IT HR подкаст 68 _ MASTER

Name: IT HR подкаст 68 _ MASTER
Uploaded: 2026-02-13T11:25:32.000Z
Duration: 2 h 58 min 30 s

Introduction to HR Metrics

Format and Structure of the Discussion

The session is structured as a casual discussion with flexibility in responses, allowing participants to skip questions if they have nothing to add.

The host emphasizes a relaxed atmosphere, encouraging natural transitions between topics.

Guest Introductions

Aся Маркевич introduces herself as the HR director and participant in the IT and HR community's online meetups. She sets the stage for discussing HR metrics.

Guest Profiles

Maxim Drofeyev's Background

Maxim shares his experience of 15 years in adult education and conducting experiments that require statistical analysis, highlighting challenges faced in this field.

He has a diverse background, originally from physics but now working at the intersection of management, psychology, and linguistics. His insights into metrics stem from practical experiences with people.

Tatyana Pankova's Expertise

Tatyana introduces herself as an HR consultant with a background in psychology from MGU, focusing on emotional intelligence among leaders. She has transitioned from training roles to consulting within HR environments.

Her current work involves helping companies develop their HR strategies and analyze processes to identify inefficiencies, particularly through her research on HR metrics.

Discussion on HR Metrics

Importance of Metrics

Tatyana asserts that while some may view HR metrics as dull or complex, they are crucial for understanding organizational dynamics and improving processes within companies. This perspective aims to engage listeners by framing metrics positively rather than negatively.

Types of HR Metrics

The conversation shifts towards defining what constitutes HR metrics: indicators that track various aspects of human resource processes such as efficiency, speed, quality, costs, and outcomes related to employee engagement and loyalty. These can be categorized based on specific processes like recruitment or training effectiveness.

Understanding Metrics in Lookout

The Importance of Metrics

The speaker expresses uncertainty about whether to answer questions but suggests that the concept of metrics is intuitively understood by everyone. They propose discussing specific examples of effective and ineffective metrics.

A question is posed regarding the purpose of measuring data in Lookout, emphasizing the need to understand why numbers are collected.

Reasons for Collecting Metrics

The speaker identifies three primary goals for collecting metrics:

Management Purpose: To understand how processes work and make data-driven decisions.

Informational Purpose: To observe and comprehend ongoing activities without immediate action.

Motivational Purpose: To use metrics as a basis for performance bonuses or incentives.

Examples are provided where metrics can justify budget allocations or timelines, highlighting their practical applications in decision-making.

Trusting the Numbers

A discussion arises about whether one can trust the collected figures. The speaker notes that while numbers can carry meaning, they must be interpreted correctly.

Citing influential figures like Edward Deming and Eliyahu Goldratt, it’s emphasized that metrics should guide proactive decision-making to mitigate risks rather than merely serve as performance indicators.

Understanding Measurement Errors

It is explained that all measurements contain errors—both systematic and random—which can affect decision-making based on these figures.

The speaker reflects on intrinsic motivations for quality work beyond financial incentives, suggesting that many individuals naturally strive to perform well regardless of monetary rewards.

Validating Metric Reliability

Trust in metrics hinges on understanding their origins and context. Knowing what constitutes normal versus abnormal values helps gauge when company objectives may be at risk.

Goldratt's perspective is shared, asserting that once salaries are tied to specific metrics, those metrics lose credibility. This viewpoint aligns with the speaker's agreement on maintaining skepticism towards such measures.

What Should Specialists Trust When Setting Bonuses?

The Challenge of Metrics in Performance Evaluation

The speaker reflects on the legacy of management thinkers like Goldratt and Deming, emphasizing the importance of understanding metrics in performance evaluation. They question how specialists can trust certain metrics when making decisions about bonuses.

A disagreement arises regarding the reliance on a single metric for performance bonuses. The speaker warns that focusing solely on one KPI could lead to meaningless outcomes.

Technical issues interrupt the discussion, but the focus remains on the implications of using only one metric for evaluating sales performance.

Understanding Context in Metrics

The conversation highlights that metrics should not be taken out of context. It is crucial to understand how data was collected and under what circumstances it was formulated to ensure its reliability.

If sales performance is evaluated solely based on revenue generated, it may encourage low-quality sales practices, such as selling overpriced or undesirable products.

Adding Quality Measures

Introducing additional metrics beyond just revenue—such as product portfolio distribution or customer satisfaction—can provide a more comprehensive view of sales effectiveness.

