One Model Announces Updated 2018 Trade Show Schedule

Find our team in a city near you, and stop by in person to learn more about our workforce analytics solutions.

February 9, 2018 - Austin, TX - The One Model team recently returned from the People Analytics and Future of Work (PAFOW) in San Francisco, where we participated as a key sponsor and speaker. There, our CEO, Chris Butler, was invited to announce a preview of our latest feature: One AI.

(Above) One Model CEO, Chris Butler, announces One Model's newest tool: One AI, at PAFOW in San Francisco.

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Predict Turnover Risk for $0.57c using AWS Machine Learning  - Part 1

We scoffed when you predicted he would leave, six weeks later he was gone. Never in a million years would I have said he would leave
— One Model AWS ML Test Customer
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People Analytics Data Integrations - the Good, the Bad, and the Ugly

Our Experience Integrating HR Tech for People Analytics - a Vendor by Vendor Comparison.

This will be a living post and we will continue to update as we have time to collect thoughts on each vendor and certainly as we complete integrations to new vendors.  Not every source we work with will be listed here but we'll cover the majors that often work with.

At One Model we get to see the data and structure from a load of HR systems, and beyond, bascially anything that holds employee or person data is fair game as a core system to integrate for workforce analytics.

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Smaller organizations have a surprising appetite for People Analytics.

The biggest change in people analytics that surprised me in 2017 wasn't any new leap in technology or shiny new object. For me, it was the growth in interest anduptake by smaller organizations.

Traditionally this space has been reserved for companies that had statistically significant populations and budget's to match them. They could hire a team to build and grow HR analytics and had discretionary budget to spend on tool-sets to assist them. A few years ago you rarely would have seen a company with less than 5,000 employees spending resources on these initiatives. The last couple years and this year in particular however we've seen a substantial increase in appetite from companies with less than 1,000 employees.

In fact, the smallest company I spoke to in 2017 was barely over 100 employees.

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One Model Announces Launch of Recruiting Analytics for SmartRecruiters Customers

Recruiting Analytics is now available for SmartRecruiters Customers

We’re happy to announce that a standalone Recruiting Analytics version of One Model is now available for SmartRecruiters customers through their online marketplace.  We often deliver recruiting analytics in combination with the rest of workforce data from HRIS, Payroll, Talent Management, Survey, and any other system that holds employee data.

However, we see there is a need to assist talent acquisition teams to gain greater visibility and insight into their efficiency and effectiveness. 

Having previously worked with SmartRecruiters customers, One Model can now offer a comprehensive suite of metrics, analytics, and dashboards to analyze, and distribute data visibility out to TA teams and beyond.  We are thrilled, as this new offering will allow us to better serve SmartRecruiters customers. One Model is excited to announce that Measurement, Content, and Collaboration on all your SmartRecruiters data will now be available for $99 per month.

What do you, a SmartRecruiters customer, get?

Out of the box we deliver a catalogue of metrics, and analytics. These include dashboard content to get you started with key measurement, reporting, and analysis across the data set.  You can completely customize this content, create your own metrics, and build custom-made dashboards - all of which you can publish to the team.

Most of our TA customers will share this content with the wider team, use it to manage workloads, and track pipeline and efficiency with more flexibility and insight to the data set than they receive from the ATS itself.

See below for a few sample dashboards

  • explore-1.png
  • recruiting_create_metric-1.png
  • recruiting_effectiveness_1-1.png
  • recruiting_effectiveness_2-1.png

How does it work?

Simply.  All we need is for you to create an API key for us to use to extract data.  Our software will connect to the Smart Recruiters API’s, extract, and rebuild a historical transactional data warehouse that our analytics engine sits on top of.  We then roll out our SmartRecruiters data models, metrics, attribute dimensions and dashboards, and provide you with access to the application for review.  This all happens in a matter of hours from receiving the API key.  You can then review, alter, and/or create any of your own content to create the view of talent acquisition that fits your business best.  Our content is but a starting point; use it, edit it, or throw it away in favor of your own, we provide complete flexibility.  

But our data is bad.

Usually we hear this when TA teams know that recruiters are not using the ATS as intended to track candidates moving through stages.  We can overcome many of these challenges with inference or synthetic events, but we find that most teams are able to show recruiters their own data and why it is important to use the ATS correctly. 

One of our customers gives access to all recruiters and shows them how they are being measured and reported to leadership.  When a recruiter’s own data is lacking it is obvious and has become a trigger for change in process and accountability.  Sharing this data allows for a natural progression to improve how the ATS is used and TA is measured as a result.

What’s special about what you do?        

All the integrations we build do more than just extract the raw data; we typically need to extend and transform the data so it fits an analytical context.  In the case of SmartRecruiters we need to use more than just the available Analytics API.  On its own it is fine for basic reporting, but it misses some context around historic status values that we would use for measurements like time between step/status. 

In order to provide this full data context we end up extracting across all of SR’s API’s and not just the analytics API.  We merge all these pieces and our model provides a full historical view of the data. As such, this is a custom integration and model we have built for Smart Recruiters as we do for other ATS that provides the needed level of event granularity for measurement. 

Who should have access to Recruiting Analytics?

