People Analytics: A key component to your HR Technology strategy

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Deloitte Capital H Blog, October 28, 2021

by Jamaal Justice and Albert Hong

What do you get when you combine promising new technological advances, a host of evolving solutions, and droves of eager customers who see the potential of these new solutions to move their businesses forward but are uncertain about what they should buy? The people analytics market.

The promise of generating actionable insights in every aspect of work, the workplace, and the workforce has created fast-growing demand for people analytics (PA). A myriad of vendors are seeking to meet the demand with an ever-expanding mix of solutions (e.g., tools for Human Capital Management Systems (HCM), data ingestion, data warehouse/lake, extract/transform/load (ETL), business intelligence, and advanced analytics that are evolving as quickly as their underlying technologies.

The explosive proliferation of Human Resources (HR) technology and solutions is a challenge for HR leaders at every step along the PA maturity curve for two principal reasons. First, the ideal set of PA tools and solutions for meeting all organizational needs has not yet emerged. Second, most organizations are still in the process of developing the capabilities needed to evaluate their PA needs: Deloitte’s 2020 High-Impact People Analytics study found that 82% of organizations globally are in the earlier stages of their maturity journey.

Whether HR leaders are in the early stages of building PA capabilities or trying to stay on the cutting edge of what is possible, they need a North Star — a guiding light and a path that will lead them to a strategy and HR technology architecture capable of delivering on the promise of PA across their organizations over time. Read the rest here.

The Insights-to-Action journey

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Deloitte, May 2021

by Marc Solow, Christina Brodzik, and Dan Roddy

Analytics promise to help companies make better, more data-driven decisions about work, workplaces, and the workforce. But, so far, this promise is unfulfilled: In our 2021 Global Human Capital Trends survey, a mere 3% of the 6,300+ executive respondents said they have the information needed to make sound people decisions. This is a prescription for frustration—and failure—in today’s disruptive environment. 

Leaders need analytics, but only to the extent that analytics provide insights that enable their organizations to act successfully in a reliable, adaptable, and human-centric manner. We call this “Insights-to-Action.” Organizations need to be able to use analytics to sense, analyze, and act—the three legs of the Insights-to-Action journey—in response to the myriad of disruptions affecting work, workplaces, and the workforce.

We know this firsthand because Deloitte uses analytics to fuel its own Insights-to-Action journeys. One example, which illustrates how analytics support our workforce efforts, is Deloitte’s ongoing quest to bolster diversity, equity, and inclusion (DEI) throughout the organization. Read the rest here.

Seeing, doing, and imagining

strategy+business, March 16, 2021

by Theodore Kinni


Photograph by Laurence Dutton

My vote for the foggiest assertion of the pandemic to date is that the U.S. has an abysmally high number of COVID-19 cases — more than 29 million as of March 8, 2021 — because of testing. “If you don’t test, you don’t have any cases,” former President Donald Trump said during a televised White House roundtable on June 15, 2020. “If we stopped testing right now, we’d have very few cases, if any.” I wonder how many people who heard that had the same first thought as I did: Correlation is not causation.

Though associations gleaned from big data drive recommendation engines and bolster corporate revenues, they have their limitations. Imagine trying to control a viral pandemic by refusing to test people for the virus.This isn’t to say that correlation — the idea that two or more things are associated in some way — isn’t valuable. Indeed, there is big money in correlation. In order to peddle subscriptions, Pandora doesn’t need to know what causes people who listen to The Grateful Dead to also listen to Phish. To bulk up its sales, Amazon doesn’t need to know what causes people who buy a Paleo diet book to also buy beef jerky.

The passive observation of data has limited value, because, as Judea Pearl reminds readers several times in The Book of Why: The New Science of Cause and Effect, data is profoundly dumb. “Data can tell you that the people who took a medicine recovered faster than those who did not take it, but they can’t tell you why,” writes the director of UCLA’s Cognitive Systems Lab. “Maybe those who took the medicine did so because they could afford it and would have recovered just as fast without it.”