By incorporating quality measures alongside quantity, organizations can reduce manipulation opportunities while still recognizing creativity among employees.

Flexibility in Performance Systems

There is an acknowledgment that systems can be manipulated; thus, flexibility is essential. Organizations must adapt their KPIs based on corporate culture and individual employee characteristics.

Planning for potential changes in measurement methods or metrics is vital to maintain relevance and effectiveness over time.

Transitioning to New Motivation Systems

A hypothetical scenario raises questions about how long it takes to implement new motivation systems within organizations. Employees often resist change due to comfort with existing rules.

Experiences shared indicate that transitioning processes can take months or even years, depending on business scale and employee feedback integration into new systems.

The speaker expresses concern about adapting business strategies amidst changing environments and emphasizes the need for agility in response to market dynamics.

Understanding Metrics in Business Management

The Importance of Relevant Metrics

The speaker emphasizes the need to focus on metrics that directly support achieving business goals, particularly profitability.

Changes in contract dynamics are highlighted, stressing the importance of adapting management strategies based on metric insights tied to employee motivation and performance.

A discussion arises about the selection of metrics; it is crucial to determine who decides which metrics to use and how they should be calculated.

Responsibility for Metric Selection

Questions are raised regarding who is responsible for choosing the methodology behind metric calculations—whether it's HR or upper management.

The conversation shifts towards understanding the purpose behind measuring specific metrics, such as turnover rates, and how these relate to broader organizational objectives.

Methodology Based on Goals

The choice of methodology depends heavily on the specific goals set by leadership; clarity on what needs measurement is essential.

If a metric aims to compare against market standards, it’s vital to adopt established methods rather than creating new ones without justification.

Data Availability Challenges

There may be limitations in data availability that affect how metrics can be calculated accurately; this necessitates careful consideration when selecting which metrics to track.

Specific examples illustrate challenges in tracking recruitment timelines due to potential gaps in data collection processes.

Resource Allocation for Metrics

Effective metric calculation requires adequate resources and systems; relying solely on manual processes can lead to inefficiencies and inaccuracies.

Ultimately, the choice of metrics must align with available resources and capabilities within the organization, ensuring that efforts are both practical and effective.

Understanding Employee Turnover Metrics

The Importance of Context in Metrics

It is suggested that employee turnover metrics should not include those who have relocated to another city, as this does not reflect true turnover but rather a change in circumstances.

Leadership must be cautious when linking metrics to salary; it’s essential to find ways to adjust metrics with minimal effort without altering processes.

Challenges in Data Collection and Interpretation

A critical point raised is that no metric comes without cost; understanding the implications of data collection is vital.

Most metrics require investment, whether financial or emotional, highlighting the need for automated data collection systems to ease the burden.

Calculating Turnover Rates

An example was shared about calculating turnover rates for a tendering employee, emphasizing the importance of accurate data for compliance with tender requirements.

The speaker noted discrepancies in calculated turnover rates due to rapid company growth and varying definitions of what constitutes turnover.

Common Errors in Data Handling

Discussion on frequent mistakes made during data collection and interpretation was initiated, particularly regarding how errors can stem from negligence or incorrect source selection.

Emphasis was placed on ensuring reliable sources are used for data gathering; relying on hearsay can lead to inaccurate conclusions.

Misinterpretation Risks

Comparing different groups inaccurately (e.g., small vs. large teams) can skew turnover statistics significantly, leading to misleading interpretations.

Many errors arise from misinterpreting context; extracting metrics without understanding their background can result in flawed conclusions.

Sales Growth Post-Training: A Case Study

Client Success Story

A client shared their experience of significant sales growth for specific products after attending a training session. The increase in sales was described as "multiple times" compared to pre-training figures.

Seasonal Context Consideration

The discussion highlighted the importance of considering seasonal factors when evaluating sales data. For instance, the training occurred in November, and December typically sees increased gift purchases, which could skew results. This emphasizes the need to account for context in metrics analysis.

Metrics and Contextual Analysis

It was noted that while comparing sales before and after training is common, failing to consider external factors like seasonality can lead to misleading conclusions about the effectiveness of training programs. Understanding these nuances is crucial for accurate assessment.

Business Goals and Responsibilities

The conversation shifted towards business objectives, particularly focusing on maintaining or increasing revenue while minimizing losses. There was an emphasis on understanding who holds responsibility within organizations for achieving these goals and how they can avoid common pitfalls in performance evaluation.

