Transform 2021

Elevate your enterprise data technology and strategy.

July 12-16

Cloud-based analytics platform Visier, which focuses on human resources (HR) and workforce strategy applications, today announced that it closed a $125 million series E round led by Goldman Sachs at a post-money valuation of over $1 billion. With the investment, Visier joins a small list of HR technology providers that have raised more than $100 million in a single funding tranche.

Visier, whose total capital raised stands at $219.5 million to date, will use the proceeds to support product development, go-to-market expansion, marketing, sales, partnerships, distribution channels, and strategic acquisitions “as they make sense for the business,” CEO Ryan Wong says. He claims that Visier is the first independent vendor of its kind to reach a $1 billion valuation.

Companies are in the midst of digital transformations due to increased demand for better people analytics. People analytics, also known as talent analytics or HR analytics, refers to the analyses that can help managers make decisions about their employees or workforce. While people analytics is a new domain for most HR departments, 70% of company executives cite people analytics as a top priority, according to McKinsey. As for Wong, he argues that the rapid shift to a remote workforce brought on by the pandemic has made the need for people analytics more apparent.

Founded by John Schwarz and Wong in 2010, Vancouver, Canada-based Visier, which has around 417 employees, is designed to integrate data from disparate sources. It onboards data using prebuilt connectors for existing systems, letting customers build their own data integrations, import bulk data, and export and query data as well as transform data into analytics models.

Above: Visier’s executive dashboard.

Image Credit: Visier

“Visier was founded 11 years ago by a group of us who had spent the majority of our career pioneering business intelligence, first at Crystal Decisions and then, through a series of acquisitions, at Business Objects and SAP,” Wong told VentureBeat via email. “Visier was founded on the belief that the analytics problem needed to be solved differently — not as a general-purpose platform, but a series of purpose-built apps that provide much faster time to value, much lower total cost of ownership, and access to the sort of insights that managers and executives need to make better decisions and run their businesses.”

People analytics

Companies are increasingly struggling to apply data strategies to their HR operations. A Deloitte report found that more than 80% of HR professionals score themselves low in their ability to analyze, a troubling fact in a highly data-driven field.

Visier seeks to address this by providing access to more than 2,000 out-of-the-box analytics modules that can be embedded in websites, apps, and portals. On the backend, the platform preps data for analysis, filling in the voids and creating derived data attributes.

“People data is notoriously messy, complex and challenging to analyze,” Wong said. “Visier has a number of powerful capabilities — connected to our proven analytical model for people data — that directly solve key people challenges for our customers spanning areas like diversity, retention, rewards, and recruitment.”

Visier has more than 12 million employee histories across 75 countries loaded into its platform, which has enabled the company to create benchmarks that span multiple industries and predictive models.

“Companies use Visier to understand and improve their workforce at every stage of the employee lifecycle — from recruitment to retirement. Visier is designed to answer virtually any question HR, people managers and executives need to ask about their workforce to improve diversity and inclusion, performance and productivity, employee retention and happiness, and to more effectively plan hiring and career progression, and manage people and teams,” Wong said. “Our product helps answer questions like: do we have the people and skills we need to meet our goals? Are we managing our workforce responsibly and sustainably? Are diverse individuals represented at all levels of the workforce? Are we prepared for what’s to come?”

Visier isn’t without competition in the global HR analytics market, which was estimated to be worth $2.49 billion as of 2020. ChartHop recently closed a $35 million funding round, and rivals CoachHub and Hibob are similarly well-capitalized. There’s also workplace analytics startup VergeSense, OKR-tracking platform WorkBoard, employee evaluation tool 15Five, and Gloat, a career development marketplace for knowledge workers.

But Visier has a strong foothold in the segment, with a customer base of over 8,000 brands including Uber, Adobe, Bridgestone, Wayfair, and EA.

“There have been plenty of negatives that are similar to any company operating during [the pandemic]. The difference for Visier is that, in a way, we’ve also benefited from it because we’re providing a solution to many of these challenges,” Wong said. “The way I see it is that the pandemic just amplified and accelerated what was already happening: employees have new expectations for how, where, when and why they work. The pressure is on for companies to know and understand their employees better than ever before and this requires the sort of data and insights that Visier provides.”

Transform 2021

Elevate your enterprise data technology and strategy.

July 12-16

Presented by KPMG

AI is more hype, less reality, say three-quarters of the executives surveyed in a 2021 study by KPMG, “Thriving in an AI World: Unlocking the Value of AI across 7 Industries.” And half think AI is moving too fast in their industry, even as they wish their company was moving faster. The hurdles to implementing AI are high, from a fundamental misunderstanding of what AI actually is and what it can do, to the significant lack of expertise available to help AI-curious organizations get their footing, says Dr. Ellen Campana, head of enterprise AI at KPMG LLP.

“The overhype is a real concern,” Campana says. “There are a lot of people trying to get into the game. Many have had a bad experience along the way, because they have put their trust in a group or person that didn’t have a lot of experience with AI, or they didn’t have a clear understanding of what to expect when launching an AI program.”

At least some of the hype is warranted, says Campana. According to KPMG research, AI is becoming ubiquitous — 92% percent of the execs that have implemented the technology in their organization believe in its ability to deliver value and make their organization run more efficiently. There is clear confidence that AI has the potential to solve some of their industry’s biggest challenges. Yet there are still long-standing hurdles to overcome.

