By Sultan Saidov, Co-founder and President, Beamery & Jeet Mukerji, Product Manager, Beamery
In this article, Sultan Saidov Co-Founder and President and Jeet Mukerji Product Manager of Beamery describe how Beamery started as a candidate relationship management system and shifted to adding talent intelligence capabilities. Saidov and Mukerji uncover why Talent Intelligence is important and how to make meaningful decisions with data from multiple sources. The article then reveals if talent intelligence is a new, up-and-coming vendor category. Saidov and Mukerji conclude with practical applications of talent intelligence and predictions for the future.
Overview of Beamery
Beamery has been a mission-driven company since its inception. Our goal, from the beginning, has been to eliminate the ‘passport lottery’ – the idea that the place that you are born, becomes your destiny – and give every person the opportunity to find the right career, education, and healthcare. It’s a crucial part of the journey to the eradication of poverty and a better world.For companies to transform, @TalentTechLabs says #talentacquisition and #talentmanagement need to be more strategic, more agile, and more efficient than ever before: Click To Tweet
Giving every person this opportunity begins with making talent transformation a central issue for every business. Beamery’s Talent Operating System is the platform that organizations can rely on to drive this strategic transformation – our software enables the world’s largest companies to attract, engage and retain talent at a global scale.
For companies to transform in this fashion, and build towards the future of work, talent acquisition and talent management need to be more strategic, more agile, and more efficient than ever before. Organizations need the right skills to succeed today and tomorrow. They need to uncover which of those skills exist internally, and which they are missing.
Beamery’s platform helps companies make sense of their talent data. The platform provides a unified foundation of enriched, clean, and standardized data. Beamery can help companies tackle their talent challenges – whether the priority is attracting and pipelining passive talent, launching new internal mobility programs, driving DE&I initiatives, individualizing talent experiences, connecting workforce plans to business objectives, and reporting across the entire cycle to make better decisions.
What is Talent Intelligence?
To define Talent Intelligence it’s useful to separate out information, intelligence and insight first. For example, if the information is a list of candidates and jobs, intelligence could be as simple as matching candidates and jobs using signals of potential and intent to identify fit. But insight comes from understanding why the specific combination of potential and intent makes for a good match, and that requires a system (or talent team) to have a deeper understanding of each position and each person.
Talent Intelligence is a stepping stone to insight, but it’s in danger of being interpreted as an end in and of itself. At Beamery, we believe it’s a way to achieve more informed and impactful experiences for candidates, recruiters, employees, and talent managers. This has to start with high-quality information (i.e. good data) in the system—and without data that’s standardized, complete, fresh, unique, valid, and usable… meaningful intelligence can’t be derived.
We think of Talent Intelligence less as a noun, and more as a verb—of cleaning the data, turning it into something that’s meaningful, and then using it to derive insights and enhance experiences.
From CRM to Talent Intelligence
Talent intelligence has been a core part of what we do from the start—whether that’s helping recruiters to be proactive with suggested tasks or scaling their impact with our automation engine and Chrome extension. Applying machine learning capabilities to our platform has been a natural evolution of how we improve experiences further, especially as we’ve grown from a CRM to a Talent Operating System with use cases for creating greater value in transformation initiatives like internal mobility and talent planning.
We’ve always been intentional about innovation, rather than touting fix-all AI features just for the sake of keeping up with the Joneses. Many of talent operations’ foundational problems have been solvable without AI—like connecting fragmented technology stacks for real-time data share and seamless workflow handoffs between applications. The industry is now starting to look more acutely at gaps in legacy processes and how to connect big ideas to plans and execution more consistently.
Areas like improving recruiting impact by extending talent pipelines to include internal, external, contract, and gig talent lend themselves well to solutions with AI. Breaking through operational silos requires better connectivity, more collaboration, and faster decision-making—and process automation coupled with deep learning can rapidly scale the otherwise limited bandwidth of talent teams to do more without getting bogged down with all of the heavy liftings.
Data, Blessing, and Curse
It’s incredibly hard to make and act on decisions as a talent organization with incomplete, out-of-sync data. It creates an unfortunate cycle where talent teams can’t see the data holistically to make informed decisions, and fill the gap with more tools that may not be necessary—it’s the reality for many.
This generates more disconnected data, which adds to the confusion. It’s like the story of the blind men and the elephant, where each person attempts to describe an elephant after touching a different part of it. You’re bound to get misaligned teams and subpar experiences. The issue isn’t having different, specialized systems—it can often be better than having a monolithic system that tried to do everything.How can #TalentIntelligence help you modernize your #talentacquisition and #talentmanagement strategies? @TalentTechLabs breaks down the approach in their latest blog: Click To Tweet
In our experience, the foundational issue isn’t a lack of “AI” but rather the need for a unifying data platform that connects these systems together standardizes the information, and creates a common language across the ecosystem of tools. With a solid data foundation, the information talent teams need can be more easily collated and consumed for meaningful decision-making.
