1.
HOW TO START PEOPLE ANALYTICS
PRACTICE?
MARTIN BOUDIKIANOV - 26. SEPT 2019
HR & PEOPLE ANALYTICS, SOFIA

2.
ABOUT
Martin Boudikianov
Delivering Digital HR with
SuccessFactors and SAP Cloud Platform
linkedin.com/in/martinboudikianov
2

3.
A HR PRACTICE ENABLED BY INFORMATION
TECHNOLOGY THAT USES DESCRIPTIVE,
VISUAL, AND STATISTICAL ANALYSES OF
DATA RELATED TO HR PROCESSES, HUMAN
CAPITAL, ORGANIZATIONAL PERFORMANCE,
AND EXTERNAL ECONOMIC BENCHMARKS TO
ESTABLISH BUSINESS IMPACT AND ENABLE
DATA-DRIVEN DECISION-MAKING. (1)

4.
WHERE ARE WE KNOW? WHAT DO WE WANT TO ACHIEVE
EFFICIENCY VS. EFFECTIVENESS

5.
WHERE ARE WE KNOW? WHAT DO WE WANT TO ACHIEVE
ANALYTICAL MATURITY
Descriptive Predictive Prescriptive

6.
START WHERE YOU ARE.
USE WHAT YOU HAVE.
DO WHAT YOU CAN.
Arthur Ashe

7.
MOTIVATION
PITCHING KEY STAKEHOLDERS
▸ What do we want to achieve and why it is important for the organization and its
stakeholders/employees:
▸ The majority of organizations (54%) use people analytics to improve HR effectiveness
today. Organizations more frequently rely on people analytics to improve business
outcomes, organizational performance and achieve labor cost savings. (3)
▸ People Analytics allows HR to gain a more strategic role in the organization and
clearly show its impact.
▸ Advanced organizations use data to analyze the workforce proactively, make
predictions, and create and monitor comprehensive workforce plans to achieve
financial success. (3)
▸ HR data has become an strategic priority, but it takes efforts in order to enable the
usage of it.

8.
THE SUPERHEROES
THE TEAM
Domain Knowledge
Communication & Story Telling
Management
Critical Thinking
Data Exploration
Business Intelligence
Visualisation
Model Assessment
Statistical Modeling
Machine Learning
NLP
Programming
Math
Data Architecture
Big Data Infrastructure
& Storage
Database Administration
Data Extraction
Data Transformation
DATA TRANSLATOR
DATA SCIENTISTDATA ENGINEER

9.
THE ABCD DATA QUALITY FRAMEWORK
DATA QUALITY
Accuracy Breadth Consistency Depth
Information is factually
correct.
Information is
comprehensive and
representative.
Information conforms
to its defined format
and has no gaps.
Information is
sufficiently granular
Source: Gartner

10.
HOW TO AUDIT YOUR TALENT DATA QUALITY
DATA QUALITY
Gather Talent Data
Stakeholder
Identify Where
Data Quality
Causes Frictions
Align Data Quality
Issues to ABCD
Framework
Weigh Business
Impact
List talent objectives
and strategies
dependent on data.
Brainstorm where poor
data quality prevents
ideal talent analytics
output.
Map quality issues to
their appropriate
ABCD category.
Segment quality issues
by their impact on the
business.
Source: Gartner

11.
LOGISTICS
DATA LOGISTICS
▸ System Integrations - connecting the different sources with
target systems.
▸ Storage, Usage and Retention - based on volumes, velocity
and sensitivity (security).
▸ Data Provenance - process framework for defining the data
entities, actors and available activities.
▸ GDPR compliance.

12.
THE AGE OF PEOPLE ANALYTICS
DEEPER INSIGHTS (3)
▸ People analytics maturity requires an organizational-wide effort.
▸ A culture of change/innovation management is essential for
organizations to achieve the highest possible value from their
efforts to create a data-driven culture and drive enhanced
business outcomes.
▸ Organizations that adopt more solutions, use more data
sources, embrace more analytics topics, encourage a broader
scope of users and use leading change management practices
achieved improved financial performance.

13.
DATA HAS A BETTER IDEA
THE PROCESS OF ANALYTICS
3. COLLECT AND OBTAIN DATA
4. EXPLORE DATA
1. DEFINE BUSINESS OBJECTIVE
2. TRANSLATE BUSINESS OBJECTIVE TO A DATA PROJECT
5. TRANSFORM AND CLEAN DATA
6. CONDUCT DATA ANALYSIS
7. TRANSLATE DATA INSIGHTS INTO ACTION
8. ASSESS OUTCOMES

14.
DATA HAS A BETTER IDEA
WHERE ANALYTICS SHOULD NOT BE USED
▸ When trying something novel (historical data is not
available).
▸ If the salient event is rare (models can't be trained
sufficiently).
▸ When trying to confirm what one already believes (typical
pitfall).

15.
DEMO
TRAINING AN ALGORITHM FOR PREDICTING
EXPECTED INCOME

16.
DATA HAS A BETTER IDEA
EXAMPLES OF QUESTIONS THAT CAN BE ANSWERED (2)

17.
"IT IS NOT
ENOUGH TO
STARE UP THE
STEPS - WE
MUST STEP UP
THE STAIRS."
THANK YOU!

18.
CREDITS
▸ (1) Janet H. Marler & John W. Boudreau (2017) An evidence-based
review of HR Analytics, The International Journal of Human Resource
Management, 28:1, 3-26, DOI: 10.1080/09585192.2016.1244699
▸ (2) SAP SE
▸ (3) https://hello.visier.com/resources_research-reports_age-of-
people-analytics.html
▸ Pictures: Mika Baumaister, Jukan Tateisi, Frank Chamaki, Stephen
Dawson
▸ BUx MOOC - https://www.edx.org/school/bux