Association, which Pearl, a Turing Award winner, identifies as the first of three steps on his ladder of causation, won’t help executives answer many of the questions they need to ask when formulating corporate strategy, making investment decisions, or setting prices. To answer questions such as, “What will raising prices by 10 percent do to revenues?” you need to start climbing Pearl’s ladder. Read the rest here.

Why So Many Data Science Projects Fail to Deliver

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MIT Sloan Management Review, March 2, 2021

by Mayur P. Joshi, Ning Su, Robert D. Austin, and Anand K. Sundaram

Image courtesy of Jean Francois Podevin/theispot.com

More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning. Moreover, evidence suggests that the gap is widening between organizations successfully gaining value from data science and those struggling to do so.

To better understand the mistakes that companies make when implementing profitable data science projects, and discover how to avoid them, we conducted in-depth studies of the data science activities in three of India’s top 10 private-sector banks with well-established analytics departments. We identified five common mistakes, as exemplified by the following cases we encountered, and below we suggest corresponding solutions to address them. Read the rest here...

The Transformational Power of Recommendation

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MIT Sloan Management Review, November 24, 2020

by Michael Schrage




Image courtesy of Paul Giovanopoulos/theispot.com

Wikipedia defines recommendation engines (and platforms and systems) as “a subclass of information filtering system that seeks to predict the ‘rating’ or ‘preference’ a user would give to an item.” But as a tool, technology, and digital platform, recommendation engines are far more intriguing and important than this definition suggests.

In data-driven markets, the most effective competitors reliably offer the most effective advice. When predictive analytics are repackaged and repurposed as recommendations, they transform how people perceive, experience, and exercise choice. The most powerful — and empowering — engines of commerce are recommendation engines.

Recommendation engines have been essential to the success of digital platforms Alibaba, Amazon, Netflix, and Spotify, according to their founders and CEOs. For companies such as these, recommendation engines aren’t merely marketing or sales tools but drivers of insight, innovation, and engagement. Superior recommendations measurably build superior loyalty and growth; they amplify customer lifetime value. Computing compelling recommendations profitably reshapes human behavior.

The influence and purpose of recommendation engines are not limited to customers or consumption. Large employers, most notably Google, have adopted and adapted recommendation engines as internal productivity platforms to nudge workers to their best decision options. Indeed, in late 2016, Laszlo Bock, the senior vice president of people operations at Google, left the company to launch Humu, a recommendation-engine startup for advising workforce behavior change.

While data remains the essential advisory ingredient, the global recommendations revolution reflects profound and ongoing algorithmic innovation, enabling machine learning and AI to power improvements in deep learning and generative adversarial networks. Successful recommendation engines learn how to learn. The more people use them, the smarter they become; the smarter they become, the more people use them. Done right, recommendation engines enable virtuous cycles of value creation.

The networked nudges and prompts of recommendation engines increasingly influence people’s choices in clothing, entertainment, food, and medicine; they also influence the texts we send, which friends we contact, the customers and prospects we prioritize, the experts we seek, the job candidates we hire, the investments we choose, the memos we edit, and the schedules we follow.

But prompts and nudges shouldn’t obscure the subtle but vital design principle that makes the recommendation-engine value cycle more virtuous: Recommendation is about ensuring better options and choices, not obedience or compliance. Recommendation engines don’t seek to impose optimal, best, or right answers on their users. To the contrary, their point and purpose are greater empowerment and agency. Influence, not control, is the algorithmic aspiration. In this, successful recommendation-engine design depends more on how recommenders seek to influence than on how much they know.

Recommendation engines transform human choice. Much as the steam engine energetically launched an industrial revolution, recommendation engines redefine insight and influence in an algorithmic age. Wherever choice matters, recommenders flourish, and this profound digital transformation of choice will only become more pervasive as recommenders become smarter. Better recommenders invariably mean better choices. Read the rest here.

