JPK

Gro

up

Organizational, Customer & Data Intelligence and Analytics Forum

June 8-9, 2017  •  Boston, MA

Organizational Intelligence Assessment Workshop

June 8, 1:00 pm

Founder and Managing Director of Aurora Predictions, a software company that provides revenue foresights planning to ensure companies make their quarterly revenue targets and insights to understand what drives their revenue dynamics.

 He is also founder and president of Z Concepts, a company that promotes innovation in technology and civics; and co-founder of US Vigilance

(www.usvigilance.com), inspiring responsible citizenship through fact based and reasoned discussion on public policy. He has been a high-tech entrepreneur for

25 years founding and growing software companies in telecommunication, manufacturing & distribution, high data availability, and predictive analytics. 

View presentation online at: https://jpkgroupsummits.com/attendee6/

Presenter: Robert Zwerling – Aurora Predictions

Learn the expected outcomes to compare your organization & assess the viability of the practical use of business analytics

Organizational Intelligence Enablement

Workshop

“Discover your tomorrow today”

Notice

This report contains forward looking statements. Forward looking

statements have no guarantee of being achieved. This

presentation is for information only and Aurora Predictions, LLC

makes no representation or warranty to the accuracy or

completeness of the data herein or fitness or merchantability of

its use of any kind. Trademarks are the property of their

respective company.

/ 2 /

Topic & Goal

Organizational Intelligence (OI) Assessment

Workshop

Benchmark and plan intelligence and data governance

strategy through the systems, people and processes

needed for the enablement of analytics that support OI.

Goal – take home plan to enable Analytics for OI

/ 3 /

About – Robert J Zwerling, P.E.

Managing Director, Aurora Predictions

/ 4 /

• Co-Author, Vigilance The Price of Liberty

• Co-Founder, US Vigilance, promoting responsible

citizenship (www.usvigilance.com)

• Co-Producer with Richard Nixon Foundation, The

Collegiate Forum

• 25+ years growing software companies in M&D, Telecom, High Data

Availability & Predictive Analytics with 3 successful exits

• Authored a dozen papers on predictive analytics, 1 patent pending

• Bachelor & Masters in Engineering, Member Tau Beta Pi, and

Register Professional Engineer

Workshop Plan to “Round the Bases”

• Start with a line drive to right with OI Fundamentals

• Head to 1st base with Building Analytical Thinking

• Round 1st with the Barriers to Analytics

• Steal 2nd with cases of Analytical Failure & Success

• Run to 3rd with Analytics Infrastructure Considerations

• Round 3rd with Analytic System Considerations

• Tag home plate with Building Blocks of DG

/ 5 /

Organizational Intelligence (OI)

Fundamentals

/ 6 /

“line drive to right field”

Four High Level Components of OI

/ 7 /

Culture

What is the Definition of OI

• Organizational Intelligence (OI) is the

capability of an organization to

comprehend and conclude knowledge

relevant to its business purpose

/ 8 /

Understanding the Components of OI

• Comprehend & Conclude – is the process

of analysis, evaluation and conclusion

• Knowledge – is derived from data

transformed to meaningful information

• Data – is relevant (right) data vs. big data

/ 9 /

To Comprehend and

Conclude from Knowledge

of the Data requires

Analytics

/ 10 /

Definition of “Analytics”

/ 11 /

• Reporting: presentation of past data & its

arithmetic comparison – Scorecards/Dashboards

of past are NOT analytics

• Analytics: arithmetic, statistic and mathematic

calculation on past data for insight and prediction

Meaning of “Insight” & “Foresight”

• Insight – revealing facts of the past –

interesting but not necessarily actionable

• Predictions (Foresight) – calculation on

facts of past for future outcomes both

interesting & actionable

(forecasts, predictions, probability & accuracy)

/ 12 /

Data Requirements

Timely

Accurate

Complete

Accessible

/ 13 /

Plan for Implementing Analytics for OI

• Develop Analytical Thinking

• Identify Barriers to Analytics

• Build Infrastructure for Analytics

• Define DG Framework for Analytics

/ 14 /

Building Analytical Thinking for OI

/ 15 /

“heading to 1st base”

