Date post: | 22-Jan-2018 |
Category: |
Data & Analytics |
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Leading Analytics in a Period of
Talent/Technology
Transformation
Corinium Chief Data Analytics Officer, Boston
Lori C. Bieda, Head, BMO Analytics Centre of Excellence Personal and Commercial Bank, CAN
#loribieda
October 2, 2017
The Answers are in the Data.
| 2
“The answers to the innumerable business opportunities we face lie in our data, yet our thirst
for business insight often goes unsatisfied.”
Leading Analytics in Changing Times, MIT Sloan Management Review, Sept 2017, L. Bieda
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Optimal Conditions
for Analytics, Right?
Analytics goes mainstream
Affordable Technology
Data Wealth Massive demand
World of Openness
This should be our panacea…
|
The Dream
4
Out of pace with the reality…
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Management & Analytics
The Promise of Technology
Talent-Starved Where demand outpaces
all hopes of supply
The Translation/
Interpretation Gap
Not at pace with reality of data,
or able to offload volume
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Service Provider
Consultant Business Driver
Executes analysis as service in response to a request
Provides analysis, opinions & insights
Drives analytical priorities for overall business & plays strong
role in strategic planning
31% 41% 24% 31% 44% 23%
*5% (US), 7% (Can) (unsure)
Source: SAS
The Role Analytics Teams Play
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The flood of analytics demand…
30-50% YOY
| 8
18 Months to become value-generating
1 year to learn company data & business
An
aly
tic
s T
ale
nt
Against backdrop of….
Out of date corporate pay scales & policies
Global analytics talent drought, including fewer women in data & analytics
Inability to grow “translation layer” talent at pace needed
Double Digit analytics attrition
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Managing Through..
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Standardize, Automate, Self-serve & Segment
Analytics Talent Management
Rationalize, Ruthlessly Prioritize & PLAN Analytics on analytics & the analytics database Business alignment sessions & link to strategic priorities Workflow tracking Analytics planning (tactical and strategic) to move to new technology
Standard in-take Segmentation of the work for future state (automation, machine
learning, robotics.. ) and segmented match-up against talent
Customized acquisition, development & succession plans Career ladders, technical and non-technical training Direct involvement in solving business issues & seeing outcomes
An
aly
tic
s T
ale
nt
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3%
1%
20%
26%
50%
Not sure
None of the above
They are part of a hybrid analytics team
They are part of a de-centralized analyticsteam
They are part of a centralized analytics team
Organizational Models for Analytics
Source: SAS
Ma
na
ge
me
nt
& A
na
ly
tic
s
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Managing Through..
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Analytics as a Competency & Function
Analytics Translation
Increasing Analytics IQ
Fostering fact-first culture Executive & management education
Repositioning capability Deliberate cross-pollination
Communication, influence and persuasion Business acumen investment
Read up: The Translation Layer: The Role of Analytic Talent
Ma
na
ge
me
nt
& A
na
ly
tic
s
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Th
e T
ec
hn
olo
gy
Pr
om
is
e
From selling analytics, to being overrun with demand, to the promise of the machine offloading analytics demand…
|
13
Reporting
Optimization
Ad Hoc Forecasting
Measurement vs
KPI’s via reports
dashboards
/scorecards by
analysts
Estimates on
trend & variance vs
target thru reports,
dashboards,
scorecards, &
portals by business
& financial analysts
Estimations of
based on statistical
projection thru
presentations &
reports delivered by
statisticians
Constraint based
optimization on
changing realities
Simulations &
statistical models via
presentations,
reports & software,
by statisticians
Prediction What’s best that
can happen? What’ll happen
next? What if trends
continue?
What happened?
How many, how
often, where?
Analysis on key
projects, problem
diagnosis, variance
vs norm via stand
alone reports by
business analysts
KNOW DIAGNOSE ANTICIPATE PROVISION CAPITALIZE
Descriptive Predictive
Rear-view Mirror Windshield Crystal Ball
Descriptive Predictive ``
Descriptive Predictive Foundational Strategically Differentiating
Type of Analytics
Business Value
Significant portion of volume still ad
hoc and not “automateable”
Our Reality…
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State of the Data
vastly improved, but still consuming
30-40% of analyst time
Not as clean, or integrated as
needed
Building Technology for Future
Means Tapping into Today’s Analysts
Taps into today’s analysts (who are
tapped out with existing demand)
Advanced technology predicated
on clean data (garbage in/out)
We’re on a Journey..
|
Managing Through..
15
A Pivot to Data Monetization
Change Management
A True IT/Analytics Partnership
Selection of Technology – fit for architecture, business & users
From rationalization and efficiency to incremental uplift – to BOTH
The Plan for Open source – a people, process & technology plan Need for significant integrated capacity planning
Th
e T
ec
hn
olo
gy
Pr
om
is
e
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Management & Analytics
The Promise of Technology
Analytics Talent Where demand outpaces
all hopes of supply
The Translation/ Interpretation Gap
Not at pace with reality of data,
or able to offload volume
Rationalize, Ruthlessly Prioritize & PLAN Standardize, Automate, Self-serve & Segment Analytics Talent Management
Increasing Analytics IQ Analytics as a Competency & Function Analytics Translation
A True IT/Analytics Partnership A Pivot to Data Monetization Change Management
Thank You
17
Stay Connected:
loribieda
#loribieda
Recent Writing: Leading Analytics in Changing Times, MIT Sloan Management Review, L. Bieda The Translation Layer: The Role of Analytic Talent, SAS, L. Bieda The Essential Journey Towards Journey Analytics, L. Bieda