When Employees Speak Up, Companies Win

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MIT Sloan Management Review, November 17, 2020

by Ethan Burris, Elizabeth McCune, and Dawn Klinghoffer





Business headlines suggest that employees are speaking up more than ever. Activist employees are calling out their companies over where and with whom they do business, burned-out employees are asking for more and more unique work-life accommodations, and concerned employees are raising questions about hiring practices and promotion decisions in light of institutional biases. Often, these instances of speaking up — called employee voice behaviors — result in an embarrassingly public airing of organizational issues.

Yet our research reveals that the headlines are not an accurate reflection of the current state of employee voice. We asked 6,000 employees of a Microsoft business unit to tell us how often they spoke up to their managers. In addition, we asked how many of 15 topics they spoke up about, such as their immediate job assignments, the culture of their teams, how employees are treated across the organization, the strategy of the company, and the work-life balance alternatives available to them. We found that relatively few employees consistently share their thoughts and opinions about a multitude of work issues with their managers: Just 13.6% of the surveyed employees said that they speak up on more than 10 of the topics. Slightly more are silent: In fact, 17.5% said they do not speak up at all. The largest group of employees — 47.1% — said they speak up on five or fewer topics, typically on issues related to their jobs.

If we assume that these findings reflect similar tendencies in other organizations, leaders should be concerned, because employee voice is not a voice of complaint or protest per se. It encompasses the willingness of employees to speak up about opportunities for improvement. These efforts are not a prescribed part of employees’ jobs; they are a voluntary communication of constructive ideas to leaders that enable learning and effective change in work groups of all sizes, from teams to entire organizations. Yet these efforts to tell the truth can involve confronting leaders, who can feel challenged or even threatened, especially when the proposed changes involve things that leaders have helped create or for which they are responsible.

More and more, companies are seeking to expand efforts to listen to their employees by inviting them to share their opinions and ideas in areas that are outside of their day-to-day tasks. For instance, in 2014, in the aftermath of a recall of 6 million vehicles for an ignition flaw linked to at least 13 deaths, General Motors launched its Speak Up for Safety program, which asked employees across the company to speak up about anything that might impact customer safety. The growing use of innovation platforms and ecosystems is another example. In addition, during the global COVID-19 pandemic, we’ve seen companies frequently survey employees on topics such as physical and mental health and their working conditions.

The effectiveness of all these efforts depends on employees’ willingness to use their voices. In this study, we sought to examine the benefits of a more expansive employee voice, the factors that determine voice behaviors, and the ways in which companies can encourage those behaviors. Read the rest here.

The Six Dysfunctions of Collaborative Work

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Connected Commons, June 2020

by Rob Cross and Inga Carboni


Beth was excited when the CEO asked her to take over a high-profile commercialization project that had been struggling. The leader in charge of the effort—one expected to double the technology firm’s revenues in the coming decade—had recently accepted another job. Beth accepted the job on the spot.

In her first week, Beth dug in. She found the project fully funded and staffed by 64 carefully selected people from departments across the company, including engineering, marketing, finance, and quality assurance. The threeday, offsite visioning session held to launch the project had been attended by the entire team and was, by all accounts, a resounding success. Three concurrent work-streams—focusing on research, product development, and marketing and sales—were identified and a well-respected leader was appointed for each one.

Yet, ten months later, the project was badly behind schedule and bogged down. Everyone with whom Beth spoke was frustrated with the slow pace of progress. They were all pointing fingers, but in different directions. The CEO believed the problem was a failure of leadership. The departing project leader blamed team members for not devoting enough time to the project. One team member said the problem was poor meeting management; another said key decisions weren’t being made in a timely manner.

What should Beth do? Appoint new workstream leaders? Relaunch the project? Restructure the group or the work? Add more people to the project team? Schedule more meetings or provide an online work platform? ...read the rest here

Want to Make Better Decisions? Start Experimenting

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MIT Sloan Management Review, Summer, 2020

by Michael Luca and Max H. Bazerman



Image courtesy of Ken Orvidas/theispot.com

Suppose you work on Google’s advertising team and need to decide whether ads should have a blue background or a yellow background. You think that yellow would attract the most clicks; your colleague thinks that blue is better. How do you make the decision?

