The document discusses HR analytics, detailing its significance in gathering and analyzing data related to human resource functions to enhance decision-making. It outlines different levels of analytics, including descriptive, predictive, and prescriptive analytics, and emphasizes the role of data in organizational efficiency and effectiveness. Additionally, it highlights challenges organizations face, such as lack of internal analytics expertise and high investment costs, as they adopt HR analytics tools.

1. VTU IV SEM MBA
BUSINESS ANALYTICS AREA
HUMAN RESOURCE ANALYTICS 22MBABA404
HRA UNIT 1 - HR ANALYTICS

2. HR Analytics in Perspective
• Analytics is based on data. HR analytics is the science of gathering,
organizing and analyzing the data related to HR functions like recruitment,
talent management, employee engagement, performance and retention to
ensure better decision making in all these areas.
Role of HR Analytics
• HR management software offers real-time data analytics that helps you
hire, retain, and develop the best talent. Human resource analytics also
called as workforce analytics or talent analytics enables your HR managers
to make better decisions based on hardcore data.
• Analytics helps in predicting retention and offers extensive modelling
capabilities for workforce planning. Workforce analytics enables you to
calculate how many employees would retire per year, the type of
candidates needed to replace them, and from where those new hires can
be recruited. This helps in categorizing and prioritizing action plans that
address the potential talent shortage

3. DEFINITION- ANALYTICS
• Analytics is a meeting of art and science. The arts teach us how to look at
the world. The sciences teach us how to do something. Analytics is first a
mental framework, a logistical progression, and second a set of statistical
operations.
• Human resources (HR) or human capital analytics is primarily a
communications device.
• It brings together data from disparate sources, such as surveys, records,
and operations, to decorate a unified, actionable picture of current
conditions and likely futures.
• This is an evidence-based approach to making better decisions.

4. Analytics is divided into three levels:
1. Descriptive Analytics-Descriptive HR analytics reveal and describe
relationships and current and historical data patterns. This is the
foundation of your analytics effort. It includes, for example, dashboards
and scorecards; workforce segmentation; data mining for basic
patterns; and periodic reports.
2. Predictive Analytics-Predictive analysis covers a variety of techniques
(statistics, modeling, data mining) that use current and historical facts to
make predictions about the future. It’s about probabilities and potential
impact. It involves, for example, models used for increasing the
probability of selecting the right people to hire, train, and promote.
3. Prescriptive Analytics-Prescriptive analytics goes beyond predictions and
outlines decision options and workforce optimization. It is used to
analyze complex data to predict outcomes, provide decision options, and
show alternative business impacts

5. TWO VALUES
• The purpose of analytics is to find the best path through a mass of data to
uncover hidden value. Value comes in two forms: financial and economic.
There is a distinct difference between these forms initially, yet they
eventually combine.
• Economic data includes practical, non-cash significant items or processes
affecting material resources.
• Examples include market reputation, customer satisfaction, best companies
to work for, and community relations.
• Financial data are often referred to as off– balance sheet assets.
• Each of them eventually should turn into financial value as stockholders
invest in company stock, customers purchase products or services, high-
performing personnel seek employment with the organization, and
favourable community support ensues. Financial value examples are cash
and other liquid resources such as stock and bonds. These are recorded on
the income statement and balance sheet, the building blocks of accounting

6. VALUING HR ANALYTICS IN THE ORGANIZATIONAL SYSTEM

7. The Building Blocks of Analytics

8. The Levels of Analytics

9. Description of levels of analytics

10. HR ANALYTICS: THE THIRD WAVE FOR HR VALUE CREATION

11. Capturing Value through Third-party Analytics Arrangement
• Project-based HR analytics. This is typically a one-time consulting-type engagement where the
organization needs a solution to a pressing problem within a fixed time frame. This is presently the
most popular engagement model.
• Ongoing-relationship / outsourcing-based HR analytics. There are broadly two types of
arrangements here:
• As part of HR outsourcing arrangement. This is on an ongoing basis as part of an existing outsourcing
engagement (a multi-process HR outsourcing engagement or a single-process outsourcing engagement such as
Recruitment Process Outsourcing (RPO)). There are two different constructs in which it works:
– Business-as-usual (BAU) construct. The outsourcing contract does not specify the usage of analytics but
the buyer expects the service provider to utilize analytics in a BAU construct to make incremental
improvements
– Specified analytics construct. In this case, analytics is specifically mentioned in the initial contract or
incorporated at a later stage.

