Planning a Strategy for Autonomous Analytics and Data Warehousing

T: +44 01273 041134 (UK) W: https;//mjr-analytics.com E: info@mjr-analytics.com
Planning a Strategy for Autonomous
Analytics & Data Warehouse Cloud
Mark Rittman, CEO, MJR Analytics
UK Oracle User Group Tech’18, Liverpool ACC

© MJR Analytics 2018, T: +44 01273 041134 (UK) 415-218-2161 (US) W: https;//mjr-analytics.com E: info@mjr-analytics.com
Introductions …. And It’s Good To Be Back..!
● Mark Rittman, Oracle ACE Director
○ Past UKOUG Oracle Scene Editor
○ Author of two books on Oracle BI
○ 18+ Years in Oracle BI, DW, ETL + Big Data
○ Host of Drill to Detail Podcast
● Past two years as Product Manager at Tech Startup
● Now - back again as founder of MJR Analytics
○ Specialists in Modern Cloud & Digital Analytics
○ 100% Cloud focus + project delivery
■ Oracle Analytics Cloud
■ Oracle Autonomous DW Cloud
■ Oracle Data Integration Cloud
■ Oracle Big Data Cloud
■ Speak to us during UKOUG Tech 2018

Take the Next Step with MJR Analytics
● Specialists in Modern Cloud Analytics
● Founded by Mark Rittman in 2018
● 100% Cloud focus + project delivery
○ Oracle Autonomous Analytics Cloud
○ Oracle Autonomous DW Cloud
○ Oracle Data Integration Cloud
○ Oracle Big Data Cloud
● Speak to us now during OOW 2018
info@mjr-analytics.com
+44 7866 568246
https://www.mjr-analytics.com
MJR Analytics & Red Pill
Analytics Tech’18 Happy Hour
4pm-6pm today, Pump House

What Is Autonomous Analytics Cloud?

● Oracle Analytics Cloud with Oracle-managed infrastructure
● Runs on next-gen Oracle Cloud Infrastructure (OCI)
○ Simpler setup and automated install
○ Automated management, patching, backups, upgrades
○ Customer responsible for metadata, reports, dashboards
○ Part of the wider “Autonomous” product initiative
○ Oracle Autonomous Data Warehouse
○ Oracle Data Integration Platform
What Is “Autonomous Analytics Cloud”?
+
● A number of new and enhanced ML-driven features
○ Automated insights, visualization, and narration
○ Automated data discovery and data preparation
○ Proactive and personalized insights

© MJR Analytics 2018, T: +44 01273 041134 (UK) W: https;//mjr-analytics.com E: info@mjr-analytics.com© MJR Analytics 2018, T: +44 01273 041134 (UK) W: https;//mjr-analytics.com E: info@mjr-analytics.com
Customer Managed
● Oracle Cloud Infrastructure Classic
● You manage the service lifecycle and
configuration, and have SSH access to the
compute node VM
● You create and manage RCU DB
● You can scale cluster up-and-down
● Full access to APIs for DevOps
● You configure the network, firewall, l/b
● You choose schedule (and do the work) for
service setup, backups, upgrade patches,
monitoring of both OAC and RCU DB
Autonomous
● Oracle Cloud Infrastructure
● Oracle provides you with lifecycle
management and configuration
● Oracle sets-up l/b and network
● No scale-up (or down)
● API limitations for DevOps
● Oracle handles service setup for
both OAC and RCU DB, then
patches, maintains and backups

Customer Managed
Autonomous
● For customers who need maximum control over config
● To integrate OAC tightly into corporate network
○ To keep control over when / if OAC is updated
○ To control when/how backups take place
○ To be able to scale-up and scale-down capacity
● For customers / developers needing full debug access
○ To have ability to SSH into OAC VM nodes
○ To have access to diagnostic logs
● For customers with skills/time/interest in configuring,
monitoring, maintaining and patching OAC in-house
● For customers who just want analytics-as-a-service
○ Are happy with OAC running securely on Public Cloud
○ Don’t mind when it’s updated as long as given notice
○ Are happy that at least someone is taking backups
○ And didn’t even know that it could scale-up or down
● For customers happy to use Oracle Support for issues
○ Good luck with that one, but then most of you think
turning off logging is a best practice, so there you go
● For customers who quite frankly have something better to
do with their time, like use analytics to solve problems