In Google’s early days, the two of you might have debated the issue until someone caved or you both agreed to kick the decision up to the boss. But ultimately, it dawned on leaders throughout Google that many of these debates and decisions were unnecessary.

“We don’t want high-level executives discussing whether a blue background or a yellow background will lead to more ad clicks,” Hal Varian, Google’s chief economist, told us. “Why debate this point, since we can simply run an experiment to find out?”

Varian worked with the team that developed Google’s systematic approach to experimentation. The company now runs experiments at an extraordinary scale — more than 10,000 per year. The results of these experiments inform managerial decisions in a variety of contexts, ranging from advertising sales to search engine parameters.

More broadly, an experimental mindset has permeated much of the tech sector and is spreading beyond that. These days, most major tech companies, such as Amazon, Facebook, Uber, and Yelp, wouldn’t make an important change to its platforms without running experiments to understand how it might influence user behavior. Some traditional businesses, such as Campbell Soup Co., have been dipping their toes into experiments for decades. And many more are ramping up their efforts in experimentation as they undergo digital transformations. In a dramatic departure from its historic role as an esoteric tool for academic research, the randomized controlled experiment has gone mainstream. Startups, international conglomerates, and government agencies alike have a new tool to test ideas and understand the impact of the products and services they are providing. Read the rest here.

The algorithmic trade-off between accuracy and ethics

strategy+business, March 12, 2020

by Theodore Kinni



Photograph by Yuichiro Chino

Strava, a San Francisco–based fitness website whose users upload data from their Fitbits and other devices to track their exercise routines and routes, didn’t set out to endanger U.S. military personnel. But in November 2017, when the company released a data visualization of the aggregate activity of its users, that’s what it did.

Strava’s idea was to provide its users with a map of the most popular running routes, wherever they happened to be located. As it turns out, the resulting visualization, which was composed from three trillion GPS coordinates, also showed routes in areas, such as Afghanistan’s Helmand Province, where the few Strava users were located almost exclusively on military bases. Their running routes inadvertently revealed the regular movements of soldiers in a hot zone of insurgency.

The problem, explain University of Pennsylvania computer and information scientists Michael Kearns and Aaron Roth, authors of The Ethical Algorithm: The Science of Socially Aware Algorithm Design, is “that blind, data-driven algorithmic optimization of a seemingly sensible objective can lead to unexpected and undesirable side effects.” The solution, which they explore for nontechnical leaders and other lay readers in this slim book, is embodied in the emerging science of ethical algorithm design.

“Instead of people regulating and monitoring algorithms from the outside,” the authors say, “the idea is to fix them from the inside.” To achieve this, companies need to consider the fairness, accuracy, transparency, and ethics — the so-called FATE — of algorithm design.

Kearns and Roth don’t deal with the FATE traits in a sequential manner. Instead, they describe the pitfalls associated with algorithms and discuss the ever-evolving set of solutions for avoiding them. Read the rest here.

A Noble Purpose Alone Won’t Transform Your Company

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MIT Sloan Management Review, December 10, 2019

by Rob Cross, Amy Edmondson, and Wendy Murphy

Consider these two companies: The first is a retail chain with hundreds of locations globally — innovative, but basically a sales platform. The second is a hospital that treats the world’s most devastating cancers. Which do you think has a more engaged workforce?

If you chose the latter, in light of its quest to save lives, you wouldn’t be alone. Yet, when we spent time with both organizations, we discovered that the working environment in the hospital was rife with fear, workforce morale was low, and employee turnover was high. At the retail chain, on the other hand, there was a palpable spirit of camaraderie, employees were energetic and enthusiastic, and customers were very pleased with the service. The retailer had the more engaged workforce by a long shot.

It’s a common misconception, both in businesses and in management articles and books, that a sense of purpose is what matters most when it comes to engaging employees. Many leaders concerned with attracting and retaining top talent believe that nothing motivates people as much as the larger good they might be doing or the chance to change the world. Accordingly, they extol the higher virtues of their companies’ missions and the meaning of the work they offer.