Talent Intelligence: A Feature or A Category?
We’ve primarily seen talent intelligence used as a proxy for artificial intelligence to date. Despite the number of solution providers touting their offerings, AI is still relatively new in our industry. When organizations weigh up who to partner with, it can be tempting to categorize companies into those who claim to have it as a core part of their product and those who do not for ease of assessment.
In reality, this is not much different than treating AI as a feature where vendors either “have AI” or they don’t. This oversimplifies the question of what capabilities are available and leads many to make the wrong assumptions when trying to choose a vendor who will act as a partner.
To be clear, AI has the potential to transform the industry—but it isn’t an out-of-the-box solution. Rather, it’s a differing set of capabilities that talent teams can leverage to solve some of their myriad problems. AI features alone are not effective when not delivered in intuitive interfaces and running on top of good quality data. The data underneath, the user experience, the implementation, and adoption all matter just as much as the algorithm your vendor has on offer.
So what’s more pertinent is choosing a partner who can clean and contextualize data across systems, and use this data for relevant AI applications embedded in easy-to-use, well-implemented and well-adapted solutions.
Practical Applications of Talent Intelligence
Every interaction and experience can be improved with better data applied in smarter ways—which is probably why there are so many vendors in the market touting their unique solutions.
In our experience, Talent Intelligence is particularly helpful for problems around personalization, recommendations, and predictions which can be applied across use cases. For employees, this means individualized career paths and recommendations on career growth based on their goals. For talent leaders, this means visibility into the capabilities they have now and the ability to predict how they can fill future roles based on their workforce potential. For recruiting teams, this means activity informed by not only a candidate’s potential but also their intent—their likelihood to engage, move and accept an offer.
Candidly, many talent teams aren’t quite there yet. They’re still in Phase 1 of talent transformation, focused on adopting more modern tools and processes, optimizing their tech stacks, and improving data integrity. For those slightly ahead, they’re utilizing better reporting capabilities to identify gaps in talent pipelines around things like diversity and critical skills. Phase 2 of talent transformation strategies are looking beyond time-to-fill metrics to focus on more future-proof KPIs, and talent intelligence will likely accelerate maturity at this stage. We just have to get the foundations in place first.@TalentTechLabs believes that every interaction and experience can be improved with better #data applied in smarter ways. See how you can leverage #TalentIntelligence to better your #TA and management strategies: Click To Tweet
Predictions For The Future of Talent Intelligence
The way we work has changed, fast-tracked by the COVID-19 pandemic, and is likely to continue to change and become more fluid. Companies are more intently shifting their focus to finding potential in talent externally and internally as they look to build up their organizational agility. As you might expect, this has highlighted a persistent problem where we’re increasingly inundated with scattered data and a growing number of talent tools. These operational challenges are making the shift to new ways of working more difficult.
That’s why we believe so strongly that in this changing, complex environment, the solutions that will be most impactful are those that bring the tools together coherently and contextualize the data as a foundation for talent experiences.
A prime example of this trend is the increasing use of skill and role taxonomies to bring order to data, which has meant that talent organizations have somewhat benefited from using a setlist of static keywords. In the next five years, we expect that forward-thinking vendors will have to go far beyond semantic keyword associations to more readily capitalize on knowledge graphs to add context to those words, allowing us to personalize, predict, and recommend more meaningfully, and in turn deliver more dynamic, agile experiences. This transition to graph-based talent intelligence has already begun, and over the next few years, this should mature.
Leveraging Tools to Enhance Decisions For Hiring and Strategic Planning
Before moving ahead with a vendor, dig deeper into how much importance they put on data quality and how they maintain it. If you’re considering going all-in on transformative strategies, good data is not only fundamental to better decisions, but also to the adoption and efficacy of talent intelligence tools.
So go further into the client references, ask to talk to a Head of Talent Operations, and understand how the tool has been adopted, what outcomes it has driven, and how their vendor partnered with them during and after implementation to ensure success from the start.
And, of course, it’s critical to consider how technologies that utilize talent data handle bias. Obfuscating a candidate’s details is not enough if the underlying algorithm or training data set holds bias. Look into how a vendor defines bias, as there can be different types. Evaluate their anti-bias measures—both machine and human—put in place from data gathering to modeling to delivery to learning.
There should be reasonable explanations throughout the AI delivery pipeline for how and why the AI is built. If that is not there, they are unlikely to be the right vendor for you.