Predictive vs Prescriptive Analytics

Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 1
Unlock Potential
William McKnight
McKnight Consulting Group
Predictive vs Prescriptive
Analytics
@williammcknight

Looker Overview
Elena Rowell
Sr. Product Marketing Manager

1
https://emtemp.gcom.cloud/ngw/globalassets/en/information-technology/documents/trends/gartner-2019-cio-agenda-key-takeaways.pdf
Digital-fueled Growth is the Top
Investment Priority For Technology Leaders.1
Rebalance your technology portfolio toward digital transformation
Percent of respondents
increasing investment
Percent of respondents
decreasing investment
Cyber/information security 40%1%
Cloud services or solutions (Saas, Paa5, etc.) 33%2%
Core system improvements/transformation 31%10%
How to implement product-centric delivery by percentage of respondents
DigitalTransformation
Business Intelligence or data analytics solution 45%1%

1
https://www.forrester.com/report/InsightsDriven+Businesses+Set+The+Pace+For+Global+Growth/-/E-RES130848
Insights-driven business harness
and implement digital insights
strategically and at scale to drive growth
and create differentiating experiences,
products, and services.1
7x Faster growth than global GDP
30% Growth or more using advanced analytics in a transformational way
2.3x More likely to succeed during disruption

1 in 2
customers integrate
insights/experiences
beyond Looker
2000+
Customers
5000+
Developers
800+
Employees
Santa Cruz
San Francisco New YorkChicago
Boulder Tokyo
Dublin London
Empower
people with
the smarter
use of data

Technology Layers
Built on the cloud
strategy of your choice
In-database architecture
Semantic modeling layer
‘API-first’ extensibility

Unified Data Platform
Governed metrics | Best-in-class APIs | In-database | Git version-control | Security | Cloud
Fully custom application
to 1M merchants for
granular drillable
analytics at scale
Productize self-serve
analytics at scale
Best-in-class in-app
analytics to compete
upmarket and achieve
net new growth
Monetize your data
to drive new growth
Build better data products
Data Lake
Modernize business intelligence
Consolidated customer
data - from the web,
apps, print, and more -
for a 360-degree view of
customers
Deliver best-in-class
Business Intelligence
Expand customer base
with proactive
communications from
sales and customer
success
Tailor data experiences
for any department
Smarter customer
acquisition with dynamic
AI-powered
bid engine
Fully automate
optimization in real-time
Increase trust and
revenue by delivering a
more transparent
experience to their
clients
Align your company
behind data
Infuse workflows with data

©Looker2018.Confidential
“Data allows us to
better interact and
connect with our
customers.”
Kate Caputo
Senior Manager, Business Intelligence

Resources: https://info.looker.com/
Blog: https://looker.com/blog
Upcoming Events: https://looker.com/events
Request a personal demo: https://looker.com/demo
Email us: hello@looker.com
JOIN 2019: https://looker.com/events/join-2019
Thank You!
© 2019 Looker. All rights reserved. Confidential.

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 the
Year Finalist
Owner/consultant: Data strategy and implementation
consulting firm
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

Analytics is Moving

Analytics Have Evolved
• From Business Initiative to business imperative

• Business Intelligence
Rearview mirror showing what happened
• Predictive Analytics
Tells you what is going to happen
Real-time
Summaries of data
• Technology is different
• Questions are different
What is the difference between business
intelligence and predictive analytics?

Analytics
Formed from SUMMARIES of data
i.e., Customer Segmentation and Profit
Tied to Business Actions
Continual Re-evaluation
Adding Big Data!

The Future
Trusted knowledge of an accurate future
is undoubtedly the most useful knowledge
to have
That future is one that you would want to
intervene into and tune to your preference
Analytics is the deep systematic
examination of a company's information
Analytics are key to predicting the future

Analytics Examples
Number of customers in each customer state (optionally by product or
multiple products)
Average balance of customers by geo
Average start date in each customer lifetime value decile by geo and device
New Number of customers in each state
Propensity to churn by age band and device
Cost of acquisition by age and gender
Average session duration by cost of acquisition
Session duration differences between first and tenth session
Network with highest up time last month
Number of calls per session
Best performing ad network by day part in a geo, age band and device
And on and on and on and on….

Big data + analytics = big value
Personalized
recommendations
based on history
Best time to buy;
average fare by
airline, date &
market
Customized energy
management for
customers
Proactive health
insurance that
identifies at-risk
patients
Optimize the siting of
wind turbines by
mining larger volumes
of data
Analyzes data from
viral “listening posts”
to prevent pandemics
Custom auto
premiums based on
actual driving habits
via sensors

Commodity Purchasing Application Example
Streaming Data Solution
• Business relies upon a critical commodity.
• There are multiple suppliers of this commodity.
• The goal is to always buy from the optimal
supplier.
• Considerations
– Quality/condition of the
commodity
– Minimization of risk
– Supply and Demand
– Storage cost and availability
– Impact of weather on supply
Chain

Children’s Hospital Monitoring Premature
Infants in the ICU
• Correlating blood oxygenation with blood
pressure to predict “Baby crashing”
• Infection Prediction
– Monitoring heart rate variability with other
information to predict sepsis
– Up to 24 hours earlier
than experienced
ICU Nurses

Analytics in Action
Prescriptive Analytics

• More data, more predictions
• Go back in time
• No guarantee of 100% correct prediction (and
that’s OK!)
• Getting “a little better” can mean a lot to the
business
Las Vegas is built on “51%”
How much can predictive analytics truly
predict?

