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13 min read
Nicholas Garbis
At some point, every successful People Analytics team will develop a meaningful partnership with the Finance organization. Unfortunately, this partnership is usually not easily achieved and it's quite normal for initial alignment efforts to last for a couple of years (or more!). We are delighted to repost this insightful blog post authored by Nicholas Garbis on May 4, 2021. Revisiting his valuable insights will help us all foster a deeper understanding of how HR and Finance can collaborate more effectively. A new or maturing People Analytics team may fail to recognize the effort level required and not prioritize the work needed to establish this critical partnership with Finance. They do so at their own peril. The day will inevitably arrive when a great analytics product from the PA team will be dismissed by senior leaders when they see the foundational headcount numbers do not match. The PA team will be lacking in a clear explanation that is supported by the CFO and Financial Planning & Analysis (FP&A) leaders. But why is this the case? And how can HR and People Analytics teams do a better job of establishing the partnership? Analyzing the analytics conflicts between finance and HR Lack of alignment on workforce data At the heart of the issue is a lack of alignment on the most basic workforce metric: headcount. Both Finance and HR teams are often sharing headcount data with senior leaders. In many companies, the numbers are different. This creates distrust and frustration, and I will contend that, given Finance’s influence in most organizations, the HR team is on the losing end of these collisions. End result is that the organization spends time debating the figures (at a granular level) and misses the opportunity to make talent decisions that support the various company strategies (eg, growth, innovation, cultural reinvention, cost optimization). While headcount is at the foundation, there are several other areas where such disconnects arise and create similar challenges: workforce costs, contingent workers, position management, re-organizations, workforce budgets/plans, movements, etc... Solving the basic headcount alignment is the first step in setting the partnership. Source of the Disconnect: "Headcount Dialects" and "Dialectical Thinking" The disconnect in headcount figures is nearly always one of definition. Strange as it may sound, Finance and HR do not naturally count the workforce in the same way. It's as if there is a 'headcount dialect" that each needs to learn in order to communicate with the other. Therefore, if they have not spent some intentional, focused time on aligning definitions and processes, they will continue to collide with each other (and HR will fail to gain the trust needed to build an analytics/evidence-based culture around workforce decisions). The dialectical thinking challenge is for Finance and HR to recognize that the same data can be presented in (at least) two different ways and both can be simultaneously accurate. It is for the organization to determine which definition is considered "correct" for each anticipated use case (and then stick to that plan). Primary disconnection points Two primary areas of disconnect are the definition of the term “headcount” and whether a cost or organizational hierarchy is being used. Definition of “Headcount”: There are several components of this, underscoring the need for alignment when it comes to finance headcount vs HR headcount. Using Full-Time Equivalent (FTE) or Employee Count: Employees that are working less than full-time are often in the system with FTE values of 1.0 (full-time), 0.5 (half-time), and every range of fraction in between. The Employee Count, on the other hand, will count each employee as 1 (sometimes lightly referred to as a “nose count” to distinguish it from the FTE values). In some companies, interns/co-op employees are in the system with FTE value of 0, even though they are being paid. Determining Which Status Codes are to be Included: Employees are captured in the HR system as being active or inactive, on short-term or long-term leave of absence (LOA, “garden leave”), and any number of custom values that are used to align with the HR processes. In many companies, the FTE values are updated to align with the change in status. Agreeing on which status codes are counted in "headcount" is required for setting the foundation. Organization versus Cost Hierarchy: The headcount data can be rolled up (and broken down) in at least two ways: based on the organization/supervisor hierarchy structure or based on the cost center/financial hierarchy. Each has its unique value, and neither is wrong -- they are simply two