Accountability in Organizations

Questions arose regarding accountability within companies—who should be responsible for outcomes? It was suggested that responsibility lies with knowledgeable individuals rather than a standardized answer applicable across all contexts. This highlights the complexity of organizational dynamics and decision-making processes.

Challenges with Data Interpretation

Misinterpretation of Data Trends

An anecdote was shared about a corporate university's focus on numerical data leading to panic over minor fluctuations without understanding their significance or context, illustrating a common issue where decisions are made hastily based on incomplete information.

Statistical Significance Awareness

The concept of statistical significance was introduced as critical yet often misunderstood in business settings; there’s ongoing debate within scientific communities about proper data handling methods that many practitioners may not grasp fully, leading to erroneous conclusions from data analysis efforts.

Methodology Confusion

A participant expressed frustration over being labeled as having "methods" when discussing statistical techniques, indicating a broader issue where individuals lack clarity on methodologies taught versus those applied practically in real-world scenarios post-academic education.

Educational Gaps in Management Practices

Disconnect Between Education and Application

There’s a notable gap between academic learning (e.g., sociology) and practical application in management roles; graduates often find themselves unprepared for real-world challenges due to outdated or irrelevant educational content that doesn’t align with current industry needs.

Recognition of Statistical Concepts

Participants acknowledged that terms like "statistical significance" rarely resonate with professionals outside academia; this reflects a broader trend where essential statistical tools are underutilized or misunderstood within management practices today, limiting effective decision-making capabilities among leaders.

How to Implement HR Metrics in Companies Without a Data-Driven Culture?

Understanding the Role of HR Metrics

The discussion begins with the importance of being useful and interesting to listeners while engaging in educational activities related to HR metrics.

A scenario is presented where an HR professional enters a new company, facing immediate numerical goals set by leadership regarding employee turnover and satisfaction.

Challenges in Establishing Metrics

The speaker questions how to implement HR metrics in organizations lacking a data-oriented culture, emphasizing the need for understanding existing cultural attitudes towards data.

It’s crucial to assess whether there is already a culture of measurement within the company or if numbers are arbitrarily assigned without proper context.

Importance of Contextualizing Goals

The speaker stresses that when specific targets like reducing turnover from 25% to 23% are set, it’s essential to understand their origins and relevance to broader business objectives.

Often, HR professionals may not be aware of strategic goals due to lack of access or communication barriers with upper management.

Navigating Organizational Dynamics

There can be situations where an HR professional feels excluded from strategic discussions, leading them to question the validity and purpose behind certain metrics.

The conversation highlights the necessity for clarity on why specific goals are established and how they align with overall company strategy.

Addressing Leadership's Uncertainty

A more concerning situation arises when leaders themselves do not have clear answers about organizational direction or goals, leaving HR professionals uncertain about their roles.

The speaker suggests that instead of merely complying with arbitrary metrics, an innovative approach could involve taking initiative and proposing strategic sessions for alignment on objectives.

The Illusion of Certainty Through Numbers

There's a critique on how numbers can create an illusion of certainty; they often lack real-world correlation unless properly contextualized within business strategies.

The discussion concludes by emphasizing that mere numerical values should not replace meaningful insights into organizational performance and objectives.

Discussion on Metrics and Human Behavior

Understanding Perception of Data

The speaker discusses how people often struggle to think critically about data, focusing instead on numerical changes without understanding the underlying reasons for those changes.

A reference is made to a general's experience with praising performance; the speaker contrasts this with their own observations that praise can lead to worse outcomes in subsequent performances.

Regression to the Mean

The concept of "regression to the mean" is introduced, illustrating that when individuals are praised for good results, they may perform worse next time due to pressure or expectation.

The speaker emphasizes that negative reinforcement (e.g., criticism after poor performance) can paradoxically lead to improved future results.

Misunderstanding Statistics

There’s a discussion about how many people view statistics as superstitions rather than tools for understanding processes, leading to misguided decision-making based on flawed interpretations of data.

Soft Metrics in HR

The conversation shifts towards measuring intangible aspects like corporate culture and team morale, questioning whether HR metrics can effectively capture these elements.

A dialogue ensues regarding operationalizing soft metrics such as employee engagement and burnout levels, highlighting the complexity of quantifying these factors.