Tuesday, September 30, 2014

How to Tell If You Should Trust Your Statistical Models

Predictive analytics often sounds a bit like quantum mechanics: fiendishly complex to look at and wildly counter-intuitive. So when someone sells you a tool, how can you verify that the black box is fit for its purpose or even lives up to the vendor’s claims?

Of course, there never are ironclad guarantees around prediction; the future can have more shapes and colors than we ever imagined. But you’ll be more likely to get the best out of your predictive analytics if you ask the following basic questions in evaluating new predictive models:

Q1: Are the data good? Make sure the data used, as well as the processes that generate and organize it, are of the highest possible quality, and that you fully understand them. As a hedge fund manager who applied data-driven trading strategies once said, “You can spend endless time and resources only to find eventually a bug in your data.”

You can get problems even with the best data. In a recent study of the daily close prices of S&P500 stocks, we “discovered” what looked like striking patterns in stock movements. When we looked more closely, we found that the patterns were explained by the fact that what was meant by the “close price” of a stock is very loosely defined. The term had multiple definitions, all of which were applied, including, for instance, the “last price before 4 PM” and the “last price reported in the closing auction.” Once we defined the term more narrowly, the patterns disappeared. We imagine that many other people will fall into a similar trap and see patterns where none exist because they haven’t thought hard enough about what the data they’re looking at actually are.

Q2: Does the model tell a story? Sound models usually tell a clear story. If the predictive analytics you’re using don’t give you one, beware—the models may need to be refined. This is not to say that the story has to be what you expect or that it has to be a simple one-line statement. It is rather that the story has to be understandable to the people basing their decisions on it. It is not about avoiding complex statistical models, which of course are often necessary, but about having thought through, refined, and simplified the models enough to be able to understand them.

Q3: Is the math as simple as it can be? It’s natural to assume that the model that does the most with the most variables will be the best model. But theory, such as Statistical Learning Theory in Machine Learning, teaches us that predictability typically first improves and then deteriorates as model complexity increases, so adding complexity should not be a goal in itself. This is precisely why describing a model using a simple story that makes economic sense is often a good sign of a refined model with the right level of complexity. Of course, there are equal risks to oversimplifying the math, so don’t go too far in that direction either. Einstein is often quoted as saying, “Everything should be as simple as it can be, but not simpler,” a good principle to apply to predictive analytics.

Q4: Is the predictive accuracy stable across different, fresh data? All too often analysts study historical data to develop a model that explains that data and then apply the model to the exact same data to make a prediction about the future. For example, developing a credit scoring model using data of past defaults and then testing the model on that same data is an exercise in circularity: you’re predicting what you’ve explained already. So make sure that your analysts apply the model to fresh data in new contexts. More importantly, check that the predictive accuracy of the models is reasonably close to how well the model succeeded in explaining the data it was developed to explain; prediction accuracy should be similar across multiple environments and data samples.

Q5: Is the model still relevant? Confirmation and other behavioral biases, along with the “sunk cost” fallacy, often encourage people to see predictability where there is none, especially if there was some before. But if the data don’t support your predictions, you should be prepared to jettison your model—possibly multiple times. Even if your model has a track record, you should still test whether it remains relevant to the economic and business context. Using predictive models of demand developed during growth years or for a price sensitive market segment may fail miserably when market conditions change. This makes predictive analytics by nature risky: they are valid as long as the reality they were developed in, the data describing a specific market during a specific time, is also valid.

Developing and applying predictive analytics requires a delicate balance between art and science. A deep contextual understanding with very honest interpretation and story development should be balanced with hard facts and statistical analysis. And always keep in mind that the most predictive analytics models can become fully non-predictive in a fortnight—just imagine how some financial models looked the day after the Lehman collapse.

via How to Tell If You Should Trust Your Statistical Models – Theos Evgeniou – Harvard Business Review.

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How to Tell If You Should Trust Your Statistical Models

What the Companies That Predict the Future Do Differently

If knowledge is power, then predictive analytics promises the ultimate knowledge — that of the future. Such knowledge does not come easily, but the increasing density of digital information, deeper automated connections across companies, and increased storage and computing power create new options for enterprise leaders. For the first time in history, the predictive future — the increasing awareness and likelihood of potential future actions and outcomes — is within reach. No wonder, then, that executives have placed predictive analytics at the top of the executive agenda since 2012, according to a recent Accenture survey.

But to know more about potential future actions and outcomes and their probability — and to act on that knowledge — organizations are engaging in new kinds of relationships. We have found that the most forward-looking organizations do these three things:

(1) Look to the outside: The main focus of analytics has until recently been internal, directed toward high-frequency, standardized, repeatable processes that connect variance with intervention. Using analytics, organizations have deployed bigger data sets, cheaper cloud computing power, and more aggressive algorithms to successfully standardize previously non-standard processes such as sales and service, making them more repeatable, predictable, and amenable to analytics.

To apply analytics to the future, though, self-knowledge is insufficient. The information most likely to influence the future comes from looking out the window, not in the mirror. Sheer computing power isn’t the key differentiator either, because the predictive future relies less on additional statistical mastication than on a greater diversity of inputs. Consider the example of a manufacturer of production equipment that collects sensor-based telemetry about its machines’ operations, the status of their parts, their performance, their resource consumption, and other data. This monitoring turns up an anomaly at a key customer that indicates a failure is imminent. Such a failure would cause a significant cost and damage the customer’s brand. The manufacturer notifies the customer, which pulls the machine off line and repairs it, saving millions of dollars in lost production and damage to its brand. Business continues as usual and the equipment manufacturer has a very grateful customer.

In this example, information that was critical to the customer came from outside its walls. But while such information exchanges have become technically feasible, they are not yet financially beneficial to the information provider and difficult for the customer to value and incorporate into their management systems. Turning information exchange into value and revenue involves changing the nature of information relationships as well as management’s abilities to act on that information. The most forward-thinking companies are developing new business models to create value from these kinds of information exchanges.

(2) Develop open multiple multi-sided relationships: Altruism or openness alone will not give rise to ready access to the diversity of data required to understand the predictive future. The availability and veracity of the data involved in the predictive future requires creating multiple multi-sided relationships with customers, suppliers, trading partners, and just about anyone else with potentially beneficial information. It is no longer enough to share information one-to-one with partners. Increasing predictive power rests in positioning yourself at the center of multiple information flows.

Current information-based services, such as Bloomberg, involve an information provider selling a single set of information with segmented services to multiple customers. Such models play a part in the predictive future, but the industrial Internet and expanded communications capabilities change the nature of information products. From one product distributed to many customers, the move is underway toward products that feed information from many sources to a single party, which rearranges and redistributes the information to many customers. In short, from many to one to many.

There’s a demand for this type of information, and thus product and market opportunities, but in an information services marketplace where people want everything for nothing, it is not easy to monetize information products. We expect, though, that a viable market will emerge as commercial terms evolve to support the multiple multi-sided relationships that give subscribers unique access to information and therefore value. Whether the information source is commercial brokers or existing commercial relationships, diverse information sources fuel the predictive future.

(3) Update management and leadership practices: An extended analytics engine fueled by multiple information sources, however, can accomplish little without the ability to act on future predictions. The practice of management itself must evolve for this capability to emerge.

It is hard enough to act on solid information about the past. The level of difficulty rises when management is asked to deal with a set of predictive futures rather than projections based on past performance. Effective use of predictive analytics involves mastering a new set of management, operational, and financial techniques and disciplines.