representations of the same underlying data. It’s quite common that insufficient time has been spent in aligning, reconciling, and validating these hierarchies and determining which one should be used in which situations. Organization Hierarchy: This is sometimes called the “supervisory hierarchy” and represents “who reports to whom” up the chain of command to the CEO. This hierarchy is representative of how work is being managed and how the workforce is structured. Each supervisor, regardless of who is paying for their team members, is responsible for the productivity, engagement, performance, development, and usually the compensation decisions, too. Viewing headcount through the organization hierarchy will provide headcount values (indicating the number of resources) for each business unit, each central function, etc... The organization hierarchy is appropriate for understanding how work is being done, performance is being managed, the effectiveness of leaders and teams, and all other human capital management concerns. It is also useful in some cost-related analyses such as evaluation and optimization of span-of-control and organization layers. Cost Hierarchy: This is sometimes referred to as “who is paying for whom” and is rarely in perfect alignment with the organization hierarchy. There is a good reason for this, as there are situations when a position in one part of the organization (eg, research & development) is being funded by another (eg, a product or region business unit). In these cases, one leader is paying for the work and the work is being managed by a supervisor within another leader's organization. I have seen "cross-billing" situations going as high as 20% of a given organization. When headcount is shown in a cost hierarchy, it indicates what will hit the general ledger and the financial reporting of the business units. It has a valid and proper place, but it is mostly about accounting, budgeting, and financial planning. Which business unit is right? The truth is that as long as you have all the workforce data accurately captured in the system, everything is right. This sounds trite, but it puts emphasis on the task at hand which is to determine a shared understanding and establish rules for what will be counted and how, which situations will use which variations, and what agreed-upon labeling will be in place for charts/tables shared with others. Some organizations that have a culture of compliance and governance could set this up as part of an HR data governance effort (where headcount and other workforce metrics would be defined, managed, and communicated). Going further, there is a need beyond the Finance and HR/People Analytics leader to socialize whatever is determined as these running rules across the Finance and HR organizations. These teams all need to be aligned. How does One Model help finance and HR collaborate? With a People Analytics solution like One Model in place, the conversations between HR and Finance can be had with much more clarity and speed. This becomes easier because, within One Model all of the workforce data is captured, data quality is managed, and all related dimensions (eg, hierarchies, employee attributes) are available for analysis. Two examples of content that is specifically designed to facilitate the Finance-HR alignment discussions are: Headcount Storyboard. Setting up a storyboard which shows headcount represented in multiple ways: FTEs versus employee counts, variations of which statuses are included/excluded, etc. This information becomes readily comparable with the metric definitions only a click away. Even better, the storyboard can be shared with the Finance and HR partners in the discussion to explore on their own after the session. One Model is the best tool for counting headcount over time. Hierarchy Storyboard. Providing views of the headcount as seen using the supervisor and cost hierarchies side-by-side will help to emphasize that both are simultaneously correct (ie, the grand total is exactly the same). This can also provide an opportunity to investigate some of the situations where the cost and organizational hierarchy are not aligned. In many cases, these situations can be understood. Still, occasionally there are errors from previous reorganizations/transfers which resulted in costing information not being updated for a given employee (or group of employees). With the data in front of the teams, the discussion can move from “Which one is right?” to “Which way should be used when we meet with leaders next time?” When you have One Model, you can bring HR and Finance together faster and more easily ... and that helps you to accelerate your people analytics journey. Need Help Talking to Finance? Let us know you'd like to chat.