● To solve a problem
● To provide the information someone needs in order to do their real job
Why do we use Analytics tools?
“But I need to find a group of
customers who might respond
to my marketing campaign”
Calculate propensity
to churn and
predicted CLV
“Now I can get on and do
some marketing work”
“I want to run a
marketing campaign to
increase product sales”

which all sounds great in-theory

Analytics Is Hard Because of the Human Factor
● Until recently, the impact of analytics has been limited because it was human-driven and labor-
intensive, requiring specific skills to understand context and what action to then take

But Doing Analytics Is Hard
● Traditionally, however, analytics has been limited because it was human-driven and labor-intensive,
requiring specific skills.
Human input
to measure
and analyze
Human input
to interpret
And then someone needs
to action those insights

Human input to measure
and analyze
What data?
Which visualizations?
What KPIs and trends?
Human input to interpret
What is the trend and will it continue?
What does this tell me?
What is the wider business picture?
And then someone needs to action
those insights
What do I do now?
And will I get time to do it?

Enter … “Augmented Analytics”

Augmented as next big jump in analytics platforms

From Visual-Based Data Discovery to Augmented
Algorithmic and
automatic schema
detection, enrichment
and metadata Automatic pattern-
matching, natural language
querying

● Oracle-managed infrastructure and ML-powered maintenance
● A number of new and enhanced ML-driven features
○ Automated insights, visualization, and narration
○ Automated data discovery and data preparation
○ Proactive and personalized insights
● Augmenting human analytical capabilities
○ Empowering more users to uncover more insights faster
○ Proactive, personalized self-service analytics on any data
○ Revealing hidden patterns and performance drivers
through predictive insights
○ Providing context through natural-language explanations
Autonomous Augmented Analytics Cloud

Oracle Business Analytics
Oracle
Augmented
Analytics Cloud
Oracle Data
Visualization
Oracle Business
Intelligence

Augmented Context and Insights from ML
● Automatic visualization of insights and one-
click advanced analytics
● Ad hoc analysis and reporting that are fully
mobile, with an adaptive user experience that
adjusts the display depending on your
preferences and previous questions asked
● Automated model building and What-if
scenario modeling with sandboxing
that enables you to perform individual what-if
analyses

● Machine learning analyzes
and explains any attribute
● Automatically see what drives
your results

● Easily identify
and analyze key
segments of
behavior
● Augmented data
analysis added
into the BI user’s
workflow

● Automated anomaly
detection and narration

Semantic Profile and Type Discovery
● Easy self-service data preparation and
blending
● Deep data patterns profiling produces
a rich set recommendation
● ML driven enrichment and transform
○ Over 20 geographic and
demographic
Enrichments
○ Out of the box recognition of over
30 semantic types
○ Instant preview of data transforms

Natural Language Queries & Insights (Roadmap)
● Interprets semantic layer, user private data,
expression library and catalog artifacts
○ Voice-enabled
○ Fuzzy match, stemming,
natural language processing
○ Generates on-the-fly queries –
visualizations are auto-created while user
types
● Natural Language Insights Turn Data into
Plain Language (roadmap)
○ Automatically turn data elements into
written insight & narrative
○ Simple to use, just like any other chart type
○ Dynamic and fully automated
○ Ability to customize the narrative
○ Support for multiple languages

Oracle Autonomous Data Warehouse Cloud
● Fully-managed DW Platform-as-a-Service
● Based on Oracle 18c Exadata Database technology
● Near-instant provisioning
● Elastic scaling and pricing
● Simplified column-store table creation
● Automated provisioning, patching and upgrades
● Automated backups
● Includes data visualization + notebook apps