Analytical Thinking

• You don’t need to be a Data Scientist

• Analytical thinking is a process approach

to the gathering the pertinent facts and

evaluating those facts

• The goal of analytical thinking is to solve

issues that optimize decisions

/ 16 /

Process of Analytical Thinking

/ 17 /

Task Targets

What Is being solved

Why Is solving valuable

Where Do the pertinent facts reside

Who Owns the facts

When Are the facts updated

How Will solving be implemented & measured

Building Analytical Thinking

• Conclusions and recommendations follow

analysis and evaluation of facts

• Beware Excel for Analytical Thinking

o Conclusions & recommendations lead

analysis and evaluation of facts

(justifying decisions already made)

/ 18 /

Barriers to Analytics & Components for

Success

/ 19 /

“rounding 1st base”

General Perception of Barriers to Implementing Analytics

• Complex

Simpler new generation tools

• Cost not Budgeted

ROI on analytics is 10X

• CEO Support

Management yes, CEO no

• Data Availability/Integrity

Only matter of effort & new gen tools address

/ 20 /

The Biggest

Barrier to Analytics

Is

Culture

/ 21 /

What is the Meaning of “Culture”

• The Merriam-Webster dictionary defines Culture:

Beliefs, customs, arts, etc., of a particular society,

group, place, or time

• The culture needed for “Analytics” is:

Mindset, system, people and processes in the

business to make data driven decisions using

quantitative analysis and predictions

/ 22 /

Components of Analytics Culture & Relation

/ 23 /

mindset

system

people

process

Mindset vs. Analytic Contribution to Business

Reporter

Commentator

Advisor

Partner

Support

Insight

Influence

Impact

/ 24 /

Analytics Contribution

Mindset to Business

mindset

system

people

process

Mindset vs. Deliverables & System

Mindset Deliverables System

ReporterTimely & Accurate

Historical ReportingExcel & BI

CommentatorReporting with Simple

Analytics of Interest

Discovery &

Visualization (DV)

AdvisorInfluential Reporting with

Insights

Desktop Stat (DS) or

DV & Analytics (DV&A)

PartnerImpactful Insights &

ForesightDV&A

/ 25 /

mindset

system

people

process

Types of Analytics Systems

/ 26 /

System Description

Desktop Statistical

(DS)

Excel add-on experimental & personal use,

small database, need statistician

Enterprise Data

Mining

Very complex, large, for specialized use

only, all else suicide

Discovery &

Visualization (DV)

Deep dive with trends & dashboards for

insights but not predictive

Discovery,

Visualization &

Analytics (DV&A)

Deep dive trends & dashboards for

insights & predictive analytics for foresight

mindset

system

people

process

Analytics Implementation Model

/ 27 /

Reporter

Commentator

Advisor

Partner

Support

Insight

Influence

Impact

Analytics Contribution

Mindset to Business

Excel

DV

DS or DV&A

DV&A

System

Analytics

Reporting

mindset

system

people

process

The People & Processes

• People: need threshold number of “core” &

“consumer” users or be at risk to attrition

• Processes: written procedures for regular

operation for data loading to collaboration

Data Accessible

Data Timely

Data Accurate

/ 28 /

mindset

system

people

process

Analytics Implementation Model

/ 29 /

Reporter

Commentator

Advisor

Partner

Support

Insight

Influence

Impact

Analytics Contribution

Mindset to Business

Excel

D&V

DS or DV&A

DV&A

System

Analytics

Reporting

Sufficient People & ProcessesInsufficient

mindset

system

people

process

Case Examples of Analytics

Failures & Success in:

Telecommunication, Healthcare, Retail &

Transportation

/ 30 /

“stealing 2nd base”

Examples of Cultural Deficiencies –

Death by Excel Culture

Fortune 500 telecom company

management believe forecasting &

analytics would enable better

productivity and decisions but failed

against culture of spreadsheets

“people who care don’t know & people who know don’t care”