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Featured
6 min read
Nicholas Garbis
How do we measure the value of People Analytics? Is your organization making better, more data-informed talent decisions today versus a year ago? This is the ultimate test of any People Analytics program, initiative, team, COE, or department. If the answer is yes, the investments in People Analytics continue and expand. If the answer is no, then People Analytics budgets are questioned. So how can we demonstrate the value of People Analytics? In our latest whitepaper, "Measuring the Value of People Analytics," we address this from the ground up, starting with the mission of people analytics and moving into the utilization of the content delivered by the People Analytics team. Click here to get the full version. With a more comprehensive view of the how People Analytics creates value, you will be better positioned to build your business case for People Analytics. Whether you are seeking initial, incremental, or transformational level investments, this value framework will help you to convince your organization to become fully invested in HR analytics. Tackling the ROI Conundrum The proposed ROI calculations that many vendors recommend for People Analytics are not very good -- and some are downright laughable. This is one of the reasons I worked on this paper. Two common approaches: Estimated savings through efficiencies of system consolidation or process acceleration Estimated savings from the consolidation of systems or accelerating processes. Reduction in attrition or faster time-to-fill of job postings or other KPIs. The promise that People Analytics technology will reduce turnover and putting a financial value on it ... then hiding when the 'Great Resignation' starts or saying 'it would have been worse!' Ugh! This is not honest or helpful. We can do better in declaring the value we propose to generate. This is one of the key points in the paper. We Need to Address the Big “Why?” Why are we investing in People Analytics? Is the deliverable we're committing to - the “return” on the investment - as simple as a bit of system and process savings and some hypothetical lift to a couple of KPIs? Mission of People Analytics: Drive better, faster, talent decisions at all levels of the organization. We are investing resources in people analytics to drive and accelerate this mission. The value of People Analytics should be judged by the quality of talent decisions that are being made across the organization. We may not be able to get directly at measuring the quality of talent decisions (though we will address that in an upcoming paper), but we can use utilization as a proxy to get started. If our People Analytics deliverables are being utilized, we can logically assume that the users are placing value on them. They are 'voting' for the content. If it was not valuable, they would ignore it. In the paper, we demonstrate how utilization can be used to calculate value with relative ease across your People Analytics portfolio. Value Journey for People Analytics Looking at each 'analytics event' through a process sequence, a "value journey," we will see how critical People Analytics content is in delivering value at scale. To impact talent decisions at all levels of the organization, we need to build a smooth and fast self-service cycle (left side) by focusing on: creating analytics mindset/culture, applying user-centered product design, and communicating effectively and applying sound change management. "We have data that can help here." The diagram below shows the target picture, where a user, encountering the talent elements of a business challenge thinks "We have data that can help here." This is the critical first step that ideally flows them into a set of high-quality People Analytics products that can deliver the needed insights. Any business challenge can be divided into talent elements (staffing, skills, productivity, etc.) and non-talent elements (market forces, supplier issues, etc.). People Analytics provides value through products and services that support understanding and solving for the talent elements of the challenge. To impact talent decisions at scale requires People Analytics teams to deliver insight-generating self-service solutions. So now that we’ve covered that, how do we measure the value of People Analytics at your company? Is there a formula we can use to make our People Analytics investments more intentional? If so, how can we determine: Where we should focus our efforts? What content or communications efforts are necessary to deliver the outcomes we expect? Another core assumption in People Analytics is that your leaders’ time is a scarce and valuable resource. And we will use that assumption to anchor our value measurement approach. We assume that your organization’s leaders: Are selective about what they spend their time on. Choose to spend their time on things they consider valuable. See value in content if they engage with it regularly. Will rely on content that continues to inform better talent decisions over time. Download the paper to see the way we have calculated the value of a small People Analytics portfolio based on the value-utilization framework. Further work is needed to articulate how to measure the change in talent decision quality more directly. We will be tackling that in future content -- so keep an eye out for it! Get the equation in our Measuring the Value of People Analytics Whitepaper Ready to see how upgrading your People's Analytics solution will improve the value your team is bringing to the business?
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Featured
11 min read
Nicholas Garbis
