Workforce Analytics: Disney’s Real-Life Fairy Tale

It was a numbers game at the HR Tech conference this year in Las Vegas.

I do mean numbers like lucky 7s, hard 8s and terrible 12s (more on my introduction to craps in a later post). But also a numbers game in terms of the growing exploration and adoption of workforce analytics.

A highlight at the industry trade show Oct. 8 was

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a session titled “Analytics Help Draw

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a Clear Talent Picture at Disney Animation Studios.”

Presenters Ann Le Cam, vice president of human resources and production management at Walt Disney Animation Studios, and Al Adamsen, president of consulting firm The Talent Strategy Institute, spoke about the way Disney has become data-conscious in the way it manages its creative workforce.

It’s a true-life fairy tale of sorts. The studio, with a rich history of producing the likes of “Snow White” among other treasured classics, hit hard times in the 2000s. Disney animators and managers struggled to make the shift from two-dimensional animated films to 3-D pictures. Films including “Treasure Planet” flopped. Management tended not to be grounded in hard facts.

“We were very emotional” in decision-making, Le Cam said.

Things changed when Disney bought computer-animated studio Pixar in 2006. Pixar executive Ed Catmull is a computer scientist and expected a more rigorous approach to operations, Le Cam said. That sparked an effort to focus more on metrics.

To aid the project, Le Cam brought in Adamsen, who had been an HR analytics practice leader at software firm Kenexa, which is now part of IBM.

Disney’s analytics push began in part with basic definitions. It created a “data dictionary” spelling out how different metrics would be calculated. Terms like “capacity planning” and “workforce planning” had different meanings to different people. “A data dictionary is square one,” Adamsen said.

And rather than focus too much on technology tools, Le Cam and her team emphasized working with colleagues to get agreement on the goals of the effort and how it would be carried out.

Film production has an inherent element of volatility. Le Cam noted that studio head John Lasseter or other members of Disney’s “brain trust” can come in part way through production and declare that a movie isn’t good enough. Films sent back to the drawing board in this way can trigger talent shake-ups, such as the need to hire more people pronto.

Still, Disney is working to be more systematic with its planning. It now creates a chart demonstrating the expected talent needs for multiple films over time. Another step is surveying those involved with a movie about the likelihood that it will be done on time. If answers are consistent, that’s a predictor of success. If they vary widely, trouble is probably brewing.

Adamsen said efforts like Disney’s should focus on business goals like better performance, a better work experience for employees and reduced risk rather than simply being able to produce numbers. The term “workforce analytics” isn’t going to mean much to executives, he said.

Indeed, the language around analytics proved to be important to Disney. The term “data-driven decisions” didn’t sit well with everyone. There was concern the “human” was getting lost in human resources. So Le Cam and her team altered the phrasing to “data-informed” decision-making.

Disney has a ways to go with respect to workforce analytics, Le Cam said. Nevertheless, her work to date has a happy ending apropos of a Disney movie classic. Attention to metrics, data and planning around talent has shifted the culture of Disney animated films and contributed to recent successes such as “Tangled” and “Wreck-It Ralph,” she said: “We transformed the studio.”

Reprinted from

5 Ways High-Performance Organizations Use HR Analytics

Mining masses of data for performance-improving insights is at  once the biggest challenge and greatest opportunity presented by big data.  While this is true for every function in the enterprise, the wake-up call for  HR should have been answered well before now. In fact, i4cp’s most recent  research on the analytical practices and capabilities of HR organizations  suggests that most are woefully unprepared to do little more with a rapidly  rising ocean of data than drown in it.

While many HR organizations are proficient  at collecting and measuring activities, few have the ambition or ability to  measure outcomes or identify the factors that most affect results.

i4cp’s new report, HR Analytics: Why We’re Not There Yet, pinpoints  the reasons for these shortcomings and highlights differences in the strategies  and practices of high-performing organizations (HPOs) and low-performing  organizations (LPOs) in addressing five key factors driving effective usage of  HR analytics – ambition, skills, data accuracy, HR leadership’s role and level  of sophistication.