Managerially, organizations need to revise management practices, including: increasing the use of experiments and pilots to enhance risk-taking based on external and incomplete data; incorporating test-and-learn experiences into decisions and action; enhancing awareness of the differences between causation, correlation, and coincidence; and placing tangible value on avoiding adverse effects and missed opportunities.

Operationally, organizations need to establish their own trust and execution mechanisms for multi-sided, information-based relationships. These mechanisms entail creating new analytics capabilities, securing access to third-party information and capabilities, continuously refreshing sources, and determining which data need to remain private to retain their value.

Financially, organizations require new models to account for information assets beyond treating them as intangibles. Financial arrangements have to evolve to handle pricing and payments for value based on possible futures. The ultimate goal is to treat information as a tangible flow rather than an intangible asset stuck on the balance sheet.

“The future is already here, it’s just unevenly distributed.” William Gibson’s dictum, though overused and abused, remains true. The predictive future is valuable precisely because it’s unevenly distributed and therefore in demand. Finding this future in the deluge of information available requires doing a better job of boiling the ocean. It requires investing in management, information-intensive relationships, and a broader view of analytics in the enterprise.

via What the Companies That Predict the Future Do Differently – Jeanne Harris, and Mark McDonald – Harvard Business Review.

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What the Companies That Predict the Future Do Differently

How You Can Identify Highly Motivated Employees

Everyone wants to hire motivated people, but few individuals are self-motivated to do every type work for every type of manager in every type of business situations. Over the years, I’ve discovered that it’s better to first discover what drives self-motivation rather than look for self-motivated people.

A story from long ago sets the foundation for this conclusion. It happened when I was a rookie engineer working on missile guidance systems. The 20 or so other engineers on the same project thought the work was mundane and put in the requisite eight hours and 15 minutes per day. However, they all told me that in their prior jobs they had been going 24/7 doing essentially the same work. The only difference was the project. Their earlier work was on President Kennedy’s moon landing program. For them, and the thousands like them, that work was inspirational. The current work, although essentially the same, had no grand purpose.

This was my first big lesson about motivation. As a driver of motivation and job satisfaction, the impact of the work is often far more important than the actual work.

Over the next few years, as I started interviewing people, I learned some other important lessons about motivation:

  • Motivation to get the job is not the same as motivation to do the job.

  • Introverted people can be just as motivated as extroverted people.

  • Being prepared and on time for an interview offers no clue to motivation.

  • On the job, people seek out work they like to do and avoid work they don’t like to do.

Over the years, these lessons have been incorporated into the performance-based hiring process underlying my company’s recruiter and hiring-manager interview training programs. Here’s a summary of the process.

Using Performance-Based Hiring to Identify Highly Motivated People

Clarify expectations up front. Define the work you need done before you start interviewing candidates. Every job can be defined by six to eight performance objectives. This is called a performance-based job description. A reliance on a traditional skills-infested job descriptions increases the chance you’ll hire someone less motivated to the do the work if that person finds the actual job uninteresting. (Here’s the legal justification for using performance-based job descriptions.)

Get examples of comparable accomplishments. For each performance objective listed in the performance-based job description, ask the candidate to describe a comparable accomplishment. The Most Important Interview Question of All Time describes the process. This reveals the types of work the candidate finds most motivating. (The full approach is described in The Essential Guide for Hiring

via How You Can Identify Highly Motivated Employees | Inc.com.

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How You Can Identify Highly Motivated Employees

Monday, September 29, 2014

The Bias Undermining Your People Analytics

People analytics – the fast-growing practice which companies use to analyze large amounts of data to quantify employee performance – has the potential to revolutionize the workplace and vastly improve how all of us are rewarded for our efforts. But used the wrong way, people analytics can be just as blind and biased as human beings have always been.

One of the most well-established findings in social psychology is the “fundamental attribution error” which essentially describes how observers over-attribute their explanations for the causes of behavior to “the person” and under-attribute the causes of behavior to “the situation.” In careers and the workplace, this means that credit or blame for performance is likely to be assigned to an individual more based on his or her perceived character, personality, intentions or efforts rather than on the situation, context, opportunities or constraints within which that individual is working. This cognitive bias explains why salespeople who are lucky enough to be selling the right products to the right market at the right time get credit and get viewed as talented “A players,” while those who have the misfortune of selling the wrong products to the wrong market at the wrong time instead receive blame and become branded as mediocre “B” or “C” players. This bias also explains why a CFO may be perceived as cheap by disposition or why a team might attribute its internal conflicts to incompatible personalities instead of resulting from organizational incentives to compete rather than collaborate with one another.

In this evolving age of data, the latest manifestation of the fundamental attribution error arises in the rapidly growing field of people analytics. Organizations can now conduct large-scale analyses with all kinds of variables in order to try to predict which employees are likely to succeed, and which are not. Companies use variables such as college or graduate school grades, SAT or GMAT scores, years of working experience, and the results of cognitive ability or personality tests, to predict turnover rates, promotions, sales volume, or other performance outcomes. With the explosion of data that companies collect and compile about their employees, it is tempting to both categorize and classify employees who currently work at the organization and to simultaneously build profiles of ideal candidates, who will be likely to perform well, remain at the organization, get promoted, and be satisfied with their jobs.

But just as people are susceptible to making the fundamental attribution error, organizations risk making what might be called the fundamental analytic error. That is to say, in many instances, critical information is missing from human capital or people analytics: situational or contextual variables. An argument can be made that for the purposes of predicting, explaining, and improving variance in performance, situational variables might actually prove better than individual variables. Using the example of salespeople, more of the variation in sales volume may be attributable to product or territory than by which sales person happens to be selling a given product in a given location. What remains indisputable, however, is that the combination of individual and situational variables together will explain much more of the variance in performance or employee engagement than individual variables or situational variables could ever explain alone.

The fundamental analytic error tempts organizations for several reasons. It’s often much more politically expedient to blame individuals when things aren’t going well than to search for the underlying organizational causes of their difficulties. If the talents or efforts of individuals get credited or blamed for performance, then performance that doesn’t meet expectations does not raise tough questions about whether culture, product, strategy, incentives, or technologies might be improperly configured or misaligned with one another. In other words, poor performance can readily get attributed to employees rather than to their leaders, who are directly or indirectly responsible for creating the conditions that should enable people to succeed. If products are not selling, it may be very appealing to initiate an analytics project to look at salespeople’s attributes instead of getting customer feedback about the company’s products. If turnover is high among entry level employees, it could much more politically palatable to analyze the personality, style, education, experience level, and referral source of the employees who leave the organization, rather than to analyze the capabilities or managerial skills of their supervisors. And no amount or kind of human capital analytics is going to save an organization in denial about disruptive changes occurring in its industry or markets.

There is a better way. The most valuable human capital or people analytic initiatives get deployed in a scientific manner. Hypotheses, nested in some kind of conceptual framework, get formulated and tested and theories and hypotheses are all subject to falsification. So, if some associates in a law firm are performing well, while others perform poorly, it is reasonable to hypothesize that their law school grades, LSAT scores, and whether or not they clerked for a judge might help predict, and partially explain, their performance as attorneys. If the associates who have high grades and scores and clerked for a judge are high performers, and associates with low grades and scores who did not clerk are poor performers, a researcher could hypothesize that this correlation indicates causation: that intelligence and motivation are reflected in the lawyers’ resumes, and that higher intelligence and motivation in turn cause higher performance.

