A vo id in g B u sin ess A n alyt ic F ailu r es & R o ad Map f o r Imp … · 2017-06-07 · J P K...
Transcript of A vo id in g B u sin ess A n alyt ic F ailu r es & R o ad Map f o r Imp … · 2017-06-07 · J P K...
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JPK
Gro
up
Business Forecasting & Analytics ForumJune 6-7, 2017 • Boston, MA
Avoiding Business Analytic Failures & Road Map for Implementation
June 7, 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/attendee4/
Presenter: Robert Zwerling – Aurora Predictions
Assess the viability of the practical use of business analytics to improve performance
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Culture & Framework to Implementing
Analytics
“Discover your tomorrow today”
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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.
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Topic & Goal
Avoiding Business Analytic Failures & Road Map
for Implementation
Through case studies of successes and failures in
implementing analytics you’ll learn whether such efforts
can be muted or meaningless for you. You’ll get a road-
map of engaging analytics and the needed systems,
processes, and people.
Goal – take home knowledge that enables your action
Assumption – generally understand value of analytics
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About – Robert J Zwerling, P.E.
Managing Director, Aurora Predictions
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• 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
• Supporter Hoover Institution at Stanford University
• 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
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Presentation Plan to “Round the Bases”
• Start with a line drive to right by Defining Analytics
• Tagging 1st with the Barrier to Analytics
• Steal 2nd with Cases of Analytic Failure & Success
• Run to 3rd with Analytic Infrastructure Considerations
• Tag home with Analytics System Considerations
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Defining Analytics
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“line drive to right field”
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What is the Meaning of “Analytics”
• 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
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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)
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Barriers to Analytics &
Components for Success
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“tagging 1st base”
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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
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The Biggest
Barrier to Analytics
Is
Culture
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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
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Components of Analytics Culture & Relation
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mindset
system
people
process
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Mindset vs. Analytic Contribution to Business
Reporter
Commentator
Advisor
Partner
Support
Insight
Influence
Impact
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Analytics Contribution
Mindset to Business
mindset
system
people
process
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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
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mindset
system
people
process
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Types of Analytics Systems
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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
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Analytics Implementation Model
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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
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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
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mindset
system
people
process
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Analytics Implementation Model
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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
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Case Examples of Analytics
Failures & Success in:
Telecommunication, Healthcare,
Retail & Transportation
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“stealing 2nd base”
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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”
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Examples of Cultural Deficiencies –
Death by Excel Culture
4 analytics projects with 10X-70X ROI
terminated after completion because Excel:
• Protects my job from productivity
• Hides mistakes – just blame the data
• Makes me indispensable – I’m the MESS
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Examples of Cultural Deficiencies –
Death by Excel Culture
• Management failed to purged people
inhibiting analytics
• Analytics that was implemented was
“backroom” – expensive with high
complexity to give same “cover” as Excel
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Implementation Failure – Mindset &
System Mismatch
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Management
Staff
mindset
system
people
process
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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”
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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: Finance it gives reports operations wants &
ops says it gets reports finance provides
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Examples of Cultural Deficiencies –
Death by Reporting Mindset
Management failed to:
• Push folk from their comfort zone
• Provide time in day to develop analytical skills
• Provide incentive to develop the analytical skills
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Implementation Failure –
Mindset & Analytics Mismatch
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Ma
na
ge
me
nt
Staff
mindset
system
people
process
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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 - anything less than a choir is at high risk”
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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 later promoted
• No written procedures nor analytic consumers
• Core user gets promoted but replacement had no
processes or consumer demand
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Examples of Cultural Deficiencies –
Death by Attrition
• People change and delay will doom
analytics unless analytics is:
o Is quickly integrated into operations
o Has written procedures
o Has sufficient core users & consumers
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Implementation Failure – Insufficient
People & Processes
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mindset
system
people
process
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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”
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Asset Discussion
Mindset
Finance sought to partner with business for
analytic revenue forecasting to save time,
quarterly 18 month 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
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mindset
system
people
process
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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
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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
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Item to Note Regardless of Failure or Success
• Data was never an issue
• Big-Data vs. Right Data
• Real Time vs. Right Time
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Case Summary – Killers to Analytics
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mindset
system
people
process
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Infrastructure Considerations
for Implementing Analytics
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“running to 3rd base
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Checklist for Analytics Infrastructure
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mindset
system
people
process
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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)
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mindset
system
people
process
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The People
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)
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mindset
system
people
process
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Empirically Deduced – Threshold
of People to Build an Analytical Culture
You need >3 core users &
>7 consumers, anything
less is at risk from attrition
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mindset
system
people
process
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Key Processes – Must be Written
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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
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Considerations of
Analytical Systems
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“coming into home plate”
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Ad-Hoc Point vs. Systematic Solutions
• Point solution often require a Data
Scientist to coble together technology to
answer a specific issue
• Systematic solution uncovers heretofore
unknown answers and inspire heretofore
unasked questions
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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)
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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
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Dashboard Sizzle – Good Looking But Difficult
to Discern a Decision
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Visualization Complexity – Lot’s of Value But
Need to be Skilled User
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Closing Thought
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“Know thy self, know thy
enemy. A thousand
battles, a thousand
victories.” – Sun Tzu
Analytics Culture for Success – Know Yourself
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“and the winners are”
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Culture & Framework to Implementing
Analytics
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