The role of the Human Resources function is to ensure that the organization has the talent it needs to execute its strategies, making HR a strategic partner for the business. So if you’re an HR leader, your focus must always be on making the best talent decisions – best for the organization and best for the people in it. People Analytics is the most important part of your HR strategy because the best decisions are always data-driven ones. Mission of HR: Deliver a sustainably high-performing workforce that is engaged in their work, having positive, inclusive experiences with the organization, its leaders, and their team. Pillars of HR Strategy I think of HR strategy as having two pillars, each critical to the successful execution of an individual HR team’s mission. The first is the delivery and engagement/execution pillar, and it represents the HR organization’s systems, goals, metrics, processes, policies, procedures, and programs. Pillar 1: Delivery & Engagement Delivery Engagement/ Execution Talent Acquisition Employee Value Proposition (EVP) Employee Experience (Journey Design) Performance Management Compensation & Benefits Internal Communications Succession Planning Talent & Workforce Management Opinion Survey HR Operations & Technology etc. Learning & Development etc. Manager Effectiveness etc. But data is not information, and information is not knowledge. The best decisions involve all of these attributes. That’s why the second pillar of an HR strategy is decision support. People Analytics is the engine that powers the decision support for talent. It consists of the systems that organize the HR data to generate insights, the products that enable the People Analytics team to achieve scale and deliver services directly to leaders. These elements will enable the organization to make the most optimal people decisions for the organization. Pillar 2: Decision Support via People Analytics Systems Products Services Data Warehouse Interactive Analytics Ad-Hoc Analytics Analytics / Visualization Storyboards & Dashboards Workforce Planning Organizational Network Analysis C-Suite/Board Reporting & Analysis Location Strategy HR Operations & Technology etc. Predictive Models etc. Market Analytics etc. Without People Analytics, the human resource strategy won't be supported by sound decisions and can't be implemented. This will jeopardize the HR mission and risk the overall organizational strategy. On the floor, this can manifest itself as having the “wrong people in the wrong seats” or leaders making decisions that result in a sub-optimal or under-utilized workforce or introducing risk. Learn how to calculate the value of People Analytics. Do you need a People Analytics Strategy? Yes, of course. Strategy involves making resource and prioritization decisions. All People Analytics strategies must balance technology and consulting choices and recognize that there is no single strategy that's suitable for everyone. Some organizations need decision support tools that are quick and flexible. Others require robust and secure tools to support extremely complex decisions and are willing to sacrifice speed. And the incumbent capabilities and change readiness of each organization will vary. A sound People Analytics strategy will support the ways in which your organization makes decisions. And yes, your People Analytics strategy should be aligned to support the overall organization’s strategy and the HR mission. People Analytics Mission: Ensure that people decisions at all levels of the organization can be informed by quality data and insights, delivered through products and services that are ethical, easy to use and supported by effective communications and training. People Analytics teams will vary in their strategies for technology, deliverables, operating model, internal collaborations, and communications. Your people analytics strategy should articulate the technologies, deliverables, operating models, and methods of communication that will enable the best talent decisions. These decisions will be made by central groups such as the HR leadership team, as well as HR and business leaders in every part of the organization. The value of people analytics is to be measured by the improvement in talent decisions. But how do you conceptualize that value, nevertheless measure it? The People Analytics Value Cycle The value of People Analytics is the degree to which people data and insights are integrated into the organization’s talent decisions. People Analytics deliverables that are underutilized such as unused models, reports, and dashboards all incur costs to maintain and they contribute to technical debt through decommissioning, reviewing, or redesigning. The People Analytics team generates value for the organization every time a talent decision is made using data or actionable insights. Here are the steps that decision makers take to generate value. Seeing the opportunity to apply data to the decision Clarifying what questions will need to be answered Knowing where to access the data & analytics Generating insights from the data & analytics Making decision on action to take Implementing the action Following up to measure the impact of the action Delivering value from People Analytics requires an understanding of the behaviors that you are trying to shape. People analytics technology can multiply the value created by the team. People Analytics Tools Accelerate Time to Value People analytics technologies are often never seen by the end consumers of its decision support. Most users will never interact with the back-end technologies like data warehouses and predictive models. The users will work with innovative front-end solutions such as storyboards, dashboards, and reports that have been designed specifically for HR and business leaders. People Analytics technologies need to accelerate the process of data being available and applied in talent decisions. Visual tools such as storyboards, dashboards, and planning tools that HR and business leaders will use in their talent decisions require the integration of many unique sources of data. The software platform and the visual design should give the