But our work with more than 300 companies over the past 20 years, particularly our research using organizational network analysis (ONA) and our interviews with executives, reveals that purpose is only one contributing factor; the level and quality of interpersonal collaboration actually has the greatest impact on employee engagement. In this article, we’ll explore why collaboration has that effect and which behaviors you can adopt and practice to nurture it. Read the rest here.

Using AI to Enhance Business Operations

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MIT Sloan Management Review, June 11, 2019

by Monideepa Tarafdar, Cynthia M. Beath, and Jeanne W. Ross



Artificial intelligence invariably conjures up visions of self-driving vehicles, obliging personal assistants, and intelligent robots. But AI’s effect on how companies operate is no less transformational than its impact on such products.

Enterprise cognitive computing — the use of AI to enhance business operations — involves embedding algorithms into applications that support organizational processes. ECC applications can automate repetitive, formulaic tasks and, in doing so, deliver orders-of-magnitude improvements in the speed of information analysis and in the reliability and accuracy of outputs. For example, ECC call center applications can answer customer calls within 5 seconds on a 24-7-365 basis, accurately address their issues on the first call 90% of the time, and transfer complex issues to employees, with less than half of the customers knowing that they are interacting with a machine. The power of ECC applications stems from their ability to reduce search time and process more data to inform decisions. That’s how they enhance productivity and free employees to perform higher-level work — specifically, work that requires human adaptability and creativity. Ultimately, ECC applications can enhance operational excellence, customer satisfaction, and employee experience.

ECC applications come in many flavors. For instance, in addition to call center applications, they include banking applications for processing loan requests and identifying potential fraud, legal applications for identifying relevant case precedents, investment applications for developing buy/sell predictions and recommendations, manufacturing applications for scheduling equipment maintenance, and pharmaceutical R&D applications for predicting the success of drugs under development.

Not surprisingly, most business and technology leaders are optimistic about ECC’s value-creating potential. In a 2017 survey of 3,000 senior executives across industries, company sizes, and countries, 63% said that ECC applications would have a large effect on their organization’s offerings within five years. However, the actual rate of adoption is low, and benefits have proved elusive for most organizations. In 2017, when we conducted our own survey of senior executives at 106 companies, half of the respondents reported that their company had no ECC applications in place. Moreover, only half of the respondents whose companies had applications believed they had produced measurable business outcomes. Other studies report similar results.

This suggests that generating value from ECC applications is not easy — and that reality has caught many business leaders off guard. Indeed, we found that some of the excitement around ECC resulted from unrealistic expectations about the powers of “intelligent machines.” In addition, we observed that many companies that hoped to benefit from ECC but failed to do so had not developed the necessary organizational capabilities. To help address that problem, we undertook a program of research aimed at identifying the foundations of ECC competence. We found five capabilities and four practices that companies need to splice the ECC gene into their organization’s DNA. Read the rest here.

Will Manufacturers Rule the Global Economy Once More?

strategy+business, February 1, 2019

by Theodore Kinni

We’ve been treated to various versions of manufacturing in the past couple of decades. There’s manufacturing as an exercise in financial arbitrage — a link in a global supply chain that is reforged whenever and wherever people will do more work for less pay. There’s the maker movement, populated by hordes of entrepreneurs laboring away in shared shops. There’s Industry 4.0, where we inefficient humans need not apply. There’s the revitalized rust belt, polished to a mirror’s shine with tariffs. And now there’s Tuck School of Business professor Richard D’Aveni’s vision of manufacturing, detailed at length in The Pan-Industrial Revolution, a book that starts out strong but eventually bogs down in speculation.

D’Aveni’s version of manufacturing could be labeled “when dinosaurs rule the Earth once more.” He thinks that hulking tyrannosaurs like battered General Electric are going to rise up and roar in the years ahead. If he’s right, the Dow Jones Industrial Average might actually become industrial again.

D’Aveni weaves this new vision on an intricate loom. Its weft is composed of additive manufacturing (AM), which includes all the evolving forms of 3D printing in combination with other production technologies, such as lasers and robotics; its warp is digitized, AI-powered management systems and platforms.