Elastic Costs based on Planned Workload
Black Friday
New Year Sales
Automatic provisioning
of additional nodes
+$$$
De-provisioning
of excess nodes
-$$$

How Data Marts
were built in 2001
How Data Marts are
Built Today
Provision
ADWC
Land Data
in Object
Store
Analyze via BI
Connector
Create simple
column-store
tables
Come back next
month when
we’ve sized the
tablespaces

Planning a Strategy for Autonomous Analytics
And Data Warehouse Clou

● Oracle takes care of the install, configuration, maintenance and upgrades of BI and DW tiers
● Any performance or maintenance tasks are raised on My Oracle Support as SRs
● All you need to resource is one or more Cloud Account Administrators
You Don’t Need BI Infrastructure + DBA Resource
Cloud Account Administrator
Creates
● Give Another User Permission to Set Up Oracle Autonomous
Analytics Cloud or Data Warehouse Cloud
● Create a Service
● Set Up Users and Application Roles
● Delete a Service
● Raise a Service Request with Oracle Support

But You Do Need to Load and Manage Your Data
● DBA work isn’t just backup and recovery, adding storage and creating tablespaces
● It’s also taking responsibility for your data - integrity, recovering erroneously deleted data etc
● Still need to source and load your data, catalog and govern that content over time
● Responsible for the integrity and cataloging of business data
● Works with ETL developers to define policies and cleansing rules
● Together with stakeholders, defines key business metrics and KPIs
● Performs the data management tasks traditionally done by DBA
Cloud Data Steward

Getting Started with Augmented Analytics
Pick the right problems to solve Start with a small list of specific business problems that cannot
be solved, or that are too time-consuming to solve, using
traditional BI and data discovery methods, and launch a pilot to
assess the viability of augmented analytics.
Start by confirming or challenging existing
findings
Use augmented analytics tools to confirm or challenge findings
surfaced by human interpretation of manual data discovery
exercises.
Use as first step in identifying patterns Use augmented analytics capabilities as the first step in
identifying patterns that can be further explored and presented
using traditional data discovery tools and techniques.
Vehicle for data governance and stewardship Use augmented machine learning-driven data preparation and
data cataloging capabilities to assist and empower the Data
Steward role (or help introduce it into the organization for the
first time)

Consider the Organizational and HR Impact
Shifting the Organization’s Analytic Maturity Augmented data discovery has the potential to shift
organizations' analytic maturity, as the performance of
rootcause analysis, predictive analysis and prescriptive analysis
will no longer rely exclusively on data scientists.
Data-Driven Operational Teams Augmented data discovery capabilities will be embedded in
front-line applications to optimize the actions of operational
workers.
MIS Pivots from Plumbing to Problem-Solving MIS teams and Analytics professionals need to pivot from
experts in infrastructure to solving problems with data - as
they’ve always wanted to do, and were doing before enterprise
BI
Recruiting and Data Literacy Investments Recruit people with analytics skills across all job functions, and
expand investments in companywide data literacy.

Further Reading
● The Rise of the Strategic DBA, Oracle Corporation white paper 2018 featuring Mark Rittman on
Oracle Autonomous Data Warehouse for agile data analytics
● Data Warehouse Like a Tech Startup with Oracle Autonomous Data Warehouse, Oracle Open
World 2018, San Francisco
● How A Big Business Can Use An Autonomous Database To Move Like A Startup, Forbes
Magazine October 2018
● How Autonomous Analytics Accelerates Business Insight - Oracle Blogs
● Oracle Autonomous Analytics Cloud product homepage on oracle.com
● Augmented Analytics Is the Future of Data and Analytics Gartner white paper, October 2017

Planning a Strategy for Autonomous
Analytics & Data Warehouse Cloud
Mark Rittman, CEO, MJR Analytics
UK Oracle User Group

Planning a Strategy for Autonomous Analytics and Data Warehousing

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