self-service analytics
Best Practices in moving to the Cloud

www.datavail.com 34
“As a Service” of cloud – pay as you
go instead of capital outlay
Increased scalability. Think about
your on-site IT infrastructure
Faster insights
Easier maintenance and disaster
recovery
Stronger decision making
Can start with a Small Project!
Cost-Savings
Agility
Scalability
Solves new analytics requirements
(Use-Cases)
Summary: Why Move Analytics to the Cloud?

Fill Out Our
Cloud Analytics
Survey
Fill out the Cloud Analytics survey for a chance
to WIN a Sonos Move, a battery-powered
Smart Speaker!
https://www.datavail.com/cloud-analytics-survey

Q&A