Why Data Quality and Governance Are Imperative for People Analytics Success
In today's data-driven world, organisations increasingly rely on data to make informed decisions. This is especially true in the realm of people analytics, where various analytical techniques are used to extract meaningful insights from employee and organisational data.
However, the foundation of successful people analytics lies in the quality of the data and robust data governance. Without these critical components, the insights derived can be misleading or even harmful. In this blog, we'll explore the importance of data quality and governance in people analytics, the challenges organisations face, and potential solutions to overcome these hurdles.
The Significance of Quality Data in People Analytics
Why Data Quality Matters
1. Accuracy and Reliability: High-quality data ensures that the analyses and subsequent decisions are based on accurate and reliable information. Poor data quality can lead to incorrect conclusions, negatively impacting strategic decisions.
2. Employee Trust and Engagement: Employees are more likely to trust and engage with analytics initiatives if they believe that the data used is accurate and reflective of their true performance and contributions.
3. Compliance and Risk Management: Accurate data helps organisations comply with legal and regulatory requirements, reducing the risk of penalties and reputational damage.
Challenges in Ensuring Data Quality
1. Data Silos: In many organisations, data is stored in disparate systems, making it difficult to achieve a unified and consistent dataset.
2. Incomplete Data: Missing or incomplete data can skew analysis results, leading to incorrect insights.
3. Inconsistent Data: Different departments may use varying definitions and standards for data, resulting in inconsistencies that can affect the accuracy of analytics.
Solutions to Improve Data Quality
1. Data Integration: Implementing data integration solutions that consolidate data from multiple sources can help eliminate silos and provide a single source of truth.
2. Data Cleaning: Regular data cleaning processes, including deduplication and filling in missing values, can improve data completeness and accuracy.
3. Standardisation: Establishing and enforcing data standards and definitions across the organisation ensures consistency.
The Role of Data Governance in People Analytics
What is Data Governance?
Data governance encompasses the policies, processes, and standards that ensure the effective management of data throughout its lifecycle. It defines who can take what action with what data, when, under what circumstances, and using what methods.
Importance of Data Governance
1. Data Security and Privacy: Robust data governance ensures that sensitive employee information is protected, and privacy regulations are adhered to.
2. Data Accountability: Assigning data ownership and stewardship roles ensures that there is accountability for data quality and accuracy.
3. Decision-Making Confidence: When data is governed properly, stakeholders can be confident in the integrity and reliability of the data used for decision-making.
Types of Data Governance
Figure 2. (Source: Insight222 People Analytics Trends Report 2022)
There are four key types of data governance:
1. Business Value: This type focuses on ensuring that data initiatives align with the organisation's strategic goals and provide tangible business value. It involves identifying data that can drive business decisions and improve performance.
2. Data Stewardship: Data stewardship involves assigning responsibility for data management and quality to specific individuals or teams. Data stewards ensure that data is accurate, accessible, and secure, and they act as the custodians of data assets.
3. Ethics: Ethical data governance ensures that data is used in a manner that is fair, transparent, and respects privacy. This includes adhering to legal standards and ethical guidelines to prevent misuse of data and to protect individual rights.
4. Project Prioritisation: This involves prioritising data projects based on their importance and impact on the organisation. Effective project prioritisation ensures that resources are allocated to the most critical data initiatives, supporting strategic objectives.
Sometimes, these elements come together in the form of a data governance council, a cross-functional team that oversees data governance activities, sets policies, and ensures compliance across the organisation.
Challenges in Data Governance
1. Cultural Resistance: Implementing data governance often requires a cultural shift within the organisation, which can be met with resistance.
2. Resource Allocation: Effective data governance requires investment in tools, technologies, and skilled personnel, which can be a challenge for some organisations.
3. Continuous Improvement: Data governance is not a one-time effort but requires ongoing monitoring and adjustment, which can be resource intensive.
Solutions to Enhance Data Governance
1. Clear Policies and Procedures: Developing clear, well-documented policies and procedures for data governance helps ensure consistency and compliance.
2. Training and Awareness: Regular training and awareness programs can help embed a culture of data governance within the organisation.
3. Technology Solutions: Leveraging technology solutions that provide data governance capabilities, such as data lineage, access controls, and auditing, can streamline governance processes.
Strong Data Foundations: A Must for Digital-First Organisations
Strong data foundations are not a nice-to-have; they are a must for any organisation striving to be digital-first. Without clean and reliable data that serves as the foundation for automation, insights, and AI tools, organisations will struggle to compete in today's fast-paced digital landscape. To achieve this:
1. Focus on Data Governance: Organisations must prioritise data governance initiatives to ensure that data is managed effectively and securely across the organisation.
Figure 3. The percentage of companies that report they have a data-driven culture for people data and analytics, 2021–2023 (Source: Insight222 research, Upskilling the HR Profession: Building a Data Literacy at Scale)
2. Build a Data Platform Strategy: With a myriad of platforms often in use, organisations need a cohesive strategy to manage their data platforms effectively. This includes integrating disparate systems and ensuring data consistency.
3. Conduct Regular Audits: Regular audits are essential to identify gaps and areas for improvement in data quality and governance practices.
Everyone's Responsibility
Ensuring data quality and robust data governance is not the sole responsibility of IT departments or data specialists – it is a shared responsibility that spans the entire organisation. It's important to note that this isn't just the responsibility of the people analytics team, although they should certainly be part of the process.
From an organisational standpoint, roles and responsibilities need to be thought about carefully to ensure everyone contributes to maintaining data quality and governance. From the top executives to every single employee, everyone has a role to play:
1. Leadership Commitment: Executives and senior leaders must champion data quality and governance, setting the tone and expectations for the entire organisation.
2. Employee Engagement: Every employee should be aware of their role in maintaining data quality and adhering to governance policies. This includes accurate data entry, timely reporting of discrepancies, and following established procedures.
3. Collaborative Effort: Cross-departmental collaboration is essential to ensure that data standards and governance practices are consistently applied and adhered to throughout the organisation.
Essential Data Governance for Successful People Analytics
As people analytics continues to evolve, the quality of your data and the robustness of your data governance framework are paramount. High-quality data is the bedrock upon which reliable insights are built, and effective data governance ensures that this data remains accurate, secure, and compliant.
By addressing the challenges and implementing the solutions discussed, organisations can lay a strong foundation for successful people analytics initiatives, ultimately driving better business outcomes and fostering a more engaged and productive workforce.
In the journey towards leveraging data science in people analytics, remember quality data and governance are not just technical necessities; they are strategic imperatives. Everyone within the organisation has a responsibility to uphold these standards, ensuring that data serves as a trustworthy and valuable asset in making informed decisions.
Strong data foundations are essential for any digital-first organisation, enabling automation, insights, and AI tools to thrive on a bedrock of clean and reliable data.
ABOUT THE AUTHOR
Jasdeep joined Insight222 in 2023 as a member of the products and services team. She brings a wealth of experience in data science, analytics, and client delivery having worked across a wide variety of industry sectors. After working for three years in Dubai, Jasdeep returned to the UK in 2019 where she established and led the People Science team at GE. She went on to develop an analytics client facing function within the analytics business at Workday. Jasdeep holds a Ph.D. in Applied Statistics from the University of Cambridge, and a Master’s in Mathematics & Statistics from The University of Sheffield. In her free time, Jasdeep enjoys travelling, dancing, trying different cuisines from around the world and following Formula 1.
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