People Analytics team flexibility to create what is needed. The team needs to be responsive to the demand for new content and the ability to easily mine new insights. It may be tempting for HR IT teams and data engineers to build the data warehouse internally, but it is likely to take too long and cost too much. Plus, there’s the risk that a DIY data warehouse ends up being less flexible than a software-as-a-service platform. A SaaS-based solution like One Model delivers data integration, data warehousing, pre-built and custom storyboards, and predictive modeling tools, all in one package. SaaS solutions tend to cost less with a faster time to value, and include continuous innovation as well. Another key technology consideration is the visualization front-end which will be used by HR and business leaders. Sound visual design of the interface and its graphical components create wider accessibility and accelerate decisions by giving users who can generate insights in a moment’s notice. We recommend a People Analytics technology roadmap that addresses these areas. Data sources. The upstream systems from which data must be integrated. Data processing. The way the data from these source systems will be extracted, transformed, and loaded, including derived data and metrics calculations. Content. Creation of effective visualizations, storyboards, dashboards, and reports. Predictive modeling. Clear prioritization of the predictive models to be explored and developed. Employ a Product Mindset More and more People Analytics leaders are adopting a “product mindset” with respect to their deliverables. The product mindset appreciates that users have choices when they are seeking insights and that the People Analytics deliverables need to be easy and insightful. A product mindset incorporates concepts such as portfolio management, road mapping, user research and feedback, benchmarks and metrics, deploying minimum viable products, and managing and communicating change. Adopting a product mindset will help ensure that the People Analytics team is always delivering value to the organization. Choose Your Operating Model There isn’t a perfect People Analytics operating model for any particular type of organization. There is no right answer, but some approaches will be better and generate more value than others. The key is to design your team intentionally with a focus on value. The team structure, roles and responsibilities, and processes must align with the needs of the internal customers. The team should be composed of an appropriate mix of technical and consulting capabilities. Some teams may need more data engineers, others may need more visual storytellers. Make It Happen Since every organization strives for better, data-driven support, People Analytics is a critical facet of an effective human resource strategy. Talent decisions are the most important decisions any organization can make, and can help make HR a strategic partner to the overall business. People Analytics is decision science for the HR function and is a key pillar of HR strategy. Making it happen means being able to communicate the value it will bring in order to get the investment and support you need. Start by calculating that value. Get the equation in our Measuring the Value of People Analytics whitepaper Ready to see how upgrading your people's analytics solution will improve the value your team is bringing to the business?
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Featured
21 min read
Nicholas Garbis
Retailers are riding a supercharged shopping cart full of change that has accelerated due to the pandemic and exacerbated by a one-company megatrend: Amazon. Amazon has over 10,000 people … just in Kentucky! That’s more than many retailers' entire organization. Accordingly, workforce issues are some of the biggest strategic challenges that the rest of US retailers face today. People analytics, which aims to improve decisions involving employees, work, and business objectives, can deliver immediate impacts to retailers by bringing better data and insights to leaders at all levels of the organization who are making workforce decisions every day. Every retailer has sufficient velocity and scale to make them great candidates for the enormous value that can be captured through analytics, modeling, and insight generation. People Analytics (PA): The application of data and insights to improve business outcomes through better decision making regarding people, work, and business objectives. (Source: Explore the Power of People Analytics, Whiteman and Garbis, 2020) Retailers once blazed the early trails of people analytics. In the first wave, from 2005-2015, big retailers were at the forefront. Unfortunately, the retail industry has mostly been surpassed in their people analytics prowess by peers in industries such as technology, financial services, and pharmaceuticals over the past decade. Perhaps too many of those analytics leaders moved out of retail and into other industries? The pressure of today’s HR challenges in retail should inspire us to find the innovative spark once again. Retailers are in a great position to drive change in their organizations through the use of effective people analytics. Using people analytics technology, they can unlock significant value and show how workforce issues are understood and resolved. There’s really no choice but to apply the best decision-making practices possible toward solving the workforce challenges. The entire business model depends on it. How might data help us to better understand this issue? How can we use data and insights in deciding the actions we should take? Find out more by downloading our Retail Whitepaper If you’re a human resources leader in retail, you may be inspired to rise to these challenges by applying people analytics to get the most value from your workforce data. Amazon has been rolling over traditional retailers, capturing