A TA leader or operations manager is a perfect place to start but we do recommend giving access to the whole TA team.  Providing democratized access to the data allows everyone to understand how they are being measured and prompts them to invest in ensuring data accuracy by using the ATS appropriately. 

The level of access to data can be controlled using our role-based-security. The customer may choose to show all data and allow recruiters to compare themselves to their peers or only provide access to a user’s own data. 


Expanding beyond Recruiting

One Model is a full HR data platform. We can accept data from any HR system and any piece of business data you can lay your hands on.  We often find that TA is able to move beyond measuring efficiency into looking at effectiveness of the function by measuring the post hire performance of the employees.  To do so we would typically tie in the HRIS, Talent Management, engagement data sets to be able to provide the additional context to the ATS data and truly measure quality of hire from a post hire perspective. 

When you have the appetite to expand your data view, we can add more data to your existing solution.  One Model customers can make use of our data models, metrics, and advanced augmentations for more complex algorithmic calculations and machine learning for prediction. 

Get in touch with us today or check us out through the Smart Recruiters marketplace to get rolling.  At $99 you have very little to risk. 

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One Model Secures $3.7M in Series Seed Investment

On behalf of the One Model team I am excited to announce on our third anniversary of founding that we have secured an amazing seed round investment of $3.7M to take our people data platform to the next level for our customers.  

2017 has been an incredible year of growth for us and has shown that our approach and value proposition resonates strongly with customers.  So much so that we are yet to lose a single customer (0% churn) which is just about unheard of for a SaaS company that is now three years old.  

Our vision hasn't changed, we believe that in order to fully deliver on the value to be found in people data organizations need One Model to connect and understand the data held in the dozens of systems they use to manage the workforce today.  Effectively we become a secondary system of record free of the constraints that transactional systems (HRIS, ATS, Talent Management, Payroll) suffer from. 

The last three years have been spent building out our core data platform, to connect and accept data from any source, to understand all the behaviors between data sets, and deliver our bespoke reporting and analytics platform.  With this powerful framework in place we can add in more of the high value use cases that ordinary organizations would never be able to achieve on their own.  Extending data with external sources, advanced algorithmic calculations, your own custom R/Python programs, and our incredible new automated machine learning tools.  All running within our data pipeline and managed by HR.  

We're incredibly excited about our immediate future and this investment gives us the resources to chase it down.  

Chris Butler

(press release below)

One Model Secures $3.7 Million in Funding to Fuel Growth in HR Data and Analytics Software Market

Austin, Texas, November 1, 2017 – One Model, the people data strategy platform, announced the closing of $3.7 million in Series Seed funding from The Geekdom Fund, Otter Consulting, Techstars, and Lontra Ventures.

The One Model team will leverage this additional funding to fuel its international growth strategy, accelerate enterprise adoption for its products, and to further develop its leading HR data and analytics platform.

“Getting to know the One Model team over the past couple of years made it an easy decision for us to want to lead this round. From the beginning, the team has been able to address major enterprise needs with their powerful HR data analytics platform driving data insights from machine learning, delivering this to customers flexibly while making implementation easy,” voiced Don Douglas, Managing Director of The Geekdom Fund. “One Model's people analytics infrastructure has changed how a number of organizations plan, execute, and evaluate their HR strategies and we are excited to support the proliferation of this game changing platform.”

“The HR departments of multinationals see the value proposition that One Model brings to their infrastructure, evidenced by the rapid growth One Model has experienced. The implementation time and dollars saved are enormous,” states Mike Wohl, the Investment Manager of Otter Consulting. “The future looks very bright for One Model and all of the companies that utilize their offering.”

The Austin, Texas-based startup is uniquely positioned to address a key pain point within the HR industry and is primed for growth. The company’s platform removes the heavy lifting out of extracting, cleansing, modelling, and delivering analytics from your workforce data.

“One Model sits at the center of all people data held by an organization. As such, we’re in a unique position to understand, extend, and deliver organizations with transformative value from this data. Our vision is that every company will need what amounts to a secondary system of record that connects together all of their disparate people systems and provides a level of insight that no transactional system can achieve on it’s own. We’re only beginning to scratch the surface of what is possible with the level of HR system interaction we are now achieving, and this investment allows us to double down on our approach” says Chris Butler, CEO of One Model.

Founded in late 2014, the company has rapidly grown to support HR data and analytics needs of customers in over 156 cities around the world. This additional round of funding continues to authenticate the universal need for improved HR data and analytics management, and to validate One Model’s decision to assume a leadership role in addressing these data challenges head-on.

“One Model leads the charge as the HR industry embraces analytics to improve career satisfaction, retention, and equity. The team is comprised of true industry experts who understand the nuances of enterprise software and the power machine learning. One Model’s robust pipeline of enterprise and channel customers will transform the lives of millions of professionals across the globe,” according to Andrea Kalmans, Lontra Ventures.

About One Model

One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle.  Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to.  We provide a full platform for delivering more information, measurement, and accountability from your team. Request a demo today at

About The Geekdom Fund

The Geekdom Fund is a venture capital fund that invests in early stage IT startups in San Antonio, South Texas and beyond. It is managed by Riverwalk Capital, LLC.

