Bersin by Deloitte - Demystifying Big Data

Demystifying Big Data
Todd Tauber, Vice President
Wendy Wang-Audia, Research Analyst
September 25, 2014
How to Start the Journey in
Talent Analytics

Copyright © 2014 Deloitte Development LLC. All rights reserved.2 Demystifying Big Data: How to Start the Journey in Talent Analytics
Contents
Who we are
Global provider of leading practices, trends, and benchmarking research in talent
management, learning, and strategic HR.
6 research practices
•  Human Resources
•  Leadership Development
•  Learning & Development
•  Talent Acquisition
•  Talent Management
•  Tools & Technology
Offerings
•  WhatWorks® Membership: Research, Tools, Education, Consulting
•  IMPACT: The industry’s premiere conference on the Business of Talent
•  Advisory Services & Consulting
Human
Resources
Talent
Acquisition
Leadership
Development
Learning &
Development
Talent
Management
Tools &
Technology

Big Data and talent analytics
How to get started with talent analytics
Data integration is key
Strategic metrics
Today’s agenda

Big Data is defined by volume, velocity, variety,
and veracity.
In 2012,
2.5 billion gigabytes of data
were created
EVERY DAY
Sources:
1. Douglas Laney, “The Importance of Big Data: A Definition”, Gartner, June 21, 2012.
2. Meghan M. Biro, “Big, Bad Data: How Talent Analytics Will Make it Work in HR”, Forbes.com, August 7, 2014
3. Matthew Wall, “Big Data: Are you ready for blast-off? “ BBC.com, March 3, 2014
Last 2 years =
90% of data

Every minute…
Sources: Infographic by DOMO, Allegra Tepper, “How Much Data is Created Every Minute?” Mashable.com, June 22,
2012
2,000,000+
Google searches
100,000+
tweets
$272,070
spent on
web shopping
204,166,667
emails
48 hours
of videos uploaded
to YouTube

People
data
Patterns /
Insights
Decision-
making
(Talent) analytics is analyzing data to support
decision-making.

Mature analytics organizations are seeing benefits.
2x more likely to improve
recruiting efforts
2x more likely to improve
leadership pipelines
3x more likely to realize cost
reductions / efficiency gains
30% higher stock returns than the
S&P 500 over the last 3 years
Source: High-Impact Talent Analytics, Bersin by Deloitte, 2013

HR is investing more in talent analytics.
Source: High-Impact Talent Analytics, Bersin by Deloitte, 2013; High-Impact Learning
Measurement survey, 2014
19%
Purchased
analytics tools
Hired consulting
services
22%
Up-skill existing
staff
40%
Built/improved
data warehouse
31%
Hired or
transferred more
staff
31%
Develop better
processes to
ensure clean and
accurate data
47%

What is your organization’s biggest challenge
with starting talent analytics?
Responses:
a. Lack of tools
b. Lack of funding
c. Lack of staff/skills
d. Lack of data quality
e. Other:

Finance &
Operations
Sales &
Marketing
HR
Only 15% of organizations think that HR is credible
with talent analytics.
15%80% 57%

Source: Bersin by Deloitte, 2014
Most organizations are just starting out.
BersinbyDeloitte
Operational Reporting
Reactive Reporting of Operational & Compliance Measures •
Focus on Data Accuracy, Consistency & Timeliness
Level 1
Advanced Reporting
Proactive Reporting for Decision-Making • Analysis of Trends
& Benchmarks • Customizable, Self-Service Dashboards
Level 2
Advanced Analytics
Statistical Analysis to Solve Business Problems • Identification of Issues
& Actionable Solutions • Centralized Staffing & Integrated Data
Level 3
Predictive Analytics
Development of Predictive Models • Scenario Planning •
Integration with Business & Workforce Planning • Data Governance Model
Level 4
4%
10%
30%
56%
86%
Talent Analytics Maturity Model