market share and exacerbating workforce challenges. Challenge #1: Workforce Retention Is Not Just A Stores Issue Anymore Retail has always had high turnover rates, but the truly eye-popping annual rates of 100%+ were limited to the store locations. These rates were treated as an accepted fact, a cost of doing business, and an operational challenge for management. In the past, only modest efforts were made in response to turnover, such as faster training to decrease time-to-productivity, recruitment automation to decrease open rates, and better benchmarking of prevailing local wages through labor market analysis. Now warehouses and distribution centers are exhibiting turnover rates that rival the stores. Turnover rates in DCs and warehouses historically ranged from 30-40% annually. Today, retailers see figures approaching 100% or more. Wow! In the meantime, store turnover has remained persistently elevated. In response to this challenge, retailers have adopted a “hole plugging” approach that involves ramping up recruiting resources to backfill departing employees. The analogy to a draining bathtub fits; the drain keeps opening wider, and stores keep trying to open the faucet further in response. Retail hourly workers are leaving for wage increases in many cases, but they are also leaving for concerns such as scheduling practices, paid time off policies, Covid and other safety protocols, and career growth opportunities. And the competition across big box retailers is being compounded by Amazon’s exploding demand for workers to fill its massive warehouse expansion. People Analytics to Consider for Workforce Retention: New Hire Failure Rate. This metric involves calculating the portion of hires that do not last for 30 or 60 days at a given location, district, region, or across the total chain. Analysts would then explore “hot spots” and address the causes to improve recruiting sources, selection and evaluation criteria, onboarding processes, etc. Drivers of Turnover. Advanced analytical methods can help to determine the key drivers of turnover for stores and warehouse locations as well as up through the organizational hierarchy. Cost of Turnover. This metric helps HR teams calculate the cost to replace an average store or warehouse worker. Analysts should provide this information to leaders to ensure that these costs can be used in informing turnover reduction strategies. Cluster Analysis. This type of analysis helps to determine if there are groups of similar locations that have significantly different turnover rates. HR retail teams can then conduct qualitative interviews to discover best practices that can be tested in other locations. Challenge #2: The Digital Talent Squeeze The competition for digital talent seems to be growing more fierce every day. Talented developers, software engineers, product managers, and data scientists are moving between a wide range of industries and in/out of start-ups. Most significantly, Amazon, Google, Apple, Facebook, and Microsoft are swooping up talent by the truckload, bidding up salaries and emptying the shelves of more cost-conscious retailers. The competition is so unrelenting that newly-hired digital talent is even being coaxed away between their offer acceptance and their start date. Wage compression is a significant concern as starting pay for highly-prized new hires approaches that of their more experienced peers and even their leaders. The impact to retailers extends beyond the costs and frustrations of hiring and losing of new hires. Constantly open positions impede progress on digital innovation that retailers desperately need to remain competitive, such as customer-facing solutions that meet changing shopping patterns and automation in the supply chain and stores. As retailers struggle to fill these digital roles, they are becoming more open to remote and location-flexible talent. This is providing them with a wider talent pool to recruit from, but retailers may find it difficult to manage these exceptions as they return to physical offices when the pandemic subsides. Retailers who push too aggressively on a return to the office could lose the talent that they worked so hard to secure. Or they may half-knowingly end up with a two-tier policy where remote work is only available to those with rarified skills. People Analytics to Consider for the Digital Talent Squeeze: Internal Mobility Rate. This metric involves calculating the rate at which the digital talent moves into different roles. Stagnant pockets of ‘hoarded’ talent should raise concerns since that talent will eventually find opportunities outside (rather than inside) the organization. Recruiting Funnel Analytics. HR retail teams should identify the phase of the recruiting process where the most-qualified talent voluntarily drops out of the candidate pool. Within this broader withdrawal rate analysis you can look at the rate of offers being declined. There’s also plenty of value in diagnosing where the speed of the recruiting process can be improved. Retention Surveys. Develop surveys that create a deeper understanding of the factors that keep critical digital talent in their roles and get specific data to help learn if your employee value proposition (EVP) is clear and compelling for talent in key roles. Challenge #3: Seasonal Staffing Models May Be Broken In retail, the holiday season accounts for about 20% of annual sales, but occur during a 10% slice of the calendar. For roughly five weeks, every aisle and sub-aisle gets clogged with stacks of TVs, toys, and gadgets. Every register light is blinking and joyful music plays in stores. This is shopping nirvana, and retailers are at the center of everyone’s life. But staffing up to deliver on this experience is becoming more and more difficult. Some retailers have quantified lost sales due to their inability to staff at necessary levels. The tight labor market is not just due to competition