/ 31 /

Examples of Cultural Deficiencies –

Death by Excel Culture

• 3 analytics projects with proven 10X-70X ROI

o Forecast demand accuracy increased to 98.6%

vs. 94.1% over 1 year or 35 million units delta

o Forecast EDA use 72% of time more accurate

then internal & 71% with 90%-99% accuracy

o Forecast disk utilization and disk allocation to

save $7 million per month

/ 32 /

Examples of Cultural Deficiencies –

Death by Excel Culture

Projects terminated because Excel:

o Protects my job from productivity

o Hides mistakes – just blame the data

o Makes me indispensable – I’m the MESS

/ 33 /

Implementation Failure – Mindset &

System Mismatch

/ 34 /

Management

Staff

mindset

system

people

process

Examples of Cultural Deficiencies –

Death by Reporting Mindset

$5B healthcare provider wants

analytics for better decisions &

personnel development but failed

to develop personnel

“Good enough is good enough”

/ 35 /

Examples of Cultural Deficiencies –

Death by Reporting Mindset

FP&A successful in regular monthly and ad-hoc reporting but

despite repeated attempts failed in analytics

• Comfortable with copy & paste from DV&A to Excel and

email in “a minute”

• Comfortable with a large team spending 2 weeks in C&P to

produce monthly management reports

• Disconnect as Finance says it makes reports Operations

wants & Ops says it gets reports Finance provides

/ 36 /

Examples of Cultural Deficiencies –

Death by Reporting Mindset

Management failed to:

• Push folk from their comfort zone

• Provide time to develop analytical skills

• Put incentive to develop the analytical skills

• Coordinate Finance and Ops analytical needs

/ 37 /

Implementation Failure –

Mindset & Analytics Mismatch

/ 38 /

Ma

na

ge

me

nt

Staff

mindset

system

people

process

Examples of Cultural Deficiencies –

Death by Attrition

NASDAQ Retailer installed analytic

system for better operational

forecasting & planning but failed

to assure sufficient users

“one & done”

/ 39 /

Examples of Cultural Deficiencies –

Death by Attrition

• Goal to reduce monthly planning cycle effort by

50% and get 98% forecast accuracy – goal met!

• One person in Finance trained as “core” user

then promoted but left no written procedures

• Replacement had no processes or consumers

/ 40 /

Examples of Cultural Deficiencies –

Death by Attrition

• People turn-over and usage delay will

doom analytics unless analytics is:

o Quickly integrated into operations

o Has written procedures

o Has sufficient core users & consumers

/ 41 /

Implementation Failure – Insufficient

People & Processes

/ 42 /

mindset

system

people

process

Example of Cultural that Works –

Doing All the Right Things

NYSE freight transportation

company successfully implemented

forecasting & analytics for better

revenue planning & risk mitigation

“putting all the pieces together”

/ 43 /

Asset Discussion

Mindset

Finance sought to partner with business for

analytic revenue forecasting, quarterly 18

month rolling rev planning to mitigate risk &

to manage street expectations

Analytic

System

Realized Excel & DS are not enterprise so

went DV&A

PeopleStart team of 3 core users that grew to 80

consumers in executive, sales & marketing

ProcessUsers trained, operational processes for

data loading & collaborative input

Cultural that Works – Doing it Right

/ 44 /

mindset

system

people

process

Example of Cultural that Works – Doing it Right

• Increased forecast accuracy & forecast line of

sight through analytics & human collaboration

• Extensively use forecasting & correlations with

economic indicators to assure revenue trends

• Extensively use advanced analytics for Street

earnings management

/ 45 /

Example of Cultural that Works – Doing it Right

Use correlations to

find lead economic

indicators &

advanced

forecasting to reveal

risk points in

demand

Units vs. U.S. GDP

Lead 9 Months

Point of discussion

when forecast bar

outside green line

/ 46 /

Item to Note Regardless of Failure or Success

• Data was never an issue

• Big-Data vs. Right Data

• Real Time vs. Right Time

/ 47 /

Case Summary – Killers to Analytics

/ 48 /

mindset

system

people

process

Infrastructure Considerations for

Implementing Analytics

/ 49 /

“running to 3rd base

Checklist for Analytics Infrastructure

/ 50 /

mindset

system

people

process

Analytical Systems – Vendor Sampling

• Desktop Statistical• Crystal Ball (Oracle), Minitab®, Forecast Pro

• Enterprise Data Mining• SAS, SPSS (IBM)

• Discover & Visualization• New Gen Cloud: ClearStory, DataHero, Domo

• Old Legacy: QlickView, Tableau

• Discovery, Visualization & Analytics• New Gen Cloud: Alteryx, BigML, Datameer, Aurora