About Lontra Ventures

Lontra Ventures is an Austin, Texas based entrepreneurial consultancy that specializes in life science consulting, and technology for high-growth companies.

About Otter Consulting

Otter Consulting, LLC operates as a venture capital firm. The company, which is headquartered in Florida, provides early stage venture capital financing services.

About Techstars

Techstars Ventures is the venture capital arm of Techstars. Techstars Ventures has $265M under management and is currently investing in their third fund ($150M). Alongside the VC and Angel communities, they co-invest in companies built by Techstars accelerator companies and alumni.

For questions, please contact Stacia Damron, Senior Marketing Manager at



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The end of the snapshot for people analytics: Workday edition

I recently made a simple post on LinkedIn which received a crazy amount of views and overwhelmed us with requests to take a look at what we had built. The simple release was that we had managed to take workday's point in time (snapshot) based reporting and rebuild a data schema that is effective dated, and transactional in nature. The vast majority of organizations and people analytics vendors use snapshots for extracting data from workday because this is really the only choice they've been given to access the data.  

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How to Game the Best Company to Work For Awards

This is continued from a "Gloves Off Friday Post" by Mike West on Linkedin Pulse here:  How the Best Company Award is Wrong

How The Best Companies To Work For Are Ranked.

Different newspapers, magazines and institutes have different methodologies to rank order companies, however the thing they have in common is that a large portion of the rating is based on an employee survey. 

Below is what it states on the Fortune website:

"To identify the 100 Best Companies to Work For, each year Fortune partners with Great Place to Work to conduct the most extensive employee survey in corporate America.

Two-thirds of a company’s survey score is based on the results of the Trust Index Employee Survey, which is sent to a random sample of employees from each company. This survey asks questions related to employees’ attitudes about management’s credibility, overall job satisfaction, and camaraderie. The other third is based on responses to the Culture Audit, which includes detailed questions about pay and benefit programs and a series of open-ended questions about hiring practices, methods of internal communication, training, recognition programs, and diversity efforts.

Glass Door also bases its rating on survey questions.

"To determine the best places to work, Glassdoor looks at company reviews provided by employees between November 13, 2013 and November 2, 2014, in which individuals are asked to consider and rate such factors as overall satisfaction, CEO leadership, career opportunities, compensation, and work-life balance." 

To the credit of the rating agencies, asking employees at different employers the same questions seems like the fairest and most scientifically valid way to compare employers to each other. 

How to Game it.

Knowing surveys are the most substantial part of the ranking, the key to gaming Best Company to Work For Awards is to know the natural distribution of attitude by employee characteristics and use this information to increase the % of employee surveyed in segments that have higher positive attitude than average, while decreasing the sample rate in those with lower positive attitude than average. 

Relatively unsophisticated rating agencies, such as local newspapers, could easily be exploited in this manner. On the other hand some rating agencies, like Fortune and the "Great Company to Work For Institute" will reply to this with, "we use a random sample". Unscrupulous HR bosses do not be deterred. While using a "random sample" seems like a great way to prevent manipulation, this too can be beat. The unscrupulous HR boss could beat random sampling by proportionally manipulating the quantity of email addresses from different segments he/she provided to the rating agency (determined by an understanding of the natural response of varied segments) and/or he/she could just filter which emails allowed to pass through the email server.  To be clear - I am making a point - I am not suggesting a company should cheat, however if a company wanted to do so this would be how they could do so without directly standing behind employees shoulders while they fill out the survey or offering to throw pizza parties for the groups with the highest results. By the way, you better believe that some companies and managers DO do things like that. 

I am not aware of a specific circumstance where a company has deliberately manipulated a survey process, however I am aware of circumstances where companies have benefited indirectly without the knowledge of the rating agency.

Here is the Problem.  

How positively people respond to questions at their employer varies reliably by certain employee characteristics and these characteristics are not uniformly sampled across all employers.   Characteristics that may statistically matter extend from natural demographic distributions (age, gender, ethnicity..), to natural job type distributions (professional, skilled labor and unskilled labor), to natural geographic distributions, to other characteristics we may not even typically record. I left out the most important. The characteristic that I have found to consistently vary between segments by substantial margin, unrelated to the actual quality of the company, is Tenure Group. Company Tenure is calculated something like this (Current Date - Start Date) and is usually grouped something like this (0-1 Year, 1.1 to 3 Years, 3.1 to 5 years, 5.1 years to 10, 10+ Years).

What it Looks Like.

A typical tenure group pattern looks something like this:

Typical Employee Engagement Pattern by time in job

It is worth mentioning that Tenure Distribution is at least partly driving geographic and industry differences. You can see this if you consider that the labor market characteristics of geographies and industries have a relationship with the proportional distribution of Company Tenures. Faster growing local economies and industries have lower overall tenure so these populations would also have proportionally more people in low tenure groups. 

In the graph below note the growth characteristics of our leading industries. Think about the growth characteristics of the industries to the right.  See it?

PWC 2015 Report
PWC 2015 Report

Does it Matter That Much?

You might say, "Come on!, How much could this problem really matter? Actually a lot!