1) HPOs use HR data to plan and perform better;  LPOs seem content to merely report it.

HPOs take a more calculated approach, using data for strategic, long-term  planning over twice as much as LPOs (96% compared to 47%). Far more HPOs (91% compared  to 59%) rigorously assess the ROI of initiatives and programs. Not only is the use of data to make business decisions the marker of an astute organization, it  underscores that HPOs are focused on far more than simply reporting.

HPOs actively seek information that improves the effectiveness of their planning and the performance of their programs and processes. Low-performing companies do little more than meet minimum requirements necessary for business.

2) Turning data into information is the most pressing analytics challenge — and HPOs are better equipped to meet it.

A common challenge cited by HR practitioners is the difficulty in  determining what the data that is gathered actually means. This was the top  data collection obstacle cited by all survey respondents. As Sue Suver, Head of  Global HR at U.S. Steel pointed out, “Data is great if you have it. But without  people who know what to do with it, you’re still stuck.”

Sifting through an  expanse of big data to pinpoint trends or uncover stories is a difficult and  time-consuming task. It requires analytical and interpretive skills, which more than half of respondents from low-performing companies said they seriously lack compared to little more than a third of those from HPOs. Their experience  suggests that companies that can transform data into information, and  information into profitable action, will reap a competitive advantage.

3) HPOs take full advantage of processes,  automation and standards to ensure data accuracy, while LPOs rely mostly on manual checking.

Twice as many HPOs reported using company-wide standard definitions as a method for guaranteeing data accuracy. Both HPOs and LPOs check data reliability, but HPOs use automated processes (68% compared to 38%) to a  greater extent, which not only reduces errors, it frees up employee time for more  pressing tasks.

The most difficult task of all is setting data standards in the  first place. Data councils, which convene stakeholders to set policy around activities such as data collection, standards,  and security, are pivotal because they enable enterprise solutions and ensure organization-wide  consistency.

4) HPOs’ HR leaders are highly engaged in using analytics to drive performance; LPOs are content to supply data to the  executive team.

More than twice as many HPOs have HR leaders receiving workforce data than LPOs  (81% compared to 33%), which suggests a more robust, analytics-savvy HR  department in more successful companies. i4cp’s study indicates that HPOs are moving more aggressively toward the  performance advisor role identified in i4cp’s 2012 report, The Future of  HR: The Transition to Performance Advisor. HPOs are also using  people-related data and metrics to proactively inform and engage both the  senior leadership and line managers on how to better manage talent and improve  business performance.

Dominique Ben Dhaou, SVP of HR at SGS, a global leader in providing verification, testing and certification services, underscores  the importance of having a basis for action regarding data: “If you benchmark  or read a report and do nothing with it, it’s useless. But if you transform the data you have access to into solutions for business issues, it has value. When  business people say HR doesn’t understand the business, it isn’t that – it’s that  we don’t do anything with the information we have.”

5) Predictive analytics are underused for  human capital measures – even by HPOs. 

Both HPOs and LPOs are still finding their way in developing the skills and technical capability to perform and use predictive analytics. Few are now using  analytics to answer questions such as how many employees are needed, who is likely to leave, which skills will be in short supply, how changes in workforce  cost and productivity affect the bottom line, and which HR practices directly  increase company performance.

Predictive analytics can reduce uncertainty and provide an evidence-based grounding to the decisions of both HR and the  business. Using predictive analytics to understand the true drivers of customer  service representative productivity, i4cp member-company Sprint was able to improve its customer satisfaction by  record levels.

The bottom line: HPOs  are ahead in the race to connect HR initiatives to business outcomes through  data. The gap between HPOs and LPOs in mining insights from big data to show how HR initiatives  and practices generate hard financial returns is the single-most important  difference between the two groups.

The ability to close this gap – to find and  use data that can show the impact of HR programs – is one sure way that LPOs can become HPOs. By showing the actual financial impact of a program or a practice, the  relative merit of each can be seen, and strategies and budgets can be adjusted  accordingly. This evaluation of ROI is the key advantage of meaningful HR  metrics.

Reprinted from The Institute for Corporate Productivity

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