How to Take a First Step to Advanced Analytics

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How to Take a First Step toAdvanced Analytics

Lisa Kart

@Kart_Lisa

3 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

The First Step to Advanced Analytics Often Feels Like Crossing a Chasm

BIAdvanced Analytics


But It Doesn't Require Vast Resources

BIAdvanced Analytics

First Win

Evangelist

Ingenuity


1. What is advanced analytics and data science?

2. How do we take that first step?

3. What are the new skills and tools required?

Key Issues





Key Issues


All 4 Types of Analytics Capabilities Ultimately Use Data to Drive Insights and Better Decisions

Human Input

Data DecisionPredictive

What Will Happen?

Diagnostic

Why Did It Happen?

Descriptive

What Happened?

Prescriptive

What Should I Do?

Analysis

Action

Decision Automation

Decision Support


Advanced Analytics Uses Sophisticated Math to Give Deeper Insights to Improve Decisions

Sophisticated quantitative methods (e.g., data mining, prediction, simulation and optimization) to produce insights that traditional approaches to BI — such as query and reporting —are unlikely to discover.A

dv

an

ced

An

aly

tic

s

Human Input

Data DecisionPredictive

What Will Happen?

Diagnostic

Why Did It Happen?

Descriptive

What Happened?

Prescriptive

What Should I Do?

Analysis

Action

Decision Automation

Decision Support


Predictive Analytics Use Cases

... customer churn

... sales revenue

... equipment failure

…market volatility

... response to a marketing offer

... fraudulent claims

... cybersecurity breaches

... workforce attrition

... freight cost

… credit risk

Organizations want to predict …

Predictive

Models

Common Techniques:

Regression

Decision trees

Neural nets

Ensemble models


Prescriptive Analytics Use Cases

Organizations want to …

Decision

Models

Common Techniques:

Optimization

Linear programming

Constraint programming

Decision management

... assign optimal price

... find the best route

... schedule resources

... give customers the best next offer

... allocate limited resources

… optimize marketing mix

... acquire a company

… select a site for a power plant

… decide where to drill for oil


Build vs. Buy vs. Outsource

BUILD BUY

OUTSOURCE

via workbenches packaged apps

service providers

1. Requires in-house skills

2. Analytics is differentiator

3. Agility & control are key

1. Best ease-of-use

2. Often "good enough"

3. Best time-to-solution

4. Little differentiation

1. In-house skills not avail

2. Packaged apps not avail

3. Give up IP for sake of speed





Key Issues


Marketing HR

Risk

Lots of Data

Logistics

"Signals" in the Noise

Finance

CRM

Operational Tactical Strategic

Complex

Find the Low Hanging Fruit to Make Better Predictions and Decisions


• Large North American bank

• Improve credit line increases

• Hired a service provider to do POC as "gain share":

– Low upfront risk, cost

• Wildly successful

• Internal advanced analytics COE

You Can Operate in Stealth Mode


You Don't Need a Lot of Resources

• 100 year old manufacturing company

• 2 data science interns

• Exploration of the data

• Found 13 factors to drivelower cost and higherprocess efficiency


You Do Need to Convince Your Toughest Skeptics

• U.S. trucking & logistics company

• Optimizing routingand scheduling

• Dispatchers weren't bought into the solution

• "Dispatch game"

• "Dispatch Analysts"





Key Issues


From "Magic Quadrant for Advanced Analytics Platforms," 19 February 2015, (G00270612)

New Tools: Advanced Analytics Platforms Are Evolving


Data Discovery

Advanced Analytics

Feature Identification

Rigorous Trending and Clustering

Apply Patterns to the Future

Prescriptive Actions

Rapid and Intuitive Data Understanding

Hypotheses Generation

Business User Collaboration and Interpretation

SAS RapidMiner Alpine

Tableau Qlik Tibco Advizor Alteryx

BeyondCoreIBM

Data Discovery and Predictive Analytics OfferingsAre Complementary


The Rise of Citizen Data Scientists

Tools are getting easier to use

Expert data scientists will remain rare

IBM Watson AnalyticsMicrosoft Azure Machine Learning

SAS Visual Statistics SAP

Angoss

Alpine

Ayasdi

Emcien

BeyondCore

Citizen data scientists are well positioned to "drive" data-driven culture

Domain expertise

They can be recruited in-house as well as from many "adjacent" disciplines


Three Core Skill Areas Are Needed

Data Science

ITSkills

BusinessSkills

Ask Good Questions

Decision Making

Transparent Versus"Black Box"

Which Analytics to Choose?

Data Exploration

High- PerformanceComputing

Build, Buy, Outsource

Feature Engineering

Know the Constraints

Data Governance

Deployment


Reporting Relationship of AA/DS Teams

2

Other

Strategic Planning

CDO

CIO

CMO

CFO / Finance


Gather with your allies and make a plan to tackle 1 to 2 business problems, POCs:

– Choose high impact, high visibility problems

– Limited resources, under the radar, external experts

Evangelize and get buy-in:

– Identify the stakeholders in your organization that need to be convinced of the value of advanced analytics

After that first win (or two), decide if (when) you want to build the skills and tools internally

Recommendations for Finding Your First Win


Recommended Gartner Research

• How to Take a First Step to Advanced AnalyticsLisa Kart (G00276728)

• Advancing Business With Advanced AnalyticsAlexander Linden and Others (G00277713)

• Extend Your Portfolio of Analytics CapabilitiesLisa Kart and Others (G00254653)

• Predicts 2015: A Step Change in the Industrialization of Advanced AnalyticsAlexander Linden and Others (G00270560)

• Magic Quadrant for Advanced Analytics PlatformsGareth Herschel and Others (G00270612)

• Machine Learning Drives Digital BusinessAlexander Linden and Others (G00263964)

http://www.gartner.com/document/3083418?ref=lib

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How to Take a First Step to Advanced Analytics