• Large Cloud: Data Analytics (Oracle)

/ 51 /

mindset

system

people

process

The People for Implementing Analytics

The Evangelist – man with the cause(Man can fly)

The Backer – man with the budget(I’ll invest in that)

The Test Pilot – smart guy(I’ll fly that)

The Congregation – group of users(We’re the airline to fly that)

/ 52 /

mindset

system

people

process

Empirically Deduced – Threshold

of People for an Analytical Culture

You need >3 core users &

>7 consumers, anything

less is at risk from attrition

/ 53 /

mindset

system

people

process

Key Processes – Must be Written

/ 54 /

Processes Goal Benchmark

Data LoadingAssure Right Data

Updated at Right Time

Sufficient

Reaction Time

Data Quality

Assurance

Right Data Without

Errors

Free from Errors &

Missing Data

CollaborationAuthorities for Inputs,

Approvals & Updates

Assure Access &

Advancement

TrainingAssure Capability &

Effectiveness of Use

Clear &

Comprehensive

mindset

system

people

process

Risk of Failure – System, People & Processes Concurrent But

Not Coincident

/ 55 /

People

Processes

System

Small

Area for

Success

Alignment for Success – System, People &

Processes Conncurrent & Coincident

/ 56 /

People

System

Processes

Considerations of

Analytical Systems

/ 57 /

“rounding 3rd base

Ad-Hoc Point vs. Systematic Solutions

• Point solution often require a Data

Scientist(s) to coble together technology

to answer a specific issue

• Systematic solution uncovers heretofore

unknown answers and inspire heretofore

unasked questions

/ 58 /

Systematic & Transportable Solution

1. Systematic – the application of predefined

comparisons and statistics meaningful to

business applied to all data across all dimensions

2. Transportable – use of predefined comparisons

and statistics across any business database

(vs. point solutions are not systematic nor typically transportable)

/ 59 /

Dimensionality & Visualization Intelligence

• Analytic intelligence require unrestricted

dimensionality to reveal value in the data

(why Excel & BI have limited value)

• Visualization can further reveal value in

data – but separate sizzle from the bacon

/ 60 /

Dashboard Sizzle – Good Looking Insightful But Not Predictive

or Necessarily Actionable

/ 61 /

Will this

good

trend

continue?

Visualization Complexity – Lot’s of Value But Need to be Skilled

User

/ 62 /

Presentation

Of

Analytics

Systemization &

Dimensionality

/ 63 /

Analytics Benchmarks

/ 64 /

Analytics Benchmark – CTV

• Cost, Time & Value (CTV) – is the

quantitative measure of analytics

o Cost – how much

o Time – how long

o Value – the ROI

/ 65 /

Data Governance Building Blocks

/ 66 /

“tagging home plate”

DG Primer for Your Information Only

• RWDG is a

comprehensive,

organized & disciplined

primer for DG

• But it’s large & maybe

too much overhead for

some organizations

/ 67 /

https://www.youtube.com/watch?v=cYuRu6P8ugI

“Real World Data Governance (RWDG)”

Definition of Data Governance

Management process of data to

assure it’s timely, complete, accurate

& accessible for reporting & analytics

to its intended business purpose

(DG vs. Data Security)