But before conclusions can be drawn, other explanations need to be considered and alternative analyses need to be conducted. The hypothetical law firm, for example, might look beyond human capital analysis of its associates and consider the impact of practice area, geographic location, and types of cases on associate retention and performance. A courageous investigator, willing to risk stirring up organizational politics, might even suggest that the law firm conduct analyses to learn whether associates who work for some partners outperform associates who work for others. These additional analyses might determine that variance in associate performance is a function of whether the partners they happen to be working for provide coaching, mentoring and support, and not a result of the associates’ grades, scores or personalities.

Human psychology and organizational politics are both biased towards attributing too much causality to people and their individual attributes and not enough causality to situations and organizational context. Human capital and people analytics, despite their big data-fueled power, can easily get misused in ways that serve only to justify existing organizational systems and to unfairly scapegoat individuals who are not performing well in no small measure because of the weaknesses and constraints of those systems. Only by taking a broader, more open, less biased and less political approach to conducting analyses about the factors that predict and explain performance can organizations hope to improve it over the long term.

via The Bias Undermining Your People Analytics – Ben Dattner – Harvard Business Review.

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The Bias Undermining Your People Analytics

How Important Is Data Analytics to the Future of HR? 

Having discussed harnessing social media, enabling internal knowledge creation and leveraging social capital in previous articles, it seems a logical topic to cover next is to address one the key players in this debate and how they interplay with advances in data analytics and technology: Human Resources.

Big Data remains big news to HR so why is it (as was recently noted by the CIPD) “such a big ask for HR”? What is getting in the way? Is HR’s work too tied up in the ebb and flow of day to day business issues? How does HR achieve sophistication of operation if they are caught up in administrative tedium? It seems more than this though as the CIPD recently reported that organisational silos, insufficient internal skills sets, suspicion and scepticism from HR professionals, all surround the use of innovative and contemporary data sets.

HR analytics plays a key part in getting to grips with the challenge of containing costs while developing a high performing workforce; the primary issue facing most companies today. But do organisations know enough about their workforce to optimize its success? We all know how HR analytics can benefit talent sourcing and recruitment however it can also add to the wider HR remit including measuring and managing: retention; learning and development; sickness absence and performance data. Looking at the bigger picture, branding, marketing, social media, CSR and the creation of how HR can contribute to operational effectiveness can all be supported by gathering the right HR data in the right way.

Big Data and HR analytics are key to HR achieving sophistication and delivering a broader impact. The CIPD recently celebrated its’ centenary and asked HR professionals to comment on how HR can be future focussed. Central to the debate was how HR meets the demands of being strategic in contribution to the business. So how can HR help create lean efficiency in operations and play a pivotal role in a business? Human Resources need to be Innovators and Integrators in organisations; quite simply HR leaders must stay on top of the latest developments in their field and ensure their teams do so too. It is not simply a case of considering how analytics technology can help HR carry out their work more easily. They should also take a broader view, exploring ways they can use technology to better connect people with the company and also HR with their role in strategy.

It seems that organisations are now at different stages of the analytical journey. Organisations such as Royal Bank of Scotland, Unilever, Nestle and Transport for London are leveraging people metrics and insights to improve business performance, employee engagement and satisfaction. Ultimately it does come down to Data vs. Insight – to fully leverage the strategic benefits of HRIS data and human capital analytics, HR Analysts and HR leaders must be able to understand the data themselves and then to communicate the story it tells. The skills of uncovering “insight” and being able to communicate this effectively as a ‘story” that correctly influences human capital decisions, is of increasingly critical importance in the global economy.

via How Important Is Data Analytics to the Future of HR? | Mark Braund.

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How Important Is Data Analytics to the Future of HR? 

HR Tech Firms" Popularity Soaring With Venture Capitalists

If you want to secure venture capital for your next business idea, start an HR tech firm — preferably one that offers a new spin on recruiting.

For the past two years, venture capitalists have been pouring money into the HR tech sector, seeding promising young startups with millions of dollars in hopes that their take on benefits, employee engagement or social recruiting will be the next disruptive thing to change the way we manage talent.

As reported earlier this year, 2013 saw a record high in both the number of venture capital deals going to HR tech firms, and the amount those companies received. So far, 2014 is no different. In the past several months there has been a flurry of deals, big and small. A few highlights:

Hireology Inc., a provider of employee hiring and selection management technology, raised $10 million in Series B funding from Bain Capital Ventures in August.

SmashFly Technologies, a recruitment marketing platform technology, secured $9 million in June from OpenView Venture Partners.

YouEarnedIt, an employee engagement app, received $1.5 million in seed funding from Capital Factory, an Austin, Texas-based tech firm incubator, in May.

Work4, a social and mobile recruiting service, raised $7 millionfrom Serena Capital and Matrix Partners, in April, bringing its total funds invested to $18 million.

ZenPayroll Inc. raised $20 million in a Series A round from General Catalyst Partners and Kleiner Perkins Caufield & Byers in February for its cloud-based payroll application.

Why they are interested

“HR tech is attracting venture capitalists because of the growing complexity of today’s workforce,” said Ryan Hinkle, managing director of New York-based Insight Venture Partners, which assumed a majority equity position in Workforce Software this year. Fragmented worker groups coupled with global employment rules and shifting health care requirements make it increasingly difficult to manage talent, he said. And he sees this as an opportunity to change the way HR works.

Rather than investing in software that merely reproduce traditional processes in a technology-based format, his group looks for innovative new workforce software that change and improve the way talent is managed. “It’s about creating HR software that is infinitely configurable,” he said.

Autumn Manning, co-founder and CEO of YouEarnedIt, agreed. “You can’t predict everything people will want to do with your platform, so you have to build technology that is adaptable for the entire organization,” she said.

The rise in cloud-based software has also made it much more likely that a startup can quickly gain a competitive advantage against bigger players and thus be attractive to venture capitalists, Hinkle said. When companies had to invest in on-premise HR tech software, the ramp-up costs for new firms were too prohibitive to get a foothold. But with software as a service new vendors can quickly build revenue and try new ideas without taking on an overwhelming financial risk. “It’s allowing lots of startups to find their own piece of the market very efficiently,” he said.

Why HR Leaders Should Care

Looking ahead, the influx of capital into HR tech companies is likely to continue, especially around recruiting technology, said Holger Mueller, principal analyst and vice president for Constellation Research. “Recruiting is the biggest problem area for companies right now,” he said.

Businesses can put off upgrading performance management or social networking tools without a huge impact, but they can’t put off recruiting, he said. Between the talent crisis and retiring baby boomers, companies need new ways to find new talent. “It’s driving a lot of innovation and a lot of investment in recruiting technology.”

This ongoing flood of capital in HR tech startups could be a blessing or a curse for potential customers. On a positive side, it means the next generation of software will make it to market faster, giving customers a chance to tap into more potentially innovative solutions to their talent management challenges, Mueller said. However, every new tool purchased from an innovative new startup means yet another round of integration projects to make it work with the rest of a talent management system. “That is going to create a lot of headaches down the line.”

via HR Tech Firms’ Popularity Soaring With Venture Capitalists | 2014-09-19 | Workforce.com.

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HR Tech Firms" Popularity Soaring With Venture Capitalists

Analytics for Dummies

Workforce analytics may dangle the promise of finally letting companies use data to make better talent management decisions. But there is one big problem: no-one really knows how to do it.