Prescriptive Analytics Topics
Real-Time Analytics
Artificial Intelligence
Data Architecture
Self-Service

Data Ready For Analysis
Value
Action Time
ValueLost
Analysis
Latency
Action
Time
Capture
Latency
Business Event
Taken
Decision
Latency
The Time-Value Curve
How does the business value change through time?
Richard Hackathorn, Bolder Technology, Inc.

AI Enhances Analytics
Artificial Intelligence is key to
Predicting the future
Intervening into that future
Deeper analytics
Self-service data discovery
Intelligent recommendation of new data
AI to cluster data (i.e., photo tagging)
17

Enhance in-car
navigation using
computer vision
Reduce cost of handling
misplaced items
improve call center
experiences with chatbots
Improve financial fraud
detection and reduce
costly false positives
Automate paper-based,
human-intensive
process and reduce
Document Verification
Predict flight delays
based on maintenance
records and past flights,
in order reduce cost
associated with delays
AI-Based Analytics in Action

Get Data Under Management
19
In a leveragable platform
In an appropriate platform
For the data
For the usage
Used effectively by multiple business
groups
High NFRs
Availability, performance, scalability, stability,
durability, secure
Granular capture
Data at data quality standard
As defined by Data Governance

Analytical Workflows
Analytical
database
(DW)
Source
Systems
Analytical
tools
“Capture all
data”
Extract, transform, load
“Capture analytic
structured data”
Explore data
Report and mine data
Data Lake

Self-Service: Four Key Objectives
Make it easy
to access
data
Make solutions
fast to deploy & easy
to manage
Make tools
easy to use
Make results
easy to consume
& enhance
Self-Service

Tools Fit For Self-Service Analytics
• They work with the heterogenous data stores necessary
today, both SQL and NoSQL
• They provide data virtualization functions for the many
distributed queries necessary
• They accept the results of and participate in data
governance
• They provide secure data access
• They provide collaboration functions that enhance the use
of data
• They can be up and running quickly and can pivot with
agility

Analytics Manager (mid-level maturity)
Analytics Strategy Analytics Architecture Analytics Modeling Analytics Processes Analytics Ethics
Data scientist on staff less
than 6 months.
Concerted efforts to plan the
analytics that will benefit the
company.
Basic understanding of
analytic architecture.
Data architecture is
satisfying non-analytic
demand adequately but
still imperfect and
misunderstood.
“Black box” models
where processes not
completely understood
and harbor bias.
Acceptance of unstable
input signals.
Analytic systems with
mixed signals make
improvement
cumbersome.
Models are dependent
on other models.
Models have prediction
bias.
Amateurish
development, where the
systems are not
developed by analytic
professionals and
unintended
consequences result.
Improving a model or
signal can degrade
other models.
No central knowledge
of all model usage.
Not considering
analytics ethics.

Analytics Operator (high-level maturity)
Analytics Strategy Analytics Architecture Analytics Modeling Analytics Processes Analytics Ethics
Multiple data scientists on
staff.
New team members brought
up to speed in weeks, not
quarters.
Analytics contributions to all
major projects is considered.
Central catalog to track all
models along their lifecycle.
Enterprise data is
cataloged, accessible, well-
performing and managed.
Hard to make manual
errors.
Logic within analytics is
transparent.
Model expansion in the
enterprise.
Output from analytics is
predictable and
consistent, with
auditable outcomes.
Models are
reproducible.
Unused and redundant
settings are detectable.
Access restrictions
applied to models.
Data is tested for model
applicability.
Easy to specify a
configuration as a small
change from a previous
configuration.
Analytic applications
monitored for
operational issues.
Production analytic flow
includes packaging,
deployment, serving and
monitoring.
Scoring runs on a
periodic basis.
Good faith attempts to
remove biased variables
from models.
Potential for malicious
use of analytics
considered in analytics
lifecycle.

Moving Forward with Prescriptive
Analytics
Advance these four high-value initiatives
1
2
3
4
Grow, retain and
satisfy customers
Increase operational
efficiency
Transform financial
processes
Manage risk, fraud &
regulatory
compliance
Examples:
• Churn management
• Social media sentiment analysis
• Propensity to buy/next best action
• Predictive maintenance
• Supply chain optimization
• Claims optimization
• Rolling plan, forecast and budget
• Financial close process automation
• Real-time dashboards
• Operational and financial risk
visibility
• Policy and compliance simplification
• Real-time fraud identification

Top 6 Considerations for Taking
Advantage of Prescriptive Analytics
1. Simplify a Data Environment that Includes Big
Data
2. Data Virtualization
3. Data Governance
4. Collaboration Functions
5. Shorten Time-to-Value
6. Self-Service

Challenges to Prescriptive Analytics
Requirements for success
• Access to diverse, massive-scale data
- Incorporation of non-relational data with relational data
- Direct access to full data sets, not limited to just samples
- Immediate access to fresh data without complex data pipelines
• Ability to apply diverse analytics at scale
- Analysis co-located with data
- Flexibility to apply diverse analysis to diverse data
- Access to broad variety of languages
• Enabling on-the-fly exploration and analysis
- Tools to accelerate development and testing
- Performance and scalability to support rapid, iterative analysis
- Enable easy reuse across multiple use cases

Unlock Potential
William McKnight
McKnight Consulting Group
Predictive vs Prescriptive
Analytics
@williammcknight

Predictive vs Prescriptive Analytics

More Related Content

What's hot

Similar to Predictive vs Prescriptive Analytics

More from DATAVERSITY

Recently uploaded

Predictive vs Prescriptive Analytics