In our overview blog, we talked about the 7 challenges that organisations face when using multiple assessment tools. This kick-started a blog series that began with Part 1 of 7 on the challenges of dealing with multiple assessment vendors, followed by Parts 2, 3, and 4 of 7. Our blog today is Part 5, which covers the challenges associated with talent data scattered across multiple platforms.
Sheu Quen is a seasoned writer with a passion for talent management in the HR industry. With 8 years of experience in the field, she’s gained extensive knowledge in developing related content. Through her writing, she aims to share insights and best practices on how organisations can attract, retain, and develop their talent for sustainable growth.
For the effective execution of talent management processes like succession planning, HiPo identification, and internal mobility, organisations require a comprehensive view of all relevant talent data – performance metrics, feedback, psychometric tests, aspiration data, engagement scores, tenure, and experience.
However, all this data tends to be scattered across different systems and documents, creating a significant challenge for effective talent management. This blog explores the challenges of scattered talent data and its various failure points.
Use Cases and Failure Points
Data is everything. According to McKinsey, every aspect of work will be optimised by data by 2025. However, data is only useful when it is integrated, consolidated, and unified. After all, scattered data can make it challenging to extract insights and make informed decisions, leading to time-consuming and resource-intensive manual data compilation efforts by HR teams.
Talent acquisition is the basis of growth and relies on a solid foundation of applicant information, assessment results, interview feedback, and reference and background checks. However, these data sources may be scattered across different platforms, including:
- Candidate resumes are found on job boards, career sites, and applicant tracking systems.
- Application forms, which are collected through online application systems, applicant tracking systems, or manually through paper forms.
- Assessments, which are conducted through pre-employment testing software, which may be integrated with applicant tracking systems.
- Interviews, that are conducted through video conferencing software, phone interviews, or in-person interviews.
- Reference and background checks conducted through previous employers or third-party vendors that specialise in background screening.
This is a huge challenge that HR teams and hiring managers often struggle with when it comes to consolidating these data sources. As a result, this can lead to incomplete profiles, inconsistent evaluation criteria, and biased decision-making.
Effective succession planning prepares organisations for the future, hinging on key talent data, such as employee performance, potential, experience, aspiration and skill sets. However, these data sources may be scattered across various platforms, including:
- Employee history which are typically found in HR information systems or personnel files
- Employee education found in employee resumes or stored in learning management systems.
- Key projects, which are located in project management tools, performance management systems, or employee resumes.
- Employee performance data, which are collected through performance management systems.
- Job competencies, which are identified and defined through job analysis and job competency frameworks using individual documents.
- Career aspirations
- Potential, which are assessed through talent reviews, assessment centres, or 360-degree feedback processes.
Unfortunately, varied data sources can lead to inaccurate employee data, siloed decision-making, flawed succession plans, and compromised business continuity.
For companies to discover the hidden gems, HR teams must delve into performance metrics, behavioural assessments, and potential for growth that are aligned with the company values. However, these data sources may be scattered across various platforms, including:
- Manager recommendations, typically collected using emails or individual documents
- Potential, which is assessed through talent reviews, assessment centres, or 360-degree feedback processes.
- Performance collected through performance management systems or through regular check-ins and feedback sessions.
Alas, unconsolidated data can make it challenging for HR teams to holistically assess an employee’s potential for growth and development. Without reliable data, identifying high-potential employees may be based on subjective opinions rather than objective criteria.
Internal mobility unlocks new horizons for employees which depends on skills, experience, and preferences. In supporting this, several types of data are required to make informed decisions, including:
- Job boards and career sites
- Employee skills, aspirations, performance, and potential that are collected from different applications
- Job competencies, which are identified and defined through job analysis and job competency frameworks.
However, the absence of consolidated data can hamper this process. Scattered data can make it challenging for employees to access information about internal job openings. As a result, HR teams and managers struggle to identify suitable candidates for internal mobility programs.
Individual Development Plans
Crafting individual development plans (IDPs) charts the personal growth of employees, requiring insight into employee skills, performance, and career aspirations. However, these data sources may be scattered across various platforms, including:
- Employee performance data: These are typically collected through performance management systems or through regular check-ins and feedback sessions.
- Career aspirations
- Job competencies: these are usually identified and defined through job analysis and competency frameworks.
- Potential: This is often assessed through talent reviews, assessment centres, or 360-degree feedback processes.
- Development needs: These are identified through performance feedback, development plans, or competency assessments.
Unconsolidated data can lead to incomplete employee profiles, inconsistent performance metrics, and ineffective development initiatives. Ultimately, this causes a misalignment of goals and expectations between employees and the company.
Talent management decisions based on partial or outdated data can lead to unfavourable outcomes, such as hiring unsuitable candidates, overlooking HiPo employees, or promoting individuals who are not ready for new responsibilities. This means that HR teams need to spend a lot of time manually consolidating the data.
To tackle this problem, HR teams typically resort to manual consolidation, using tools such as PowerPoint presentations and spreadsheets. However, this approach is time-consuming, error-prone, and often leads to delays in decision-making.
Additionally, the allocation of time and resources to manually consolidate data takes HR teams away from other tasks, such as strategic initiatives and employee engagement efforts.
To find out more about how it can help your HR team produce valuable insights for accurate succession planning decisions, read our blog on The Need for Data Consolidation in Succession Planning.
Data-driven decision making leads to better employee engagement, improved productivity, increased revenue, and improved employee retention. That’s because talent analytics allows HR, hiring, and line managers to make better people decisions.
Talent assessment is a process that companies use to identify which candidate will perform the best and be the right cultural fit. It aims to predict a new hire’s on-the-job performance and how long they will work at the company.
In a nutshell, talent assessment is a process that companies use to identify which candidate will perform the best, and be the right cultural fit. It aims to predict a new hire’s on the job performance, as well as their retainability in the long term.
You might also like...