The current generation of workforce analytics tools is still relatively complicated, according to Ron Hascombe, research director at Gartner, an information technology research and advisory company. “Most technologies run ahead of what all but a few HR people are able to utilize,” he said.

Fortunately, that is slowly changing. HR technology vendors recognize that customers want faster, easier, more robust analytics tools, and they are racing to develop or acquire software specifically designed to make it easy for non-analysts to do workforce analytics.

“It is critical that we continue to simplify how customers turn the vast amount of people data… into insights,” said Leighanne Levensaler, vice president of human capital management products at Workday, a Pleasanton, California-based software producer. “There is a lot of hype and hyperbole when it comes to big data and workforce analytics, yet there is still a dearth of people with advanced analytics skills in the industry.”

Doers and Dreamers

Most companies fall into one of two categories when it comes to workforce analytics. There are companies that want to collect and interpret basic internal metrics – but don’t really know where to start. Then there are the advanced organizations that are already doing some analysis of internal and external data, and are ready to move into more predictive reporting. These companies are usually larger, and have some level of analytics expertise on the HR team.

For the time being, most companies fall into the first category, said Hascombe. Gartner research predicts that by 2017, only 15 percent of organizations with more than 5000 employees will be doing predictive analytics using internal and external data.

Fortunately, most vendors in the human capital management industry are focusing on the needs of the many by creating ever-more sophisticated analytics tools that use visualization strategies, preset queries, and simple report generators that allow managers to choose a combination of metrics and rely on the technology to do the rest.

“The vendors will continue to invest in this subset of tools for the next three years,” Hascombe said.

The most recent upgrades suggest that vendors are focused on making analytics less technical and more user-friendly.

For example, SuccessFactors, an HCM software producer, recently launched ‘Workforce Analytics: Headlines,’ an automated tool that reviews employee data, interprets and prioritizes findings, then sends relevant information to managers in the form of news stories.

“It strips away the obscure analytical terms and just tells managers what’s happening with their teams,” said Mick Collins, principal consultant of workforce analytics and planning for San Francisco-based SuccessFactors. “It supports a more self-serve model for workforce analytics.”

And last fall, Workday rolled out a new tool designed to help customers combine various sizes, sources, and structures of internal and external workforce data to give them greater flexibility in the kinds of information they explore. Customers can answer business questions by building unique scenarios merging data from multiple sources, or they can leverage pre-built analytic templates to tackle common scenarios such as market compensation comparison or retention risk and impact analysis, Levensaler said. “It is about providing people with easier access to insight.”

There are also stand-alone vendors, like Visier, which focus entirely on workforce analytics and helping clients transition from interpreting past data to predicting future trends. Visier’s cloud-based platform unifies customers’ workforce data from multiple sources and allows users to get answers to hundreds of workforce-related questions.

Visier, which is based in both Vancouver and San Jose, rolls out new updates every quarter, and is focused currently on building more robust visualization tools, said Dave Weisbeck, chief strategy officer for Visier. “Employee data has a lot of complexity that simple charts can’t capture, which is why visualization is so important.”

For the more advanced clients, both tech vendors and human resource consulting firms, like Mercer, PWC and Gartner, offer ‘analytics as a service’ models, through which consultants set up custom models to analyze masses of workforce data and provide analytics support.

Hascombe points to IBM’s launch of IBM Workforce Analytics, which provides a mix of applications to help companies do predictive workforce analytics.

Good Data Is Good Enough

Many vendors are striving to help clients achieve the ultimate goal of predictive analytics, but there are still many obstacles to overcome – both in what the technology can deliver, and how HR thinks about data.

Most of the current workforce analytics tools available are still limited, preventing companies from mixing and matching complex metrics or customizing their reports. “In most cases, to get predictive analytics still requires consulting support,” Hascombe said.

HR leaders also need to get more comfortable diving into the analytics world – even if they have limited analytics skills and imperfect data sets, Weisbeck said. “The biggest obstacle for us is the fear HR departments have about their data not being good enough to do analytics.”

Weisbeck encounters many companies that are so focused on perfecting their data and rooting out all errors and anomalies that they never actually get to the analytics process. According to Weisbeck, those companies are missing opportunities.  “You can get amazing insights from imperfect data if it is analyzed properly.”

Workforce analytics will continue to be an important part of the talent management process, and the sooner companies embrace these processes the sooner they will be able to use employee data to make meaningful decisions, Hascombe added. “In the meantime, clean up your data, invest in governance and work with your organization to determine the critical metrics that you will want to track.”

via Analytics for Dummies | 2014-08-06 | Workforce.com.

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Analytics for Dummies

Friday, September 26, 2014

Top 10 Great Workplaces for Millennials

A new study from Great Place To Work, Fortune’s partner for the 100 Best Companies to Work For list, reveals which companies offer the best perks and benefits for Millennials — the youngest generation of the U.S. workforce that boasts 77 million workers between the ages of 18 and 35. After sifting through thousands of survey results and employee comments, the researchers at Great Rated!, GPTW’s employer-review website, identified 10 companies whose workplace cultures are a perfect fit for enterprising entry-level workers. And what do Millennials value most? Fair pay, having a say in decisions and being overseen by competent management top the list. They’re also fans of socializing with coworkers, flexible scheduling, a dedication to philanthropy and community, gym memberships, and wellness programs. Here’s what they had to say.


Intuitive Research and Technology

Revenue: $187 million
Headquarters: Huntsville, Alabama
Number of employees: 266
Millennial headcount: 32%
Best Companies rank: 2 (Best Medium Companies to Work For)

Millennials at the technology services firm have a life outside of work: 98% say the company encourages them to balance work with their personal lives, thanks to an onsite fitness center, fully-paid medical coverage and unlimited tuition reimbursement. And though Millennials are often viewed as being ultra-casual, the young employees at Intuitive say they value its “best dressed” code, since it demands professionalism. One employee commented, “I enjoy working with each member of my team, all the way up through management. There is a great sense of family, everyone truly cares, and I believe that each person at Intuitive is cheering for me to be successful.”


David Weekley Homes

Revenue: $1.1 billion
Headquarters: Houston
Number of employees: 1,082 (U.S)
Millennial headcount: 27%
Best Companies rank: 13

Millennials at this homebuilder take the company’s motto “Building Dreams, Enhancing Lives” to heart: 97 percent say they feel they make a difference, and one employee said, “My work has special meaning. This is not ‘just a job."” Founder and Chairman David Weekley inspires his younger charges with a “servant leadership” ethos that stresses integrity and giving back. One employee commented, “Mr. Weekley puts such an emphasis on philanthropy that it is truly touching. I am in awe at the steps he has taken to better our communities near and far.”


Allied Wallet

Headquarters: Los Angeles
Number of employees: 1,032
Millennial headcount: 97%

More than 90% of the workforce at the 12-year old e-payment processing firm are less than 35 years old. They enjoy great office views on Hollywood’s Sunset Strip, special events, fun parties and free Friday lunches where everybody sits and eats together. Top performers are given monthly gifts. Last year the employee of the year received a new Mercedes Benz convertible.

Says one Millennial employee: “The office happy hour is unreal. The people are all off-the-hook.”


Ultimate Software

Revenue: $332 million
Headquarters: Weston, Florida
Number of employees: 1,734
Millennial headcount: 25%
Best Companies rank: 20

This HR and payroll software developer finds reasons to celebrate whenever possible and regularly sponsors cocktail hours and karaoke contests, as well as monthly birthday celebrations. CEO Scott Scherr has created a Silicon Valley culture in South Florida, in part through “48 Hours” events where employees can work on pet projects for a 48-hour period as long as it’s related to company goals.