The long-term impact of the hype cycle

This isn’t the first time people felt that AI was moving too fast and too much was being promised, Campana says. The term ‘AI Winter’ was coined as a way to describe those periods of boom and bust, specifically when interest in artificial intelligence faded during the technology’s long history — the last was in the 90s, during the dot-com boom.

Despite the public discourse, work has never stopped on advancing the technology. In 2012, breakthroughs in machine learning began to renew interest in the tremendous potential of AI. But those earlier hype cycles have had a long-term impact, Campana says, leaving a painful dearth of experienced and knowledgeable AI experts.

“Because the training programs were shut down due to that AI Winter, the people who are trained to do this work are scarce,” she explains. “It feeds into the current sense of things being overhyped, because there’s not a lot of people with deep training, but there’s a big market demand for it. This leads to a lot of variability in the advice people are hearing.”

Many of those companies that are having unfortunate experiences with AI now have unrealistic expectations about the technology — they haven’t been educated about what to expect. Campana points to the surprisingly common idea that AI is something that you install, load up, and run.

“People expect things to happen quickly, and seem to believe and expect that AI will just know things,” she says. “Improving literacy of these AI systems and improving people’s knowledge about what can and can’t be done is key.”

The real promise of AI, and the risks

Due to major breakthroughs in business platforms and tools, AI is prevailing across industries, Campana says.

“AI excels when you find a way for the human and the computer to collaborate efficiently,” she says. “If you divide the tasks based on the characteristics that each participant is good at, then there’s a lot of promise that together, people and machines can do a lot more.”

Campana and her colleagues have been engaged in a broad variety of AI projects in their work at KPMG. To address the impact that COVID-19 has had on the supply chain, they’ve been using AI to reorganize and reconfigure the food distribution system and optimize it despite disrupted supply chains and changing markets.

In technology, they’ve been working on automating identification of commercial leakage in contracts, and for financial institutions, they’re applied AI to determine whether companies are in compliance with regulations.

Their AI solutions also deal with earnings calls in order to understand how companies are talking about their finances, the implications for stock market valuation, and whether companies are communicating accurately. In health care, they are helping to optimize customer experience at payer organizations which are being flooded with calls at an unprecedented level. And in education, conversational AI is helping to distribute the technology children need in order to keep learning, particularly during the pandemic.

The risk in implementing solutions like these, or the thousands of others available, is underestimating both the need to participate actively in developing the systems, and the need to find expertise, Campana says.

As well, people will sometimes listen to marketing from technology vendors rather than seeking out third-party intelligence. That can lead to spending money on the wrong technology, or investing in a system or vendor that doesn’t understand how to maintain and keep the system current. Or companies can run into a problem with their AI implementation because their vendor has told them they don’t need a particular component, but that may be because the vendor doesn’t provide it.

“Companies need to be aware of the difference between marketing and implementation,” Campana says. “They’re sometimes turning to a technology vendor for advice about strategy, which is not something to do. They should either develop their own strategy considering multiple perspectives or come and ask for help to develop strategies. But they definitely need to make sure that they have a strategy that’s independent of a particular vendor view.”

Implementing a hype-proof AI strategy

Nearly eight in 10 executives in the KPMG survey reported that AI is functional in their organization, and a majority who are using it say it’s delivering value beyond what was promised. But how does a CTO get company buy-in without stirring up the fear that that they’re overpromising, or buying into the hype?

Campana says that you can start small, with a proof-of-concept project if necessary. It’s important to know that it will need to scale, and to have a plan for how that can realistically happen.

For C-suite stakeholders, it’s critical to continuously monitor progress and provide reporting while offering concrete, practical evidence as the project evolves. That includes documenting performance improvements as well as outlining opportunities for improvement, all the while keeping executives informed of the iterative process.

Of course, that also includes impact on the bottom line: it’s vital to identify KPIs that are about money. For instance, documenting call deflection in conversational AI, or identifying commercial leakage in contracts, so the efforts can be tied back to the business value, even in the early stages.

“We’re not waiting until three months down the line when [execs] say, ‘We invested a million dollars in your AI initiative, what have you got to show for it?’” Campana says. “You have to show them the iterative improvement. They have to understand that it’s not something that gets installed and just works. It gets better because you teach it.”

In order for that to happen — for the AI to learn and get better –it’s imperative to get buy-in from internal teams and have them actively participate in the shift to AI. For instance, if you’re implementing an AI solution to help contracts for commercial leakage, the group that handles that issue needs to be actively involved in helping the AI learn what to look for — not just the vendor, and not just a consulting group.

“A consulting group can help accelerate and make things more efficient, and so can a technology team, but they will need the help of the people on the ground,” Campana explains. “They need to spend time making sure that those people are bought in and understand that the goal is to make their lives better, not to replace them.”

For all levels of the company, implementing AI successfully takes patience as well — or in other words, understanding that AI isn’t like a lot of other IT domains where you install the software, configure it, and then it just pretty much works. It is, instead, a process.

That requires the most important part of a successful AI implementation: AI literacy, or combating the long-term damage caused by the last AI Winter. That’s the most important piece of advice Campana has for her clients about realizing the promise of AI.

“You need to pay attention to data literacy and AI literacy from the bottom to the top of the organization as you begin your AI journey,” she says. “If employees know what to look for, they’ll know how they could delegate their least favorite tasks to a computer. That means innovation opportunities that drive a lot of efficiency gains, coming from the people who are doing the work.”

Dig Deeper: Read the entire 2021 KPMG study, “Thriving in an AI World.”

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