AM is a game-changing family of technologies, and D’Aveni illustrates them with a host of gee-whiz examples. Lockheed Martin can 3D-print the entire body and interior of its 12-ton, 50-foot-long F-35 fighter jets in about three months, compared with the two to three years it takes to make them using traditional technologies. (It’s now working to cut production time to three weeks.) Electronics parts supplier Lite-On is using 3D printers to make 15 million smartphone antennas annually, demonstrating the technology’s potential for cost-effective mass production. The medical device company Stryker, which is already 3D-printing joint implants, is developing machines that can be installed in hospitals to produce customized implants while surgeons and patients wait. Local Motors has demonstrated its ability to 3D-print a car — reducing the number of parts needed from 30,000 to 50. Read the rest here.

Predictions for 2019: People analytics will augment the workforce and the workplace

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Deloitte Capital H Blog, December 4, 2018

by Kathi Enderes


Ninety percent of the data in the world has been created within the last two years alone, and the continued emergence of new technologies will likely increase that rate even more. HR leaders have been attempting for years to use people analytics to turn this vast amount of data into actionable insights, but many still struggle with how and where to apply people analytics to maximize the return on investment. In the coming year, more and more organizations will start to apply people analytics in a new way, with a direct focus on the individual, rather than through HR or leaders—a bottom-up approach, as opposed to just top-down.

Going forward, we predict people analytics will become a principal supporting factor in the growing autonomy (and productivity) of the individual, empowering each person with the insights to help them do their best work. Last year, Bersin’s High-Impact People Analytics research revealed that only 2 percent of surveyed organizations are highly mature in people analytics. That tiny percentage has granted us an advance look at how to put people analytics to work. The most mature functions not only integrate it throughout their enterprises but also focus it on addressing business problems, enhancing the quality of day-to-day decision-making, and expanding its accessibility and use through robust insight delivery systems. The purpose of people analytics in these few high-performing companies? Enhanced workforce productivity and performance. Read the rest here.

How to embed data-driven decision-making into your organisational culture

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InsideHR, November 20, 2018

by Jeff Mike



Thanks to the increasing sophistication of analytics, data and algorithms can inform and improve management, business, and HR decision-making throughout companies. But, the tools of data collection and decision-support algorithms are only one element in the quest to attain the full potential of analytics.

Another is the ability of employees at all levels to use these tools, a challenge that will require a broad-based upskilling of the workforce. And, there’s an additional element – the willingness to employ analytics to make decisions. Compounding all of this is the fact that organisations today are becoming social enterprises, where the ability to manage social, environmental and governance concerns are as important as financial returns. In this environment, workers have more influence than ever. Their voices are amplified through social media and other means, meaning errors made by an organisation can have far-reaching consequences.

So, what does all of this mean? To create a willingness to use more data and unbiased decision-support algorithms, a mindset of data-driven decision-making should be embedded in the organisational culture in a way that benefits employees in their work as well as other stakeholders. The need for a data-driven culture is important and shouldn’t be underestimated.

In fact, this need is one of the top findings in Bersin’s High-Impact People Analytics research, which revealed that a company can fully utilise people analytics only if – and when – using data to make decisions becomes part of the culture, or “how we do things around here.” In fact, the research determined that organisations that have achieved the highest levels of people analytics maturity are three times more likely to have such a culture than organisations at lower maturity levels.

However, just making the decision to implement a mindset of data-driven decision-making into an organisation’s culture won’t work. In an analytics-friendly culture, data-driven decision-making isn’t an afterthought, an add-on, or a justification; rather, it is a shared mindset in which:

• Everyone recognises that data and analytics are essential to sound decision-making;
• They use data and analytics in their decision processes for all aspects of the enterprise including financial, social and environmental well-being;
• They use data and analytics to monitor – and adjust – decision outcomes to ensure desired results and to prevent bias.

Read the rest here...

When Prediction Gets Cheap

strategy+business, August 8, 2018

by Theodore Kinni


I don’t usually write mash notes, but I recently sent one to Waze via Twitter. I figured the navigation app had helped me avoid more than 100 hours of traffic jams over a couple of years, and I felt compelled to declare my undying gratitude.