Says one employee: “I feel at home when I come to work, where everyone is happy to see me, and ready to help. I have never had a bad day of work at Ultimate. My manager is so personable and fun, while also having a sense of authority and management. I feel comfortable around her knowing she treats me with respect, and also trusts me to get the job done.”



Revenue: $50.1 billion
Headquarters: Mountain View, California
Number of employees: 42,162
Best Companies rank: 1

Still among the coolest of companies for the younger set, with its famous perks, do-no-evil mantra and world-changing power, Google’s workforce, unsurprisingly, is largely Millennial and Gen X. And yes, the benefits are stellar (onsite cafes, wellness centers and services ranging from dry cleaning to bike repair to oil changes), but Millennials also groove to Google’s GOOG 0.02% smart leadership: 96 percent say the company has great leadership that welcomes innovative suggestions from employees.

Says one employee: “I am challenged to bring great ideas to the table, and those ideas are valued and encouraged. There is also a significant, dedicated initiative to ensuring employee well being. Access to gyms, relaxation classes, massages, and delicious organic food make Google incredible.”


DPR Construction

Gregg Mastorakos

Revenue: $2.54 billion
Headquarters: Redwood City, California
Number of employees: 1,356
Millennial headcount: 28%
Best Companies rank: 10

This contracting and construction management firm based in the Bay Area hits the mark with Millennials in part by putting safety first: 99 percent of staff rate it a safe place to work. No wonder — the company offers safety awards and incentives and once gave an employee a new Ford F-150 truck when he achieved 30,000 consecutive work hours without an accident.

The company also ties construction jobs to bigger purposes. When workers reached a milestone on a project to expand a biotech manufacturing facility, it invited a cancer patient to speak to staff. One employee commented, “DPR continues to empower employees and remain true to the core values and mission that it ‘Exist to Build Great Things,’ and you really feel like you are making a difference alongside some amazing people.”


Boston Consulting Group

Headquarters: Boston
Number of employees: 2,552
Best Companies rank: 3

Employees love the way this strategy consulting firm blends high-impact work, high-integrity leadership, and high levels of camaraderie. And despite a rigorous selection process for employees, Millennials love its friendly culture. BCG gives a hand to those starting out as homeowners; new consultant hires can borrow up to $100,000 from the company at low interest rates to make a down payment on the purchase of a home. Says one employee: “It doesn’t feel like a corporate setting. Everyone knows one another and is comfortable chatting as such. It feels like everyone is just a big family. I can be supporting the CEO and it would be just like chatting with a friend. Completely comfortable and inclusive.”



Revenue: $983 million
Headquarters: Sheboygan, Wisconsin
Number of employees: 912
Millennial headcount: 25%
Best Companies rank: 1 (Best Medium Companies to Work For)

This insurance firm is a blast: 97% say it’s a fun place to work, thanks to wacky events like a chocolate fair, a circus, lunch-time performances from stand-up comedians, game shows and a talent contest. One Millennial commented, “I have never worked for a company that has an upper management team that is so forthcoming and approachable. They are always praising us and you can tell we actually are making a difference in the organization. I love coming to work and doing my job. It’s just an added bonus that we often get special treats like food and gifts as well as parties to celebrate our success as a company.”


Quicken Loans

Ray Rushing Quicken Loans

Headquarters: Detroit
Number of employees: 8,386
Millennial headcount: 64%
Best Companies rank: 5

The online mortgage lender wins over Millennials with a fun, egalitarian culture: 98% of younger employees say they are treated equally regardless of title. Each new team member is given the personal cell phone numbers of the CEO and other members of the leadership team. During Detroit’s bankruptcy woes last year, employees said they admired the company’s dedication to the city and its ongoing contributions to the community.

And according to one employee: “There is no ‘boss’ or ‘manager’ — we have leaders who are willing to help all of our team members. It’s 100% open door for any person in the company. You are provided with the atmosphere and materials to be successful here. I’m blessed to have been given this opportunity!”


World Wide Technology

Photo: Tara Wujcik

Revenue: $5 billion
Headquarters: St. Louis
Number of employees: 2,060
Millennial headcount: 35%
Best Companies rank: 34

World Wide Technology co-founder Jim Kavanaugh is a former pro soccer player, and his background in sports has helped set a team mentality that permeates the business.

According to an employee, “I genuinely feel that many of the people care about me and what is going on in my life. They have also allowed me to progress rapidly and at such a young age, not questioning if I can handle it because I’m young. I came in as an intern and they allowed me to hire on full time and become a supervisor. Very few organizations would allow such a young person to progress so quickly through the ranks, recognizing that merit can be proven at any age.”

via 10 Great Workplaces for Millennials.

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Top 10 Great Workplaces for Millennials

Google - Best Companies to Work For 2014

Rank 1

Prev Rank 1

Number of Years on List 8

Industry Media

HQ Location Mountain View, CA

Year Founded 1998

Type of Organization Public

Google’s stock climbed past $1,000 in 2013 — a boon for Googlers, all of whom are stockholders. CEO Larry Page urged them to be “audacious,” especially in philanthropy. Google donates $50 for every five hours an employee volunteers. Last year a new program sent employees to Ghana and India to work on community projects.


Total US Employees 42162

Total Employees outside the U.S. –


New jobs (1 year) 4236

FT Job Growth in the past Year –

% voluntary turnover –

Number of job applicants past 12 months excluding current employees –

Number of Openings (as of 1/2/14) 4000


Job function or title of largest number of FT salaried employees Software Engineer

Total Compensation for Salaried Position –

Job function or title of largest number of FT hourly employees –

Total Compensation for FT Hourly Position –


Offers Fully Paid Sabbaticals No

Offers Onsite Childcare No


Pays for 100% of Healthcare Costs No

Offers onsite fitness centers Yes

Subsidizes Offsite Gym Memberships Yes


Offers job sharing No

Offers compressed work weeks No


% Minorities –

% Women –



Average hours of training per year that regular FT salaried employees receive –

Average hours of training per year that regular FT hourly employees receive –

via Google – Best Companies to Work For 2014 – Fortune.

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Google - Best Companies to Work For 2014

An inside look at Google’s data-driven job interview process

If you interview for a job at Google, don’t expect the person making the hiring decision to be your prospective boss.

When it comes to evaluating job candidates, “what we’ve found is you’re much more objective if you understand the space but do not necessarily have a stake in that person actually joining,” said Sunil Chandra, Google’s vice president for staffing and operations.

And that’s why Google, the Silicon Valley giant known for its attractive workplace benefits and whimsical office culture, uses panels of varied employees — not potential direct supervisors or peers — to handle its worker selection process.

First, a small group of staffers interviews the candidates. Then a second committee reviews all materials about the applicants, including those they submitted on their own behalf as well as interviewer feedback.

“That comes from the thesis of removing any kind of trace of subjectivity,” Chandra said.

As with so many aspects of its human resources operations, Google’s job candidate interview methods are heavily data-driven. The firm recently generated buzz in the talent industry when it said it had done away with the notorious brain teaser component of its interviews after statistics showed the ability to ace them had no correlation with success at the company.

But that’s not the only way analytics have shaped the process. Google used to conduct many interviews before settling on a job candidate and making an offer. But through analytics, the company has determined that after four interviews, they don’t achieve a much greater degree of confidence about whether the interviewee is a good fit for a position. As a result, they’ve capped the number of interviews that they’ll put a candidate through.