The phenomenon can be observed in rare cases when the #1 Company Award unexplainably flips away from a company in one year and returns to them in a future year.  What explains the difference is decreased hiring rate, relative to other nearby companies on the list.


This is a little far fetched but the other thing you could do to game the award is to hire a large number of people right before the time of year of the awards and/or right before you apply for the award the first time.  I can't say anybody does that to win awards of this nature intentionally, or anyone ever would, but some benefit from a massive growth rate that ensures this will happen for them whether they try to do it or not.

Should We Care?

"Google has been on the list for 10 years with this being its seventh time at No. 1, thanks to sparking the imagination of its talented and highly compensated workers,  and by adding perks to an already dizzying array of freebies ." 

The first reason we should care is that the companies that win these awards receive substantial press and as a result receive a remarkable increase in the amount of job applicants. Think something akin to a million new applications to Google. If this is coupled with an increased ability to filter job applicant pools to identify high quality candidates then these #1 picked employers have a substantial competitive advantage in ability to select the most highly qualified workers. Further, these employers have a PR gains from which to take key talent away from other companies and keep their own key talent.

Another reason we should care is that many organizations try to imitate the "Best Practices" of the companies that are highly ranked on these lists. The companies that want to be like them may unfortunately be imitating characteristics that have no actual relationship to what makes a great company to work for, or the reported survey results and therefore arbitrary.  Recall, correlation does not imply causality. Trying to imitate all of the practices of the purported great companies may result in investments that generate no return and simultaneously decrease margin, thus making it more difficult for the imitating company to compete in the future. This could provide substantial advantages to companies that can make the top of the list AND afford to give up a small portion of big margin to spend above average on salary and unusual employee perks.

I have written extensively about how Best Practices lead us astray in a prior blog post : 7 Reasons Best Practices Are Not Best For You

What can be done about it?

For starters the newspapers, magazines and rating agencies could sample survey responses in tenure groups to ensure an apples to apples comparison. Instead of a random sample this would be called a "Stratified Random Sample".  If they really wanted to step up their game they could also just put all of the data into a single multiple regression model, to isolate a company effect from tenure effects and any other variables that may skew results, be they demographic, job related, geography or whatever. This sounds complicated but actually any undergraduate statistics major or any graduate behavioral level science major could run this analysis.  

Now, as I state repeatedly, I am not suggesting anyone should really try to game the Best Company to Work For Awards, however I can understand why you would want to up your game to truly improve employee engagement and be a truly great employer.  The best way to do so is to look across data sets and use the engagement data in increasingly better ways to get better at actually moving engagement. Survey providers are good at managing the process of constructing a good survey and collecting data but provide a very limited view of the data and no survey providers work with their survey data, plus your other data sources to provide a single longitudinal view of the truth.  No survey provider maintains ongoing relationship with your sources that adjust with underlying structural changes automatically and that you can query in real time. I know of some employers who can look at survey data in this way, however they cannot do so while maintaining employee confidentiality as a survey provider would.  The world is now in luck - One Model can take data from a survey provider (or tool) and join it into a single view of the truth with other employee related data, allowing a longitudinal view, update automatically, and most importantly, can do this while maintain employee confidentiality just a survey provider would (by not allowing you to report data below a sample size threshold) :-)  If this interests you, let us know and we would be happy to provide you with a demo so you can see for yourself what sort of new advantages this can give you!


This is a continuation of a "Gloves Off Friday" post : How the Best Company Award is Wrong

More like it:

Why Josh Bersin is Wrong About Embedded Analytics?

The Most Dangerous Technology in HR Today

What Your HR Technology Sales Rep Doesn't Want You To Know


Who is Mike West?

Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. 

Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, and PeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology startups. Mike is currently the VP of Product Strategy for One Model -the first cloud data warehouse platform designed for People Analytics.

Connect with Mike West on Linkedin | follow Misc-People Analytics | or join One.PeopleAnalytics.Community

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CHRO Guide to People Analytics - Issue 5

This continued from the CHRO Guide to People Analytics - Issue 4

What went wrong?

The mistake Donna made is that she spent a disproportionate amount of time focusing on a causal relationship story that she wasn’t able to realize. Her focus was not bad and not wrong in any universal sense, however it failed to deliver the impact on the business that would propel her and the organization to the next place. It was just another activity; one among many.  In a sense it was a high risk wager. She spent the time and resources of the entire organization on an initiative with an uncertain outcome. The uncertainty of the impact, coupled with the wager of credibility, time and resources to achieve that impact created a huge amount of risk for Donna and for the organization.  As described failure in this case is not catastrophic for the business, however it was costly, and it was particularly costly in this example for Donna   Jumping into an implementation like this makes it  impossible to learn anything until a great deal of time and resources have been spent.

Bob had a much better start deciding to focus on creating a scalable HR reporting tool set. The use of his time is arguable better than Donna’s and also not wrong in any universal sense, however it also failed  to deliver the impact on the business that would propel him and the organization to the next place.  Despite good intentions, he found himself drowning in data—and confusion—as he spent time and resources to scale reporting too soon. His team initially spent too much of their time focusing on the reporting technology, and then subsequently could not ascertain the signal from the noise, which scattered attention from where it was most needed - taking Bob’s team off course. Bob’s effort also represents a wager with risk.  An unfortunate consequence of a mistake like this it could result in the wholesale abandonment of HR Metrics or People Analytics entirely…   It also was impossible to learn anything until a great deal of time and resources were spent.