After reading Prediction Machines, by three Rotman School of Management professors, it turns out I’m not so much enamored with Waze as I am with the technology that powers it: artificial intelligence (AI). It is AI that enables the app to predict the best routes for its users.

According to Ajay Agrawal, Joshua Gans, and Avi Goldfarb, who are also, respectively, founder, chief economist, and chief data scientist of the Creative Destruction Lab, prediction is the essential output of AI. “The current generation of AI provides the tools for prediction and little else,” they write. “Today, AI tools predict the intention of speech (Amazon’s Echo), predict command context (Apple’s Siri), predict what you want to buy (Amazon’s recommendations), predict which links will connect you to the information you want to find (Google search), predict when to apply the brakes to avoid danger (Tesla’s Autopilot), and predict the news you will want to read (Facebook’s newsfeed).”

This is the key insight of Prediction Machines, and it is an extraordinarily useful one for any executive who has been grappling with the implications and ramifications of AI. AI will automate prediction, and as a result, prediction will become cheap. “Therefore, as economics tells us,” explain the authors, “not only are we going to start using a lot more of it, but we are going to see it emerge in surprising new places.” Read the rest here.

Practicing these 5 principles could help your organization improve profitability by 82 percent

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InsideHR, July 31, 2018

by Madhura Chakrabarti





People analytics has emerged as an essential competency for professionals across the HR function. One major reason: profitability. Last year, a Bersin research study, which included a survey of 900 HR and business leaders across a wide range of companies, found that organisations with the highest level of people analytics maturity reported a three-year average profit that was 82 per cent higher than those with the lowest level of maturity. Unfortunately, our research also revealed that only two per cent of the organisations surveyed has reached the highest level of people analytics maturity.

One big reason why so few companies have a mature people analytics capability is the lack of data literacy in HR. Many HR departments have core people analytics teams, but the expertise they contain is siloed. Our research found that the HR staff in nearly 60 per cent of organisations do not yet have basic data literacy skills.

One demonstrated solution is upskilling. HR practitioners need knowledge and skills to use data and analytics. When we looked at how organisations with the highest level of people analytics maturity attained that status, we found that their success at scaling data literacy across HR was supported by five action principles...read the rest here.

Wait-and-See Could Be a Costly AI Strategy

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MIT Sloan Management Review, June 15, 2018

by Jacques Bughin

From the dexterity of Amazon’s Kiva robots to the facial recognition in Apple’s iPhone X, artificial intelligence is increasingly sophisticated and accessible. It also promises to be a rich source of profit uplift — up to 10% of revenue, depending on your industry.

Nevertheless, more than 95% of companies have not embraced AI technology to reinvent how they do business. Even though there are many unknowns regarding AI’s capabilities and uses, our research at the McKinsey Global Institute suggests that following a wait-and-see strategy for too much longer could be a costly mistake.

How costly? When we collected more than 400 use cases in 19 industries and simulated the dynamics of AI diffusion (based on current corporate intent to adopt, the technology’s impact on cash flow, and the profit growth linked to adoption), we found significant divergences in the patterns of economic growth between early adopters of AI at scale and non-adopters. In the simulation, early diffusers — that is, companies that will use a full suite of AI technologies in the next five years — doubled their normal profits by 2030, bringing in an additional 4% of gross profit growth annually at the expense of their competitors. When we extrapolated this on a global basis, it equated to a shift in corporate profit to early AI diffusers of approximately $1 trillion by 2030, or 10% of the current profit pool. Read the rest here.

It's Time to Make the People Side of Business Data-Driven and Evidence-Based

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Boss, June 2018

by David Mallon



These days data is front-page news. It’s use—and misuse—has precipitated a host of corporate crises. In many of these stories, data is being demonized. But data, per se, isn’t bad or good; it’s what people do with it that matters.

We live in an era of digital technology, in which more and more of what we do generates data. A few years ago, IBM estimated that we were creating 2.5 quintillion bytes of data daily, and that something like 90 percent of all the data in the world created in the prior two years alone. The data we generate is collected, analyzed, and served back to us constantly, whether we realize it or not.