Data also have led Google to conclude that speed is of the essence when it comes to hiring recent graduates.

“When we’re on campus, we’ve found over the years of analysis there actually is an inflection point in terms of the length of time you take to make an offer and students’ availability,” Chandra said.

So for these roles, they might move faster to fill the job than they would for a more senior position, Chandra said. On average, Google takes about 45 days to hire.

While science is deeply embedded in Google’s hiring protocol, Chandra said there’s one aspect that remains a bit of an art: Determining an individual’s “Googliness,” the company’s term for how well someone will fit into its workplace culture.

To understand someone’s “Googliness,” Chandra said they’re looking to figure out, “Are you innately curious? Are you someone that just wants to know more about stuff? Are you someone that likes working in teams and likes getting people together?”

That Google uses hiring panels to interview new workers means that a broad swath of its workforce must be trained in how to question job candidates.

“Hiring is pretty much part of everyone’s job, it’s part of everyone’s DNA,” Chandra said.

That’s why sometimes a job candidate will face a fifth interviewer for a position at Google, even though the company’s data show four interviews is the sweet spot. That fifth person is a “shadow interviewer” who is simply training to conduct interviews for future job seekers, and that person’s analysis isn’t included in the decision-making process.

For all of the emphasis on efficiency in Google’s job interview procedures, its initial applicant screening process seems to focus more on thoroughness.

The company receives a deluge of Web applications each year from 2 million to 3 million people, a figure that does not include the legions of other candidates Google identifies through referrals, career fairs and other sources.

It’s easy to imagine that amid a volume that crushing, many applicants might get skipped over or forgotten. But Google says that’s not the case.

“We’re really concerned about false positives and false negatives, so we spend a lot of time reviewing pretty much every application we get,” Chandra said.

To make sure they don’t miss out on top talent, Google employs a team of full-time screeners to sift through applications. The company would not say how many people are employed in these roles, but it said the group is “sizeable.”


An inside look at Google’s data-driven job interview process – The Washington Post.

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An inside look at Google’s data-driven job interview process

Tuesday, September 23, 2014

Does IT Strategy Matter?

We are fast approaching 2015 strategy-setting season for those companies in which the fiscal and calendar year align. Most companies will contemplate and define strategic objectives around revenue growth, cost reduction, customer satisfaction, mergers and acquisitions, and the like. More strategically mature companies will filter those down into strategic plans for the functional units and business units of the company such as Marketing, Sales, Finance, Operations, Human Resources, and product and service areas of various kinds.  The information technology department must bring to life many of the strategic imperatives of the other functional units and business units of the company. As a result, IT needs to weave itself into the narrative of each of those plans.

Increasingly, I have heard CIOs and other IT executives say, “There is no IT strategy; there only business strategy.” This sounds great, especially for a division of the corporate structure that has historically referred to itself as separate from “the business.”  The problem is that this would seem to suggest that there is only one strategy: the enterprise strategy. When you extend this logic, it would suggest that there need not be a Marketing strategy, an Operations strategy, product or service strategies, HR strategies, and the like.  For instance, if the enterprise strategy suggests that revenue growth is projected to be 15% for the next year, what role will Marketing play in building those revenues, and how will that division go about driving them? The Marketing strategy should provide that additional detail. Likewise, the Sales strategy should do the same, providing its version of the translation.

IT should have its own plan that is a complement to the strategies of the other divisions. As I note in my new book, Implementing World Class IT Strategy: How IT Can Drive Organizational Innovation, IT must develop a means to engage the rest of the units of the company earlier so that they can play a role in shaping the plans of each of those units, and after IT has a better understanding of what is necessary, an IT-specific strategy should be formulated.

IT Strategy Puzzle v2To continue with the example of Marketing and Sales, it may become apparent in the plans of those divisions that data analytics is path toward revenue growth for each of them.  There may be a desire to collect more data, synthesize it, and make better decisions. In learning this, IT may determine that it does not have an adequate data warehouse to be able to store the quantity of data envisioned by the rest of the organization. As a result, it may not have the tools necessary to synthesize the data. If neither of those are in place, it probably means that there are not many IT staff with the sophisticated analytics skills to fully capitalize on the vision that the other divisions envision. An IT strategy would address these gaps, highlighting an imperative to build out supply of skills and the infrastructure necessary to meet the demands of several divisions of the company. It is not a completely different plan, but rather IT’s translation of where it will focus its attention in order to help breathe life into the priorities of other organizations.

What steps should IT undertake in order to do this? My book provides exhaustive detail on methods to follow along with stories from a variety of major company CIOs.  Here are some highlights:

  • Where functional unit and business unit strategies do not already exist, engage the leadership teams of these divisions, stating a desire to learn more in order to deliver more to each
    • It is important that these not be conversations about technology solutions, but rather about business needs, opportunities, and challenges

    • IT should be tasked with identifying the solutions to each once the needs of the other divisions have been clearly laid out

    • IT should develop a simple framework reflecting what the CIO and the IT team has heard from each of the other divisions as imperatives with IT aligning existing or potential project ideas to as many as is appropriate, perhaps with multiple options to pursue in some cases

  • Suggest a common framework for each of them to use
    • As aforementioned, for those divisions without an existing plan, IT can develop one itself, playing back what it has learned from each division

    • Even in those scenarios where plans exist, the problem often is that different divisions of the company use methods that are not common.  It may seem innocuous for one division of the company to have a plan that is high-level and fits on a single page, while another division creates a plan so detailed that it requires 50 pages to explain, but IT must prioritize its efforts for these divisions and all others creating a “1 to n” list of projects, aligning the greater IT team against certain projects now, some later, and others not at all. Without a common framework with objective criteria used for each, IT has the thankless if not impossible task of determining these priorities, and the CIO may inadvertently make enemies of other leaders who feel their biggest priorities are not being met

  • Align members of the IT team with each of the other divisions of the company
    • I have advocated the creation of a group of “business information officers” or BIOs who  report to the CIO, but align with the divisions
      • In larger companies, BIOs should align with a single division of the company

      • In small or mid-sized businesses, BIOs may align with multiple divisions of the company

    • What is important is that the BIOs be involved in the strategy setting sessions of each of those divisions of the company, becoming an advocate in both directions

    • They should readily listen for imperatives raised by a business unit that can be filled with existing IT solutions, and push back on the need to invest anew, but they should also recognize when new, strategic needs are identified that warrant fundamentally new investments

  • Once IT has inputs from across the other functional units and business units of the company, it is positioned well to create an IT-specific strategy, highlighting where IT will focus its attention, noting from where each IT objective has been derived
    • This will render explicit the connection points between the strategic plans of various divisions of the company and the strategic plan of IT

Ultimately, each division should have its own strategy because the entire team should be made aware of the objectives that they are helping to drive.  If IT is left behind, accepting the notion that it does not have a separate plan, there are no shared objectives that everyone in IT supports, delivers against, brainstorms new ideas for, and the like.

Implementing World Class IT Strategy provides examples of from companies like Google GOOGL -0.43%, ADP, P&G, Marriott, BNSF Railways, Microsoft MSFT -0.95%, and Ecolab ECL -0.64%, to name a few.  (Listen to excerpt from chapter one by clicking this link. This strategy season, push to be a more active participant in the strategic planning processes of the other functional units and business units, and develop a solid IT strategy that provides an idea of IT’s major objectives for the foreseeable future.

via Does IT Strategy Matter?.