/ 68 /

Data Governance Actions

• Management – authority & accountability

• Process – written roles & responsibilities

• Timely, Complete, Accurate & Accessible

– are the measurable data goals

/ 69 /

High Level Framework for Data Governance

1. Cornerstones

2. Organization

3. Processes

4. Benchmarks

/ 70 /

Data Governance Cornerstones

1. Management makes personnel time & incentive

to attend to the actions of DG

2. Management uses the analytics requiring DG

3. Defined roles & responsibilities memorialized in

written procedure

4. Defined DG measurements to meet its goals

/ 71 /

DG Organization

• DG is best organized when distributed

• DG responsibility should be with the

users & creators of the data – not IT

• Responsibilities should be part of a

user’s role & not a role by itself

/ 72 /

IT Organization Ground Rules for DG

• Users responsible for data but IT for security

• IT should not erect “high” barriers to get data

o Person – no armies, 1 interface per database

o Processes – no bureaucracy

o Promptness – no delay for data access

/ 73 /

Process is Imperative – Written – KISS

/ 74 /

1 High Level Process Overview

Sub Process Detail

Quarterly Production Update Schedule

Production Update Process - Detail

2

3

4

• Example of Contents of a DG process

/ 75 /

Load

ModelAudit

Model

Backup Model Copy Model

to Sandbox

Analytic

Load

BI

Archive

Analytic

Load

Validate

Backup Model

Post To

WebWeekly

Process

A B C

Critical Path

1 Load Sync Up

- All team members must

participate to achieve

success

2 BI Archive

- Application update

gated by BI archive

3 Audit Model

- Audit and approval of

data load must be

completed prior to

copy to Web

- Time Sensitive

DSub - Processes

1

Sequential Update Process

2

3

4

5

6

Load Sync Up: Align on plan content

BI Archive: Process gate. Data baseline. Load Sync up 5 days prior

Validation/Back Up: Generate initial models for review

Load Model : Each knowledge site populated for validation pre WebReporter

Audit Model: Complete validation to ensure accuracy of data viewed by users

Post To Web: Users provided with access to updated data

Model Advancements

1 System & Report update requests reviewed monthly to support Model Advancement

High Level Process Overview Example

/ 76 /

Sub Process D – Weekly Detail Example

1 Process Implementation

2 - Follow up with data owner will be critical to ensure data across

systems stays aligned

BI Update

Update Web

BI Update Analytic model & BI alignment notify BI data owner of change required

- Successful repetition required to drive successful

implementation

Cost Analyst MC MC

Cost Analyst MC MC

Cost Analyst MC MC

Cost Analyst MC MC

Update Web

/ 77 /

Quarterly Production Update Schedule Example

MK

MV

MC

MC

BI Cost Lockdown / Archive

Ensure model modifications in place

Create & validate load file for each model based on agreed path

Each owner identify method of attribute review

BI alignment

Notify attribute owner of required changes to BI

Backup audited load file & post to Web

BI Archive

/ 78 /

Production Update Process – Detail Example

Finance Cost

Process

LTF Process

Web Updates

Production Updates

/ 79 /

Finance Cost Process ExampleCadence Quarterly per Cost Calendar: Dec, March, June, Sept

Model Attach Wafer Costing

Method

Load File (CSV)

Strat Marketing ForecastUnit Volume From Attach Model + QCA TM1 Feed

Build Demand from Wafer Model + Selected Cost

Inputs from TM1

Ownership

MC: Prepare and load file

OH/JL: Select TM1 Inputs to include in load file

When

Unit Volume: Update with latest Forecast available at the time - per Strat Marketing forecast calendar

Meet a week before cost archive due date and

determine which data will be transferred from TM1 to ISIS, Load file once Finance

cost models are locked

BI FeedBI Feed

Line Analytics Model Component Analytics Model Costing Model

BI inputs

be transferred from BI

to model load file once

Finance cost is locked

Load File

Data Procedures – Detail Needed

• While DG processes can be small &

simple, data loading needs detail

• The benchmark of a good procedure is it

can be used by anyone with little training

/ 80 /

Data Procedures – Detail Needed

/ 81 /

DG Benchmarks

Goals Deliverable Benchmark

Data

Timeliness

Right Time

Availability

Sufficient Reaction

Time

Complete &

Accuracy

Right Data

Without Errors

Free from Errors &

Missing Data

Data

Accessibility

Consumable by

All Who Need

Ease of Access &

Use

DG ProcessWritten Processes

& Procedures

Comprehensive,

Simple & Usable

w/o Training

/ 82 /

Closing Thought on Analytics for OI

/ 83 /

“Know thy self, know thy

enemy. A thousand

battles, a thousand

victories.” – Sun Tzu

Analytics Culture for Success – Know Yourself

/ 84 /

“and the winners are”

Organizational Intelligence

Workshop

www.aurorapredictions.com

rzwerling@aurorapredictions.com

“Discover your tomorrow today”

© 2017 Aurora Predictions, LLC & Robert J Zwerling. No portion of this presentation can be reproduced without this copyright notice. Forward looking statements have

no guarantee of being achieved. This presentation is for information only and Company or Author makes no representation or warranty to the accuracy or completeness

of the data herein or fitness or merchantability of use of any kind. All trademarks are the property of their respective company.