If you accept this definition of waste;  “Waste is any human activity which absorbs resources but creates no value.”  — James P. Womack and Daniel T. Jones, Lean Thinking, then there is a very high probability there is a lot of waste in HR. If nothing else you must admit, at the current time, there is much uncertainty about the current and long term value of specific HR actions and programs. So we can’t really say how much waste is here. That is a problem.

Limited Resources

When it comes down to it:

  • Organizations are constrained by limited time and resources.

  • Human Resources in particular is constrained by limited time and resources.  

  • You and I are constrained by limited time and resources.  

HR may be the most resource constrained of all the business functions.  I have spent 16 years working in HR for some very successful companies. Finding a budget for the things we wanted to do was 80% of the battle. The other 20% of the battle was trying to figure out how to take costs out of Health Insurance, which has been increasing above the rate of inflation each year for the last 30 years. If you understand the concept of compound interest and that 30% of the people cost in a large organization in typically found in Benefits, most of which is Health Insurance, then you have an appreciation for the challenge.  I worked for companies that cared about their employees, however felt obligated to try to maintain some reasonable rate of cost expansion over time. Knowing this, how can you simultaneously go to Management team and ask for more money for other things?

In a function that does not have a history of applying rigorous evidence based scrutiny there should be no problem finding waste once you open these activities up to scrutiny. The more important question is how do we prioritizing the biggest areas of waste to address.   The challenge is identifying the few key actions that stand to deliver the greatest impact and ignore the others.

“The essence of strategy is choosing what not to do.” – Michael Porter

When operating in an environment riddled with extreme uncertainty and limited resources, if you look for it, you will find opportunity to make major strategic impact. In this sense HR may be positioned to be one of the more strategic business function, not the least.

Traditional HR Metrics lead us astray for the following reasons:

  • Because it is not clear how to relate HR actions to business impact, we settle to monitor activities as a measure of progress. Measuring progress as activities that have an unknown relationship to current business objectives leads HR into waste.
  • Because HR is broken into multiple functional centers of excellence (Staffing, Benefits, Compensation, Labor Relations, Talent Management, Organization Design), each with different goals and activities, we end up with hundreds of metrics that do not align with each other and do not drive towards a unified goal. This results in efforts that either have no impact or work against each other, not too mention waste in the process of analytics itself. eg waste.
  • Because we have not previously devised of a single HR metric that has a direct business impact that can be applied universally across organizations and sub organizations, we substitute simplistic measures that while a good intention, may not be a universally good idea, may conflict with other objectives and may not correlate in any way with measurable business impact. This results in the wrong efforts/objectives. eg waste.
  • Investing heavily in quantitative metrics doesn’t automatically give us solutions. Metrics can usually tell us what’s going wrong, usually not why. The more you invest in quantitative metrics, with a process for qualitative input, the more you end up drowning in a sea of non-actionable data. Not only does non-actionable data not help us reduce HR waste, it embodies waste itself.
  • Even when you are focused on important measures, unless we can connect cause and effect, we can’t identify and leverage the specific elements that will bring us success. This too results in waste.

The Twist

The answer to the problems of an organization is not a better set HR activities, it is a dynamic process of identifying the right actions at the right time.

It is helpful to visualize the value stream of an organization not as one giant process, but rather as a system of interconnected processes. You can visualize this concept by imagining the value stream as links in a chain.

At any given point in time, one of these links is going to be the weakest link or constraint in the system. If we apply stress to this chain, the entire chain will not fall apart. It will break at its weakest link. Trying to reinforce all the links at once is wasteful because it will not make the chain stronger as a whole. This is the premature optimization trap.

In other words, when we’re trying to improve any sort of system, we derive the biggest return on effort only when we correctly identify and focus on the weakest link. 

We can derive two further insights from this. The first is that reinforcing the weakest link will eventually yield zero returns, because another link will eventually take its place as the constraint or the bottleneck, limiting the performance of the entire chain.  The second takeaway is that because we cannot predict where the constraint will move, we need to constantly monitor the entire system in search of the next weakest link. Blindly optimizing a single part of the system—even if it was once the weakest link—will eventually lead to waste. This is the local optimization trap.

Our People Analytics Models are no different. At the earliest stages of a People Analytics Model, the weakest links typically live in your business impact and problem assumptions. If those assumptions fall apart, everything else in your People Analytics Model also falls apart. Focusing on anything else, like scalability, is premature optimization. Beyond the earliest stages, no two problems or organizations are the same. You can’t afford to simply guess at what’s riskiest. You need a systematic process for uncovering what is riskiest.

Donna’s approach of rushing into a solution is a classic example of falling into the local optimization trap. Even though Donna’s team was working tirelessly to optimize a local objective : implement a best in class performance process (local optima), it was at the expense of the overall system throughput (global optima).  Her team should have invested effort first toward finding the weakest link or constraint in the people model, then collectively focused on solutions for breaking just that constraint.