12. Service level agreement (SLA) / Key performance
indicator (KPI)

13. APPROACH TO HR ANALYTICS

14. LOCATING THE HR CHALLENGE IN THE SYSTEM- CURRENT ADOPTION AND CHALLENGES
Traditional HR mindset.
Organizations’ internal limitations with the three building blocks of analytics. Many organizations are ill-
prepared to take on the analytics journey by themselves. A common hurdle is the lack of internal talent well-
versed with statistics/analytics knowledge within HR. Additionally, organizations may lack appropriate analytics
tools or the technological capability. Lastly, lack of a unified data warehouse due to unequal HR systems might
act as a bottleneck, especially for broad scope HR analytics usage
High investment cost. For an organization to embark on the analytics journey on their own, it will require a
high upfront investment in terms of people, technology, and data. Hiring analytics talent is a big investment.
Considering the lack of HR-specific analytics talent in the market, the company will also have to invest and
develop cross-functional talent between the fresh-hired analytics people and internal HR team. Investments in
analytics tools or a unified data warehouse are also costly propositions
High operational cost. Some of the attractive HR areas where analytics can make a difference, such as
compensation designing and performance management, are point-in-time activities which occur every year
only at specific time-intervals. Any organization attempting to gain the analytics advantage for just these point-
in-time activities will still have to maintain a vast analytics infrastructure (people and tools) throughout the
year, thereby raising operational cost through a fixed cost component.

15. JOURNEY OF HR ANALYTICS
JUSTIFICATION- MEASUREMENT- EFFECTIVENESS- VALUE CREATION- IMPACT
HR VALUE CHAIN
• 1.Efficiency 2.Effectiveness 3.Impact
HR MEASUREMENT MODEL:
It speaks in terms of Efficiency, Effectiveness & Outcome

16. HRA FRAMEWORK
• Measure the individual Behaviors, Traits or Reactions-
1. Measures of the reactions of various groups (top
management, customers, applicants or trainees)
2. What individuals have learned
• How their behaviour has changed on the job

17. 1. Include various ratios HR Ratios (accident frequency or
severity)
2. Calculating percentages (labour turnover)
3. Measuring central tendency & variability (such as standard
deviation of performance, shortages & surplus)
4. Measures of correlations (for example measures of
association between employee satisfaction & turnover)
STATISTICAL SUMMARIES INCLUDES & SPEAKS ABOUT INDIVIDUAL
MEASURES

18. HR measurement journey in tune with the HR
maturity journey understanding the
organizational system (LEAN)
• In 1987 a research team from MIT studying the Toyota production
system coined the term LEAN, to describe the system as one that
“needs less of everything to design & produce products
economically at lower volume with fewer errors”
•Womack & Jones (1991) defined LEAN as a way to do more with
less –less human effort, less equipment, less time & less space – while
coming closer to providing customers with exactly what they want.

19. LEAN Organization System:
A lean organization system describes a business or business unit that holistically applies lean principles to the way
it plans, prioritizes, manages & measures work.
LEAN organization system – Two fundamental concepts of LEAN
1. Eliminate anything that does not add value to customer
2. Work systematically & continuously to create more value to the customers
KEY PRINCIPLES OF LEAN SYSTEM
Build Quality
Deliver Fast
Defer Commitment
Respect People – lean systems are designed to maximize the customer value & minimizing the waste, lean
system encourages that allow everyone to do their best work.