Why You Need A Unified Talent Data Ecosystem – And How To Do It
Large companies know that, in order to stay competitive, they need smarter ways to attract, manage and develop talent.
But, for many, fragmented data systems get in the way, creating barriers to visibility and making it harder to make good decisions, quickly.
A unified talent data ecosystem can break down these barriers, bringing all your people and skills data together in one place so teams can work towards shared goals more efficiently and effectively, and everyone can see the bigger picture.
This article walks through how to shift from disconnected systems to a unified approach that empowers your people... and drives real business results.
First: Why does traditional talent data management fall short? 🫤
Most organizations rely on separate systems for every aspect of talent management: recruitment, learning, performance management, and HR operations. That’s not a problem in itself, but these systems often lack any ability to speak to each other – and, crucially, they don’t use a common language.
Disconnected systems lead to data duplication, outdated records, and incomplete profiles – inaccuracies, in other words. This isn’t just an inconvenience: it means you risk issues with compliance, productivity, and even talent quality.
Decision-makers who lack timely access to the insights they need will make misguided decisions. This means “bad talent data” is a critical business issue… not just a headache for HR.
A unified talent data ecosystem – ideally based around skills – can change this, creating a foundation for fast, accurate, and impactful talent decisions across the organization.
The importance of a skills-based approach 💗
As you will see below, a crucial step in unifying data sets is a common language or taxonomy. At Beamery, we recommend rethinking, restructuring and standardizing your talent data via the language of “skills”.
A unified language eliminates inconsistencies and enhances interoperability across systems, facilitates better decision making, and helps promote mobility, upskilling and reskilling initiatives.
But it’s more than just “ease of use”. Standardizing data around a common language of skills makes every decision fairer, reducing bias in talent management, and widening the pool (without lowering the bar).
Also: AI systems depend on high-quality data; a standardized skill taxonomy enhances their ability to generate insights and predictions (such as where to focus upskilling efforts, based on the biggest gaps).
“Folks are speaking the same language. Folks are equally focused on employee experience as well as… moving away from fragmented infrastructures or things where it’s disjointed or data is not connected. So that’s a lot of progress that really helps us. We spend less time convincing and more time solving.” – Ash Walvoord, Verizon
The benefits of a unified talent data ecosystem 💝
Before we dive into the ten steps for creating a unified talent data ecosystem, let’s take a look at some of the positive outcomes for your business....
Better quality hires
Talent data based around skills means you can match people with opportunities. Skills matching aligns employee skills with job requirements, adapting over time as skills evolve.
By tracking skill development automatically, organizations can more effectively connect talent with opportunities, ensuring each hire brings the right strengths to their role.
Improved “Build, Buy, Borrow” decisions
With unified skills data, HR teams can make smarter hiring decisions: working out whether they should be building internal talent, buying external hires, or borrowing resources.
This clarity helps match employees to both long- and short-term roles effectively, ensuring resources are aligned with business needs.
Higher retention and productivity rates
Accurate, up-to-date data about employees helps you build out personalized development paths, aligning with employees’ skills and goals to foster growth. When leaders can see areas for growth, they can tailor development programs and mentorship to empower employees to excel in their current roles or prepare for new ones.
Day to day, a unified (integrated) talent data ecosystem also saves time. If HR professionals know they can trust the data they are seeing, regardless of the tool they are using, they won’t spend time switching between them – and they certainly won’t have to spend time updating various systems or going looking for missing information.
More strategic workforce planning
Put simply, unified skills data makes it easier for organizations to anticipate talent needs and identify skill gaps.
With a comprehensive understanding of internal skills and external market trends, companies can make strategic decisions – like expanding into new markets or restructuring – with increased agility and insight.
Improved compliance and reduced risk
A unified talent data ecosystem provides accurate, up-to-date records of employees’ skills, certifications, and training, helping organizations stay compliant with industry regulations.
This proactive approach reduces risks related to non-compliance, ensuring employees meet necessary standards and enhancing organizational security and reliability.
Stronger collaboration
A project like unifying talent data naturally requires teamwork and collaboration – but it also results in teamwork and collaboration. Once every part of the HR team (and indeed the wider business) is on the “same page” when it comes to their talent pool, and how that relates to jobs and future skills needs, working together towards common goals becomes a cinch.
“The natural ecosystem that arises when you start investing in skills data, and when you start getting really curious about skills data and how it applies, not just to the one function that you may be started with... And it is, I think, such an interesting catalyst as well for change in culture across the organization.” – Betsy Summers, Principal Analyst, Forrester Research
Creating a unified talent data ecosystem in 10 steps 🧱
So how do you actually do it? Building a unified talent data ecosystem requires a strategic approach to identifying, integrating, and streamlining critical data sources.
1. Define Your Talent Objectives
Identify your goals for the updated approach. Are you focusing on recruitment, internal mobility, workforce planning, or all of the above? Are you hoping to bring certain teams into alignment, or even restructure your HR setup? Do you want to find efficiencies, or fully overhaul the talent experience?
All of this is possible. A joined up system can lead to more joined up ways of working, and tends to bring the talent lifecycle into clearer focus and alignment (for talent teams AND for talent itself).
2. Map Existing Data Sources
Conduct an inventory of all current talent-related data sources, such as HRIS, ATS, talent CRM, Learning & Development systems, and performance reviews.