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Does IT Strategy Matter?

Want A Job In The Future? Go Into Health Care

At a time of widespread unemployment, one part of the economy has consistently provided jobs: health care. In the past decade, the industry added 2.6 million positions, according to a report last year from the Brookings Institution. That’s a growth rate of 22.7%, compared to just 2.1% for all other sectors.

What’s more, health care jobs are widely distributed. Every community in the country added clinics and hospitals and the employment that goes with them. In some places, health care now accounts for 20% of all economic activity. Estimates show health spending making up a fifth of the entire economy by 2021.

So, if you’re worried about finding a job in the future, health care may be a safer bet than most. Certainly the government thinks so, too. These charts, created by Chris Walker at Mic.com, use data from the Bureau of Labor Statistics. Of jobs requiring an associate degree, nursing will continue to be the most job-rich option, they show. By 2022, there will be more than 2.3 million positions.

The BLS expects a 21.5% rise in “health care practitioners and technical occupations,” a 28% jump in “health care support occupations,” and 23% growth in “other health care support occupations,” including “massage therapists” (22.6%) and “dental assistants” (24.5%).

It also forecasts plenty of employment for general managers, elementary teachers, and software developers. Other types of workers will be less in demand, however. The biggest projected falls will be in “farmers, ranchers, and other agricultural managers” (-19.3% by 2022), “postmasters and mail superintendents” (-24.2%), “reporters and correspondents” (-13.8%), “embalmers” (-15%), and “meter readers, utilities” (-19.2%).

Better a career in medicine than the postal service.

via Want A Job In The Future? Go Into Health Care | Co.Exist | ideas + impact.

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Want A Job In The Future? Go Into Health Care

An Over-reliance on Contractors Could Hurt Startups

Uber, Homejoy, Spoonrocket, and Taskrabbit are the names of just a few Silicon Valley startups that rely entirely on contract workers to make their businesses fly.

A prime reason they do this is that they don’t really have to hire anyone, instead they use an army of so-called 1099 employees, who work for an hourly wage, with no benefits and very few of the legal protections that full-time employees typically get. Venture capitalists have tended to like this model, as it keeps the companies nimble and scalable, and have ponied up billions of dollars in funding for such enterprises.

The point was dramatically brought home by an expose Thursday in New York Magazine by Kevin Roose, who wrote about his engagement with the house cleaning site Homejoy. His contract worker housecleaner, obtained for the day for a promotional $19, lived in a homeless shelter. (Homejoy closed a $38 million round led by Redpoint Ventures in 2013.)

While Homejoy said its cleaners typically earn between $17 and $20 per hour, the questions raised about the employment practices of sites that use contract workers are legion. And it also illustrates the disconnect between the protected bubble of Silicon Valley, the elite venture capital firms that grease its machinery, and a new class of workers.

An ongoing legacy of the financial crisis is that millions of workers have lost their jobs and are still lacking fulltime employment. Many have turned to low-pay or freelance work to suport themselves, and freelancers are expected to make up 16 percent of the economy, by some estimates, by 2020.

While some startups, such as Taskrabbit, have attempted to address some of the inequity invovled in being a contract worker, for example by setting a wage floor of $11.20 an hour–higher than the proposed national minimum wage of $10.10–and providing some discounts for health care and transportation, contractors clearly need a lot more.

It’s not just the contractors who are at risk. The startups could jeopardize their own brands by having a substantial disconnect with their employees. Look no further than the problems Uber and Lyft have had with some of their drivers, who undergo only a cursory background check before they start driving customers.

The answer may lie, as Roose suggests, in a path to real benefits such as those offered by the on-demand personal assistant application Alfred. The site reportedly gives its workers benefits like health insurance as they establish themselves in the network once they work 20 hours a week or more. (Class actions in Massachusetts and California challenging contract worker status at some of the on-demand companies may also alter the landscape.) In the meantime, Silicon Valley entrepreneurs and venture capital firms could start with a resounding acknowledgement that contract wokers are essential to the businesses they run

via An Over-reliance on Contractors Could Hurt Startups | Inc.com.

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An Over-reliance on Contractors Could Hurt Startups

Thursday, September 18, 2014

The Biggest Mistakes I See on Resumes, and How to Correct Them - Laszlo Bock (SVP, People @ Google)

I’ve sent out hundreds of resumes over my career, applying for just about every kind of job. I’ve personally reviewed more than 20,000 resumes. And at Google we sometimes get more than 50,000 resumes in a single week.

I have seen A LOT of resumes.

Some are brilliant, most are just ok, many are disasters. The toughest part is that for 15 years, I’ve continued to see the same mistakes made again and again by candidates, any one of which can eliminate them from consideration for a job. What’s most depressing is that I can tell from the resumes that many of these are good, even great, people. But in a fiercely competitive labor market, hiring managers don’t need to compromise on quality. All it takes is one small mistake and a manager will reject an otherwise interesting candidate.

I know this is well-worn ground on LinkedIn, but I’m starting here because — I promise you — more than half of you have at least one of these mistakes on your resume. And I’d much rather see folks win jobs than get passed over.

In the interest of helping more candidates make it past that first resume screen, here are the five biggest mistakes I see on resumes.

Mistake 1: Typos. This one seems obvious, but it happens again and again. A 2013 CareerBuilder survey found that 58% of resumes have typos.

In fact, people who tweak their resumes the most carefully can be especially vulnerable to this kind of error, because they often result from going back again and again to fine tune your resume just one last time. And in doing so, a subject and verb suddenly don’t match up, or a period is left in the wrong place, or a set of dates gets knocked out of alignment. I see this in MBA resumes all the time. Typos are deadly because employers interpret them as a lack of detail-orientation, as a failure to care about quality. The fix?

Read your resume from bottom to top: reversing the normal order helps you focus on each line in isolation. Or have someone else proofread closely for you.

Mistake 2: Length. A good rule of thumb is one page of resume for every ten years of work experience. Hard to fit it all in, right? But a three or four or ten page resume simply won’t get read closely. As Blaise Pascal wrote, “I would have written you a shorter letter, but I did not have the time.” A crisp, focused resume demonstrates an ability to synthesize, prioritize, and convey the most important information about you. Think about it this way: the *sole* purpose of a resume is to get you an interview. That’s it. It’s not to convince a hiring manager to say “yes” to you (that’s what the interview is for) or to tell your life’s story (that’s what a patient spouse is for). Your resume is a tool that gets you to that first interview. Once you’re in the room, the resume doesn’t matter much. So cut back your resume. It’s too long.

Mistake 3: Formatting. Unless you’re applying for a job such as a designer or artist, your focus should be on making your resume clean and legible. At least ten point font. At least half-inch margins. White paper, black ink. Consistent spacing between lines, columns aligned, your name and contact information on every page. If you can, look at it in both Google Docs and Word, and then attach it to an email and open it as a preview. Formatting can get garbled when moving across platforms. Saving it as a PDF is a good way to go.

Mistake 4: Confidential information. I once received a resume from an applicant working at a top-three consulting firm. This firm had a strict confidentiality policy: client names were never to be shared. On the resume, the candidate wrote: “Consulted to a major software company in Redmond, Washington.” Rejected! There’s an inherent conflict between your employer’s needs (keep business secrets confidential) and your needs (show how awesome I am so I can get a better job). So candidates often find ways to honor the letter of their confidentiality agreements but not the spirit. It’s a mistake. While this candidate didn’t mention Microsoft specifically, any reviewer knew that’s what he meant. In a very rough audit, we found that at least 5-10% of resumes reveal confidential information. Which tells me, as an employer, that I should never hire those candidates … unless I want my own trade secrets emailed to my competitors.

