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.

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“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…

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The Dream

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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

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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Out of date corporate pay scales & policies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

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& A

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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

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& A

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Th

e T

ec

hn

olo

gy

Pr

om

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e

From selling analytics, to being overrun with demand, to the promise of the machine offloading analytics demand…

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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..

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Managing Through..

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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

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Pr

om

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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

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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