While it’s not strictly a source of “talent data”, look at your job architecture(s) and consider if you can bring this into the mix as well. Do you describe work in the same way in job descriptions and job adverts, for example?
3. Adopt A Skills Framework
This step is critical, and it represents quite a substantial change in how most organizations think about human capital allocation.
Standardize data around a common skills taxonomy or framework – one that helps you organize people (your entire talent pool) and work (the way you describe jobs) through the lens of skills.
As mentioned, this will not only make it easier for systems to “speak” to each other, it will facilitate fair and effective “matching” between people and work-related opportunities. This is crucial if you want to fill skills gaps efficiently, and correctly.
4. Bring Data Together
Choose a centralized platform where all talent data can be stored and accessed securely. Make sure data can be organized, accessed, and analyzed in one place, reducing silos and enhancing data integrity.
AI can be a huge help here, allowing for data cleaning, deduplication, and more sophisticated analysis that deliver more strategic insights (insights, patterns and inferences that a human might miss).
Rather than investing in yet another standalone HR tool, consider how you get accurate, dynamic and comprehensive insights – a single source of truth for candidate, employee, alumni and talent community data – within a tool you already use.
By taking an ecosystem approach, you can integrate processes across systems, such as your ATS, performance tools, CRM, and L&D platforms. Then, with an always-on Job Architecture, you can effectively create a “data layer” of skills intelligence for your talent teams. (Hint: Beamery does this…)
With a single source of truth for information related to people and roles (even if this data comes from a range of places), you can better manage workforce plans and talent-related tactics, as they relate to business goals.
5. Develop Integration Capabilities
Implement integration tools or APIs that facilitate seamless data sharing across different platforms and departments.
If you use a middleware solution, it should be able to ingest data from your other tools, such as Workday or SAP, and use it to improve what is already in there, or feed systems like talent marketplaces – continually.
With automation in real time, you will get more value from existing tools, and speed up any new implementations.
Another thing you might wish to integrate or embed to build a competitive edge is insights about the wider labor market, such as which skills are on an upward trend or in decline. This will make it even easier to spot skills gaps, prioritize roles, optimize hiring efforts, and build out location strategies.
6. Implement Data Quality and Standardization Protocols
Establish data governance practices and protocols to ensure high-quality, consistent data.
Standardized data enhances the accuracy and reliability of talent insights, which is of course crucial for decision-making (and predictive analytics).
Consider how you can gather information on people’s skills, experiences and aspirations at the same time as getting their consent and data usage preferences, to enhance data quality while aiding your compliance efforts.
Talent data must be:
- Searchable based on things that are important, like skills (not just job titles)
- Accurate and always up to date
- Collaborative and useful to those that need it
- Owned by the business, not by recruiters and managers
- Actionable, with users able to make decisions (and take next steps) with the information
- Contextual, so people can understand the full history for a contact, and understand their skills in relation to your specific organization/industry
- Measurable, to show how ‘healthy’ your talent data is
7. Incorporate Skill-based Assessments and Analytics
Embed assessments and analytics that measure skills at both individual and organizational levels.
Skill-based assessments provide objective data on employee capabilities, making it easier to map talent to opportunities or upskilling needs.
Uncovering these skills gaps is one of the most important use cases of unified talent data ecosystems, helping you take pre-emptive action and avoid risk to the business.
8. Use AI to Enhance Skill Mapping and Matching
Leverage AI and machine learning to analyze skills data, match candidates with roles, and identify internal mobility or development options.
AI identifies skill adjacencies, and can be more accurate in terms of matching people to opportunities than a human.
It’s certainly a lot faster, and more flexible – if you tweak someone’s profile or a role description, for example, AI-powered tools should be able to show you instantly how the matching results would change.
9. Enable Self-Service for Employees and Managers
Joined up, accurate talent data is great, but unless it can be easily found and applied, the hard work in bringing it together will not be worth it. The examples above show how HR can benefit from actionable skills insights, but can you provide access beyond the People function?
Build interfaces that empower employees to access and manage their own data, such as skill profiles, career paths, and job opportunities, and help managers see how they might upskill their teams to meet emerging requirements.
Self-service features encourage employee engagement, giving them autonomy in career planning and development, while bringing data into the platforms your leaders already use – think Teams or Slack – makes it even more likely that they will be able to make solid decisions, quickly.
“The fundamental thing was just making sure that we’ve got some data that everybody can access that’s clearly visualized and that we know is accurate.” – Lara Farrell, Hilti
10. Continuously Refine and Scale
Regularly evaluate the success of taking an ecosystem approach, monitoring user adoption, gathering feedback, and making iterative improvements.
A talent data ecosystem is a dynamic structure that needs continuous refinement to stay aligned with changing workforce needs and business goals.
How Beamery Helps…
Speak to Beamery about creating a unified talent data ecosystem – true skills intelligence that can power your talent planning (and all your workforce decisions later on).
With Beamery you can:
- Connect multiple data sources together. We sync high volumes of data in near real-time.
- Clean, normalize, and dedupe data from different systems, to generate an accurate, consistent, rich data set.
- Get accurate, streamlined data folded into the flow of work, for relevant decision-makers and stakeholders to collaborate and take action with confidence.
- See insights presented in a role-relevant way, integrated with key HR systems to enhance adoption and productivity – and strengthening the value of your existing HR ecosystem.
- Automate workflows and processes that empower speed and accuracy.
- Avoid risk, with safeguards that support compliance processes.