This writer wishes to build on these concepts and marry systems thinking, LEAN and the scientific method to tackle the Human Resources and People Analytics challenges I have outlined often in previous blog posts.

Why is People Analytics hard?

First, there is a misconception around how successful earth shattering People Analytics get built. The media loves stories of “wunderkind” nerds invading HR who are so smart they helped the moribund HR function (usually at some cool tech company) figure this problem out. The reality, however, rarely plays out quite as simply.Even the unveiling of the hiring algorithms at Google, in Laszlo Bock's words was years in the making, built on the contributions of many and several incremental innovations (and failures). Google also has a lot of people working in People Analytics, and a management that self decided, “we will make all decisions with data.” My point is this, it wasn’t some genius, by him or herself, in a room shouting “Eureka, I have it!”

Second, the classic technology-centric Reporting or “Business Intelligence” approach front-loads some downstream business partner involvement during a “requirements-gathering phase” but leaves most of the HR business partner and business customer validation until after the reporting solution is released. There is a large “middle” when the Analytics function disengages from the ultimate intended users of these reports for months, maybe even a year, while they build and test their solution. Sometimes the solution is rolled out in HR first, just to be sure it is safe for humans before inflicting it on the rest of the organization.  Imagine a few wild eyed HR people hiding in the bushes outside the office preparing to jump up on an unexpecting executive on his way into work one morning. During this time, it’s quite possible for the Analytics function to either build too much or be led astray from building anything remotely useful to the organization. The most interesting thing to me is that this is the exact, fundamental, dilemma of Startups described by Steve Blank in The Four Steps to the Epiphany,  Eric Ries in Lean Startup and Ash Maurya in Running Lean in which they advocate for people phobic tech entrepreneurs to “get out of the building” for building a continuous customer feedback loop throughout the product development cycle.  Why they say it is important, most startups fail and nobody wants to spend their life building something they think is wonderful that it turns out that nobody else wants to use.  Folks, I have worked in the startup of 6 People Analytics functions and I am telling you, it turns out it is exactly the same thing. Pay attention here: exactly the same thing.

Third, Modern HR people are sensitive to the idea that : a.) we are here to serve the business, b.) business executive x is our customer, c.) those two things together mean we should do we do whatever executive x wants. The problem : even if the business executive x has all the answers, you simply cannot ask them what they want.

Quintessential to this point, maybe you have heard the following:

If I had asked people what they wanted, they would have said faster horses.  —Henry Ford
It is not the customer’s job to know what they want. —Steve Jobs

However, if you study the life work of these two you may also notice, they didn’t forget about the customers and build whatever they wanted. They listened, observed and ultimately meditated deeply on their customers.  Of course, behind “faster horses,” you might hear that they are really asking for something faster than their existing alternative, which happened to be horses. They didn’t know to ask for a car and they certainly could not have given Ford the details of the car or how to manufacture a car more cost effectively than anyone else in a list of requirements.   As Steve Jobs would put it, don’t ask them to do that, “that’s our job”.

Given the right prompting, customers can clearly articulate their problems, but it’s our job to come up with the solution.  Why is Apple one of the best at it? Probably because they have worked for a long time on how to embody the idea that the best way to solve old problems is to “think different.”  

Fourth, People are complex and messy.  People are not structural engineering challenges that are within the abilities of an engineer to control precisely. People and organizations are not like machines or computers.  There is always a certain degree of uncertainty about the effect of our actions on people and organizations. We try things based on an entirely plausible premise and they fail. Usually we had not factored in or considered the thing or things which made it fail. There are too many variables, too many possibilities and too much change occurring within and all around us. Is this not in some sense the beauty of life?  Would you rather take this away?  In human systems, failure is not the problem, the problem is failure to learn from the failure.  If we want to improve HR we should shift our attention to how we can learn more quickly.  

Is there a better way?

The new method of People Analytics I am proposing provides a better, faster way to more effectively deploy HR resources and build successful organizations:

  is about speed, learning, and focus.

  is about engaging business leaders and employees throughout the analytics development cycle.

  tackles both analytics and solution validation in parallel using short iterations.

  is about testing a people strategy by measuring business impact.

  is a disciplined and rigorous process.


The aim of this author is to suggest metrics that when combined into a system view define a working HR model. Armed with this model, you can justify the investment of your time, iterate to ideas that work, and communicate progress with your internal and external stakeholders— without drowning in a sea of numbers.

(Follow this blog or Mike West on Linkedin to get future issues)


Other Issues:

CHRO Guide To People Analytics - Project Intro

CHRO Guide To People Analytics - Issue 1

CHRO Guide To People Analytics - Issue 2 - Intro - What is it you say you do?

CHRO Guide To People Analytics - Issue 2

CHRO Guide To People Analytics - Issue 3 - Intro - How Do We Start This People Analytics Thing? IF THEN NOW DO

CHRO Guide to People Analytics - Issue 3 

CHRO Guide To People Analytics - Issue 4 - Intro - Towards a New Method of Human Resource Decisions

CHRO Guide to People Analytics - Issue 4

CHRO Guide to People Analytics - Issue 5 - Intro - How traditional HR Metrics lead us astray

CHRO Guide to People Analytics - Issue 5


Who is Mike West?

Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. 

Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, and PeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology startups. Mike is currently the VP of Product Strategy for One Model -the first cloud data warehouse platform designed for People Analytics.

Connect with Mike West on Linkedin | follow Misc-People Analytics  | or join One.PeopleAnalytics.Community


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CHRO Guide to People Analytics - Issue 4

This is continued from the CHRO Guide to People Analytics - Issue 3

HR needs data more than anyone

Most of the programs HR watches over have very large budgets. Labor costs are frequently 70% or more of revenue.  Benefits for may represent 30% or more of labor costs. On an absolute basis these costs increase over time as the employee base grows.  Things get sideways when business plan projections get off track and the cost of labor grows faster than revenue or when revenue retracts. It is not easy to pull back on labor costs quickly. Some commitments you can’t back out. Others you can pull back, however not without doing serious damage to employees sense of trust, confidence and morale. For this reason it critical for CHROs to be able to identify—quickly, early, and accurately— whether a project or activity is worth pursuing, rejecting, continuing or dropping so it may protect its commitments and preserve resources for those programs that drive the most value.

Relative to the budgets of other business functions, the resources it Human Resources receives to carry on its operations may be the most constrained.

Besides the obvious constraint of budget, the other constraint is credibility. In order to influence, HR professionals need to hold on to and build on what little credibility they start with. As CHRO you will have to justify HR’s right to have a seat at the business table by demonstrating the business impact of your programs to a CEO, management team or business line head to whom you support.  At some stage, you will all be called on to demonstrate progress. 

Finally, we are all constrained by time. Every minute spent on an activity that is doomed to fail is wasted. As a salaried employee in a large organization you may in one sense be able to temporarily escape accountability for the financial impact of their decisions, however will never be able to escape time.  How do you want to spend the best years of your productive time on this earth? What do you want your contribution to humanity to be?

[ Constraints beg that we make the most of every decision ]

HR has historically relied on two categorical measures of progress: how much stuff they are doing and how much people like what they are doing. Yet unfortunately, both of these metrics are unreliable proxies of business impact and both of these lead us down the wrong path—building something that ultimately does not matter, has no impact on the business or worse, the wrong impact.

Traditional accounting metrics, like revenue, expense, profit, and return on investment (ROI), aren’t helpful for HR because they all track numbers either don’t exist for HR, that will lead to a “race to the bottom” or that are impossible to calculate given complex dependencies. Many HR decisions impact these things over much longer development and impact so measuring the progress of HR inputs on these measures does not provide feedback in enough time to correct the problem.  If you make a big people related mistake, or miss a big people related opportunity that your competitors get right, and you get it wrong, you may not make it out of this one.

Most the HR functions I worked in over my career (some of the best companies on this planet) wanted to collect and analyze as much data as humanly possible. But in a world where we can measure almost anything, it’s easy to find ourselves drown in a sea of non-actionable data.  The major problem addressed by this book will be how to keep from drowning—and how to navigate the infinite abyss of possibilities and interesting, although fruitless analysis.


The wrong way to do it

Take a typical HR Executive—let’s call her Donna. She begins as SVP of HR for Fortune 500 company. She was brought in to turn this HR from the past to the future, and the first few years are the “honeymoon period” when anything seems possible. Donna is part of a new breed of HR professional. Donna believes it would make the HR team more effective to implement modern Talent Management, including a new performance management system.

The first problem is that in order to get funding for her vision Donna has to go to the Management team and ask for funding. Donna cycles over the next several weeks preparing a thirty-page slide deck. She knows that the trick to getting her plan funding is making a strong business case for it. Donna is actually pretty good at painting a vision. This is typically a comparison of where the organization stands versus “best practice” (assembled from white papers) complete with references of what the other companies we respect in our industry are doing.  In the meeting, most people are in agreement, except the CFO looks at her like she has two heads. He reminds her that the most important goal is to keep margins down. His point is underlined when it becomes clear that the rest of the management team are presenting their budget asks too. Let the fun begin. Where is Donna’s budget going to come from? Is HR going to take budget from the head of Sales or from Product Development? How about any other business function? How we compare this HR investment to other investments? After all, what is the ROI on this stuff?

Riding on the advantage of being recently brought in from a highly successful organization Donna carries enough credibility at this stage to get what she needs to get started. Donna gets the systems she wanted and agreement to go forward and she spends the next two years of her credibility executing this plan. Donna measures progress by ensuring that her team is implementing their projects on schedule and within budget.

Fast-forward two years. Donna’s team has been very busy and managed to launch all their projects. During this time the economy had been steadily improving from a previous downturn and turnover has been increasing.

At management team whenever a highly visible employees leaves fights break out as to whether or not is a problem, what is the problem and who is responsible?

Under pressure to demonstrate something to her stakeholders, Donna resorts to providing dashboards with basic headcount change and turnover measures – of whatever can be constructed from the available data. Because the HR system really only has capability to export lists of people - members of Donna’s team need to spend a week assembling turnover reports in Excel.   These reports show overall turnover, turnover by division, and location.