Overcoming
the
Fear

A
lot
of
our
client
organiza0ons
view
repor0ng
as
a
prelude
into
analy0cs.
Here
is
how:

Experience
in
opera0onal
and
self-‐service
repor0ng
as
well
as
dashboards

encourages
a
culture
of
data-‐driven
decision
making

As
a
consequence,
a
lot
of
impact
can
be
demonstrated
by
making
repor0ng
available

(and
valuable)
to
diﬀerent
business
stakeholders

Then
the
transi0on
into
more
advanced
maturity
levels
is
easier
(in
terms
of
stakeholder

buy-‐in,
investment
required,
data
standards,
organiza0onal
readiness,
etc.)

How to Get Started
Demystifying the Process

Goals and major activities at Level 1
“Do not try to boil the ocean or you’ll fail miserably.”
- head of analytics at global manufacturing company.
•  Understand data sources and systems
•  Prioritize a few metrics to measure
•  Establish some consistent process to measure and
define those metrics
Establish data standards
•  Understand their business challenges and needs
•  Identify key metrics to measure
Engage with stakeholders
•  Respond to ad hoc requests
•  Track operational metrics, e.g. safety, turnover, etc.
•  Leverage current HRIS, TMS, LMS
Operational reporting

Use the existing resources in the organization.
Data & Systems
Become familiar with
various data sources and
systems (e.g. HRIS, LMS,
TMS, etc.)
Staff & Skills
Leverage measurement
and evaluation staff
Build basic reports
Respond to stakeholder
requests
Tools
Leverage dashboard
functionality of HR or talent
management systems

How
to
Get
Started

Analy0cs
journey

• Export
learning
&

competency
data
from

LMS
into
Excel
pivot

tables

• Integrate
raw
data
from

the
company’s
incident

logging
system

• Process
Excel
ﬁles
and

create
compliance
&
risk

management
reports

• Schedule
report

distribu0on
to
all
levels

in
the
organiza0on

Lessons
learned

• Start
with
exis0ng
data

• Start
with
tools
you

already
know

• Demonstrate
impact

across
the
organiza0on

Next
steps

• Embrace
more
rigorous

data
standards
&

business
intelligence

plaSorm

• Expand
compliance
&
risk

analy0cs
both
inside
&

outside
the
organiza0on

(e.g.
to
regulatory

bodies)

• Integrate
data
rela0ng
to

pa0ent
outcomes
&

sa0sfac0on

Major
healthcare

provider
in
the
UK

Focus on building key capabilities.
Data Quality:
Timeliness
and
Accuracy
Dashboard
Capabilities
IT Support
Workforce
Planning
Capabilities
Culture of
Data-Driven
Decision-
Making
Staff Size /
Structure
Skills Set
Data
Visualization
Capability
Budget
Data
Dictionary
Data
Governance
Data
Warehouse

Key capabilities
Data quality
•  Accurate and timely data
•  Essential to credibility and decision-making
•  Standardize measurement, reporting, and definitions of data
•  Data integration is key
•  Examples of issues: duplication, validity, lack of consistent
definitions, etc.
Dashboard
capabilities
Team capabilities
IT support
Culture of data-
driven decision-
making

Key capabilities
Data quality
Dashboard
capabilities
•  Respond to ad hoc requests and customize reports
•  Communicate trends and trouble areas, enabling audience to
drill down and filter data
•  Self-service reporting access enables analytics team to be more
than a reporting team
•  Trending data over time and for benchmarking
Team capabilities
IT support
Culture of data-
driven decision-
making

Key capabilities
Data quality
Dashboard
capabilities
Team capabilities
•  Skills to manage, analyze, and present data
•  Expertise in statistics, database, data visualization, IT, and
understand of HR and the business
•  Consulting skills to engage with managers and business leaders
•  Centralize/unify analytics team
IT support
Culture of data-
driven decision-
making