The New York Times test is helpful here: if you wouldn’t want to see it on the home page of the NYT with your name attached (or if your boss wouldn’t!), don’t put it on your resume.

Mistake 5: Lies. This breaks my heart. Putting a lie on your resume is never, ever, ever, worth it. Everyone, up to and including CEOs, gets fired for this. (Google “CEO fired for lying on resume” and see.) People lie about their degrees (three credits shy of a college degree is not a degree), GPAs (I’ve seen hundreds of people “accidentally” round their GPAs up, but never have I seen one accidentally rounded down — never), and where they went to school (sorry, but employers don’t view a degree granted online for “life experience” as the same as UCLA or Seton Hall). People lie about how long they were at companies, how big their teams were, and their sales results, always goofing in their favor.

There are three big problems with lying: (1) You can easily get busted. The Internet, reference checks, and people who worked at your company in the past can all reveal your fraud. (2) Lies follow you forever. Fib on your resume and 15 years later get a big promotion and are discovered? Fired. And try explaining that in your next interview. (3) Our Moms taught us better. Seriously.

So this is how to mess up your resume. Don’t do it! Hiring managers are looking for the best people they can find, but the majority of us all but guarantee that we’ll get rejected.

The good news is that — precisely because most resumes have these kinds of mistakes — avoiding them makes you stand out.

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The Biggest Mistakes I See on Resumes, and How to Correct Them - Laszlo Bock (SVP, People @ Google)

How Negative Online Company Reviews Can Impact Your Business And Recruiting

Job candidates have never been in a better position to research potential employers, and employees have never been more empowered to spill it all when it comes to reviewing their current workplace. Sites like Glassdoor, Vault and CareerLeak give interview candidates and employees the unprecedented opportunity to share the inside scoop on what it’s really like to interview or work at a particular workplace; and this is leaving many employers feeling more than a little uncomfortable at the prospect of receiving public negative reviews.

One recent example of a company getting blasted on Glassdoor is Technorati.com, after a recent decision by its CEO to close its contributed content program in an effort to re-brand. According to multiple reviews on Glassdoor, many long-time contributors to Technorati.com were abruptly terminated, without thanks, respect, or appreciation. Many reviews from these employees and contributors reference Technorati’s CEO as the reason the company is “a sinking ship” and “taking a rapid nose dive.”

So, how does this sort of feedback affect future recruiting and business growth? According to research into consumers’ use of online reviews, 88% of people have been influenced by an online customer service review. And while the research into how online company reviews impact employee job decisions doesn’t reveal quite the same degree of influence, we do know that a significant number of job seekers rely on these sites when evaluating a potential workplace.

In one study, for instance, out of 4,633 random job seekers surveyed, 48% had used Glassdoor at some point in their job search. The study also found that 60% of job seekers would not apply to a company with a one-star rating (on a five-point scale). This suggests that many job seekers do seem to use workplace review sites, and negative reviews can dissuade them from applying to a particular company. 

How to Deal with Negative Reviews

If you’re an employer who has received negative online reviews, you’re likely to feel powerless; there aren’t a lot of options to defend yourself. There are steps you can take, however, to salvage your reputation and get the ball swinging back in your court. Here’s how.

1.   Ask your current employees and interview candidates to leave reviews

Since disgruntled employees are much more likely to leave reviews, actively requesting reviews company wide may help by increasing your overall ratio of positive to negative reviews. You may want to include links to the review site in your employee newsletters, verbally ask job applicants to leave a review following an interview, or periodically have managers remind employees of the opportunity they have to give feedback to management.

 2.   Respond to all reviews – positive or negative

Review sites give employers the opportunity to respond to reviews, and the value of this opportunity shouldn’t be underestimated. No one wants negative reviews, but the more optimistic among us believe these can be harnessed for good – if dealt with properly.

If you receive a negative review, respond as quickly as you can. Job seekers will not only be reading the reviews, they’ll be looking to see how businesses respond to these reviews. Responding promptly and politely will show you care about the opinions of your employees; and this can go a long way to minimizing the impact of a negative review.

 3.   Take the issue offline, and leave a brief update once the issue is resolved.

As much as possible, try not to engage in discussions of details; the last thing you want is to air your dirty laundry online, or get into a “he said, she said” situation. Respond in a non-defensive way that shows you’re listening, and whenever possible, take the conversation offline as quickly as possible.

For instance, rather than specifically addressing negative remarks, you could say, “Thank you for your valuable feedback. I would love the opportunity to talk with you about your experience in detail. Please contact me at your earliest convenience.” Where possible, having someone directly involved in the situation respond is preferable to having a customer service agent respond.

Once an issue is resolved, it’s always a good idea to ‘put a cap’ on the discussion. For instance, “It was great speaking with you, and I’m glad we could resolve this issue.”

4. Request that defamatory reviews be removed

If a review is defamatory or you suspect it’s been left by a troll, you do have some recourse. While you can’t delete a review, you can sometimes request that the post be reviewed by a member of the publisher’s team. From what I can tell, the burden of proof is then on the reviewer to show that the review is in fact legitimate.

Looking for more help with managing your online reputation? See my article, Your Guide to Online Reputation Management.

Limitations of Company Review Sites

While company review sites are an excellent tool for getting insider data like salary reports and for getting a feel for the general landscape of a workplace, job seekers should be aware of the potential limitations of these sites.

1. Negativity Bias. Our brains are wired to be more sensitive to negative news – in this case negative reviews – than to positive ones. It takes many more positive reviews to outweigh the effect of negative ones; and this means that even though a company may have an overall positive employee satisfaction rating, negative reviews are more likely to influence us.

In fact, some researchers estimate that (at least in marriage) it takes five positive interactions to make up for 1 negative one; and if we put this in the context of online job reviews, five positive reviews to make up for one negative one. And given that unhappy employees are far more likely to leave reviews, these review sites are likely disproportionately slanted toward the negative. Which leads us to point number two:

 2. Inaccurate Data. Research carried out by employee survey company Workplace Dynamics set out to determine how accurate Glassdoor reviews were for evaluating workplace satisfaction. They compared results from detailed surveys they had done with 406 companies to the corresponding Glassdoor ratings. The plan was to test the accuracy of the Glassdoor employee satisfaction scores with the much larger sample they had collected. The results? Almost no consistency between the two sources. They write: “We found that there was virtually no correlation—the overall Glassdoor star rating was a very poor indicator of what it is really like to work at a company.”

They pinpointed two main reasons for this: The number of reviews on Glassdoor only accounted for a very small percentage of total employees, and the reviews were disproportionately from “grumpy” employees. In fact, they found that unhappy employees were five to eight times more likely to leave a review on Glassdoor than happy ones.

Final Thoughts

Company review sites do help provide some important insights into company culture and employee satisfaction. For this reason, it’s critical that employers take negative reviews seriously, and respond to them in way that showcases their commitment to employee satisfaction and a positive work environment. While the ratings aren’t necessarily an accurate representation of overall job satisfaction, individual reviews  – and perhaps more importantly, employer responses to these reviews – do hold weight when it comes to evaluating potential employers.

via How Negative Online Company Reviews Can Impact Your Business And Recruiting.

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How Negative Online Company Reviews Can Impact Your Business And Recruiting