Given that even these basic things were previously unavailable to the management team, the initial feedback is positive.  5 members of Donna’s team create these reports, 1 for each division, spending 1 week them per month. As a few months pass by it clear that nobody is really using these reports. Still, these are the only data that HR provides out to the business and Donna and the Management team had agreed that Turnover would be the Key Performance Indicator for HR so they keep producing them.

At management team whenever this topic of key talent leaving comes up fights break out as to whether or not is a problem, what is the problem and who is responsible?

Donna tries to pitch a solution to turn this turnover problem around. All she needs is more HR programs and more money. CFO won’t budge.  At this stage there is even less money to go around than before and most of the shine of Donna’s newness has worn off.  After going through painful changes to the companywide Performance Management process everyone is leery of yet another program from HR. While most HR professionals would agree the Performance Management Process Donna implemented is a “Best Practice” approach, most managers and employees do not like the process because it takes up time, it’s awkward, the feedback isn’t received in a timely enough manner to make changes, they often don’t agree with the feedback, feel the process is rigged. Besides that, by design most employees receive a mediocre performance rating and without a precise description of how to get an excellent rating they don’t find this process particularly useful or motivating.

You know how this story ends, right? Over time, Donna is either asked to leave or she leaves herself.   


A better way still goes wrong

Take another HR Executive—let’s call him Bob. Bob also is SVP of HR for Fortune 500 company. Bob was brought in to turn this HR from the past to the future. Bob is also is a new breed of HR professional. Bob believes it would make the HR team more effective to first move out of legacy HR systems to modern HR systems and implement a data warehouse as a “single source of truth” and finally a visualization solution for others to see clearly what stories the data can tell the organization.

It’s not easy, Bob’s price tag is even higher, however like Donna, Bob initially has enough credibility to scramble together funding for the systems he wants.

Bob spends his first two years executing this plan to put in place a technology architecture for HR. Like Donna, Bob initially measures HR progress with schedule and within budget – not because Bob thinks that is great, but mainly because Bob doesn’t think there is anything else he can really do until the systems get up and running. 

As the team gets into the system implementation, to keep things on track and keep costs down, Bob decides to abandon the history of data in the previous HR system. It is just too cumbersome and costly to get the data out of the old system and into the format of the new system. It also is not clear what value if any this old data has to anybody.  None of the executives want to see that old data – feeling that data is probably ancient history and may be full of holes.  Some members of the team disagree but it seems a small sacrifice to make to stay on time and budget so Bob makes the call and moves on down the road.

Fast-forward two years. Bob’s team has been very busy and managed to launch all those systems. During this time the economy had been steadily improving from a previous downturn and turnover has been increasing.

Bob can provide many more advanced metrics and segmentation than Donna could. Bob's group can report by smaller segment sizes, using multiple segments filters at once, the report users can drill down into detail and Bob’s solution has much nicer visualization.  However, Bob's team also struggles with what to put on the Executive Dashboards. Bob’s team went from nothing to drowning in hundreds of metrics and segments.  After some interactions with client organizations by Bob’s HRBP team they land on some cuts people want : mostly they want to see data by Division and Location.

Like Donna, despite the simplicity of the reports the initial feedback from Management Team is positive. Unlike Donna, once setup Bob’s team does not need to spend much of any time to get these dashboards out. Actually, no time. That’s good.  However, as months pass, a member of Bob’s team decides to go into the backend and look at log reports. Bob finds out nobody is using these reports - Bob can’t believe it. Bob’s story is different than Donna’s story, but ends in a similar way.  What went wrong?

The average tenure of HR executives is about 5 years and HR systems this or less, If a new HR executive brings with them a series of new ideas, practices and systems, and big ideas take at least 2 years to pitch and fully implement and 2 more years to collect data, then assuming we even collected the right data, it means that by the time we can use this data to understand if we are on the right track everyone is ready to move on.  Something is wrong here. This is never going to work.


The aim of this author is to suggest metrics that when combined into a systems view of how an organization operates define a working HR model. Armed with this model, you can justify the investment of your time, iterate to ideas that work, and communicate progress with your internal and external stakeholders— without drowning in a sea of numbers.

(Follow this blog or Mike West on Linkedin to get future issues)


Other Issues:

CHRO Guide To People Analytics - Project Intro

CHRO Guide To People Analytics - Issue 1

CHRO Guide To People Analytics - Issue 2 - Intro - What is it you say you do?

CHRO Guide To People Analytics - Issue 2

CHRO Guide To People Analytics - Issue 3 - Intro - How Do We Start This People Analytics Thing? IF THEN NOW DO

CHRO Guide to People Analytics - Issue 3 

CHRO Guide To People Analytics - Issue 4 - Intro - Towards a New Method of Human Resource Decisions

CHRO Guide to People Analytics - Issue 4

CHRO Guide to People Analytics - Issue 5 - Intro - How traditional HR Metrics lead us astray

CHRO Guide to People Analytics - Issue 5


Who is Mike West?

Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. 

Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology startups. Mike is currently the VP of Product Strategy for One Model -the first cloud data warehouse platform designed for People Analytics.

Connect with Mike West on Linkedin | follow Misc-People Analytics | or join 



Read More