Key capabilities
Data quality
Dashboard
capabilities
Team capabilities
IT support
•  Partner in managing internal systems and data architecture
•  Evaluating and implementing tools and systems and
supporting access to data
Culture of data-
driven decision-
making

Key capabilities
Data quality
Dashboard
capabilities
Team capabilities
IT support
Culture of data-
driven decision-
making
•  Value placed on data evidence
•  Starts at the top (CEO or other executive)
•  Can be fostered by analytics team through demonstrating
value of data solving business challenges

Data Integration
Key to Quality Analytics

The Ugly Part of The StoryThe ugly side
Data integration
Data analysis
Scalable computing
Data dictionary
Disparate systems
Data quality
Reporting tools
Data governance
Data
visualization
Data entry

Data integration is essential.
45% 44% 44%
25%
6%
13%
15%
6%
4% 3%
9%
25%
Level 1 Level 2 Level 3 Level 4
3-4 systems
5-7 systems
8 or more systems

Enablers of data integration
Data governance
Keep data organized and
definitions consistent
Build a data dictionary to keep
data and metrics consistently
defined
Tools and technology
Need advanced tools to help
aggregate all data
Getting a data warehouse is an
option for many organizations
Cross-functional team
Necessary to effectively build an
analytics team
Bring together data and insights
from silo’ed measurement and
analytics staff

What common data challenges does your
organization face?
Responses:
a. Lack of data integration
b. Lack of data quality
c. No consistent definition
d. Lack of skill to interpret them
e. Other:

Data
Integra&on

Global
bank
from
Europe

•  Integrate
training
delivery
and

training
cost
data

•  Focus
on
training
eﬃcacy
metrics

(e.g.
Kirkpatrick
model
analysis)

Major
luxury
car

manufacturer
from
the
UK

•  Integrate
talent
&
opera3onal

performance
data
such
as
sales

revenue
and
customer
sa3sfac3on

•  Integrate
data
across
diﬀerent

business
units
and
geographical

regions

Strategic Metrics
Moving from Operational
Data and Reporting

From transactional to business
Instead…
Collect as much
data as possible
Engage
stakeholders
Identify
business
challenge
Identify metrics
and data
Draft reports
and dashboards
Ensure results
are actionable

What has been critical to jumpstarting your
analytics function?
Responses:
a. Tools
b. Funding
c. Staff
d. Data management/integration
e. Other:

Three things to jumpstart to analytics
Staff/skills
Self-
service
reporting
Project win

Building a roadmap
Core problems to
address in the next
1-3 years
Value that analytics will
bring to the organization
Executive sponsors in
both HR and lines of
business
Team’s roadmap for
implementation in the
next 2 years
Staffing plan for the
next 1-3 years
Necessary tools and
systems
Budget from HR and IT Necessary level of IT
support and other
dependencies

?Questions
Todd Tauber
VP Enterprise Learning Research
Bersin by Deloitte, Deloitte Consulting LLP
ttauber@deloitte.com
@toddtauber
Wendy Wang-Audia
Senior Research Analyst
Bersin by Deloitte, Deloitte Consulting LLP
mwangaudia@deloitte.com

About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of
member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also
referred to as “Deloitte Global”) does not provide services to clients. Please see www.deloitte.com/about for a detailed description of DTTL
and its member firms. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its
subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.
Copyright © 2014 Deloitte Development LLC. All rights reserved.
36 USC 220506
Member of Deloitte Touche Tohmatsu Limited

Bersin by Deloitte - Demystifying Big Data

Bersin by Deloitte - Demystifying Big Data

Recommandé

Contenu connexe

Tendances

Tendances(20)

En vedette

En vedette(20)

Similaire à Bersin by Deloitte - Demystifying Big Data

Similaire à Bersin by Deloitte - Demystifying Big Data(20)

Plus de NetDimensions

Plus de NetDimensions(20)

Dernier

Dernier(20)

Bersin by Deloitte - Demystifying Big Data