Companies and individuals must become masters of data or they may risk being mastered by it. And to master data, companies and individuals need to be mindful. What data will be collected? How can they ensure its accuracy? Who will collect it? Where, when, why, how, and for what purpose will it be used? Where will the data go? Who will have access to it? What privacy and security controls will protect it?

If that seems like a lot of questions, well, it is. We need to be active stewards of our data. We need to actively seek insights from it. We need to use those insights in positive, productive ways that drive organizational and personal value. The headlines tell us that these tasks shouldn’t be left unaddressed—especially when it comes to our people.

The HR Function Needs to Build Data Muscle

One finding rings out clearly in Bersin’s High-Impact People Analytics study: Companies that are proactively building an organizational muscle around people data and analytics are getting ahead.

Some corporate functions, like marketing, are already well along in the data and analytics race. But the typical HR department is not nearly as evolved. Our study revealed that only two percent of companies surveyed have a fully mature (Level Four) people analytics capability; meanwhile 83 percent of companies are operating at Levels One and Two.

The people side of business must become more data-driven and evidence-based. Data can and should inform decisions around performance, people, and talent, if for no other reason than the fact that relying on tradition and prior experience are neither sufficient nor prudent in today’s digital world...read the rest here

How to Cure a Bad Case of Metric Fixation

strategy+business, April 23, 2018  

by Theodore Kinni

The Tyranny of Metrics is a pearl of a book. And like all pearls, it was born of an irritation. As chair of the history department at the Catholic University of America, Jerry Muller found himself — and his school — devoting more and more precious time and resources to performance measurement and reporting.

Some of this activity was useful, admits Muller. “But much of the information was of no real use, and indeed, was read by no one,” he writes. “Yet once the culture of performance documentation caught on, department heads found themselves in a sort of data arms race.” In one instance, Muller was urged to add more statistical appendixes to a yearlong department survey, because “the report would look less rigorous than that of other departments” without them. Moreover, the university found itself hiring more and more data specialists, up to and including a new vice president for assessment.

This experience, which Muller describes as an irritating pinprick, inspired him to look more closely at metrics. And that investigation resulted in a concise and clearly argued polemic, which, Muller writes, is “for anyone who wants to understand one of the big reasons why so many contemporary organizations function less well than they ought to, diminishing productivity while frustrating those who work in them.” Read the rest here.

The Truth About Corporate Transformation

Learned a lot editing this article from the BCG Henderson Institute:

MIT Sloan Management Review, January 31, 2018

by Martin Reeves, et. al.

Corporate transformation sits atop the strategic agenda for many CEOs. While transformation is ideally undertaken preemptively, in practice it is much more commonly a reaction to changing — and challenging — circumstances. Such transformations represent a fundamental and risk-laden reboot of a company, with the goal of achieving a dramatic improvement in performance and altering its future trajectory.

Given the stakes, we were startled to find that the research underpinning the design and execution of corporate transformations is surprisingly thin. As a result, transformations are often guided by beliefs that, while seemingly plausible, are more anecdotal than empirical in nature. It’s time for a more evidence-based approach.

To study corporate transformation and its success factors, we analyzed financial and nonfinancial data of all U.S. public companies with $10 billion or more market cap between 2004 and 2016. We identified companies with a demonstrated need for fundamental change, namely, those companies with an annualized deterioration, relative to their industry average, in total shareholder return (TSR) of 10 percentage points or more over two years. This definition provided us with a large data set for empirical analysis including more than 300 companies across different industries over more than a decade.

Further, we trained a proprietary algorithm to quantify the strategic orientation of companies, based on semantic patterns within the “Management’s Discussion and Analysis” section of 70,000 10-K filings. We built a prediction model to identify formalized transformation programs, based on restructuring costs and major corporate announcements (as reported by Standard & Poor’s Financial Services LLC). And we conducted a multivariate regression analysis to determine the impact of a number of factors on change in TSR during transformations...Read the rest here.