in the mall or across the parking lot. Changing shopping patterns have forced retail warehouses to hire incredible numbers of temporary workers for the holidays. Limited supply and high demand forces temporary worker wages to increase, and now its not uncommon for a temporary seasonal worker to be enticed to another retailer during the short holiday season. So the seasonal staffing model that has worked for so many years may have finally broken. It may be impossible (or cost prohibitive) to find and ramp up the number of workers that are needed in such a short period of time. Rather than increasing recruiting efforts or recruiting earlier, some retailers are experimenting with more durable, lasting relationships with temporary workers. An “occasional” or “intermittent” employee type could keep workers active in the HR system if they work a certain minimum number of shifts within a certain period of time. There are many advantages to “occasional employment” that address the challenge of holiday staffing. Retailers can benefit from decreased recruiting costs, reduced paperwork, faster onboarding and time-to-productivity, higher retention rates, and more experienced customer service. Naturally, there are some costs associated with keeping more active employees on the books. If an occasional employee works just one shift per month, then that employee will probably be less productive than a full-timer on a per-hour basis. However, when the holiday season arrives, that occasional employee will be better prepared and more reliable, reducing the overall staffing costs of the store. An effective “occasional employee” strategy may require a change to a more flexible shift bidding and selection system. It’s also worth considering whether incentives can be created to encourage more hours. In ride sharing apps like Uber, surge pricing is built into the system. Perhaps there are surge wage accelerators for certain shifts? People Analytics to Consider for Seasonal Staffing: Seasonal Staffing Rate. Retailers can calculate the increase in staffing, including both headcount and total hours worked) that each store or warehouse has experienced.Some locations will have more hours from seasonal workers than others. Focus on where the biggest opportunities for impacts are. Seasonal Staffing Rehire Rate This metric helps to determine how successful a retailer has been at re-recruiting seasonal workers. Ideally, rehires should be associated with lower recruiting and onboarding costs and higher retention rates. This measure informs efforts to sustain relationships with seasonal workers outside of peak periods. Seasonal Staffing Cost Analysis. Retailers can develop a comparative cost model for sustaining “occasional” workers with the current seasonal staffing model. This foundational measure can lead to a cost-effective and timely staffing model alternative. Download the Whitepaper to Share with Colleagues Challenge 4: Unionization A labor relations leader at a large global firm once said, “No site ever got a union that didn’t deserve one.” His point was that unionization is a direct result of management’s failure to provide to workers the things they value most. In retail, workers appear to value: Wages that can sustain a worker who is working full time Transparent and timely scheduling practices, including schedules being published with sufficient and consistent advance notice. Pay for shifts that are cut short due to low customer or warehouse volume Acceptable policies and practices for bathroom breaks Limits to scheduling beyond stated availability or short-notice extra hours; Participation in paid time-off policies; Career advancement opportunities It’s notable that most of these items are management practices and policies that have no direct additional costs. Recently, we’ve witnessed some high-profile unionizations, including one Amazon warehouse (out of 110 total) and eight Starbucks stores (out of 15,000 total). These unionizations have sent shock waves through retailer headquarters. Many more union drives are currently in progress across the industry. In response, retail leaders are looking back through their aging “union avoidance” playbooks. Many of these are leaked to the public and are somewhat unflattering for their brands. There is a positive side to the story. The desire to unionize indicates that workers want to keep their jobs and do not want to quit outright. Unionization, therefore, is an opportunity for the retailer and their workforce to align on their shared and individual values and desires. Unionization drives are also signals that there may be an issue with the quality of the front-line management, and possibly the broader company culture and policies. The retail industry is built on the idea of scale, where each store or warehouse is running the “same play” with minimal variation. Retailers are not well-equipped for managing an ever-widening range of policies across unionized and non-unionized locations. Unionization presents significant challenges that include logistical and communications activities, negotiations at each unionized site, management of a portfolio of agreements, and the possibility of creating new benefits plans, training programs, and so on. People Analytics to Consider Regarding Unionization: Site Stability Index. Retailers should create a “balanced scorecard” that covers all locations. The scorecard should include external information such as competitor openings, nearby union participation, local unemployment rates, and relevant legislative changes in that jurisdiction. Survey Analytics. Retailers should employ a continuous pulse survey strategy with appropriate sampling and rotating questions. This information can be correlated with turnover, mobility, leadership stability, manager performance, ombuds claims, and a sales or productivity versus plan report. Union / Non-Union Analytics. Retailers can learn from analytics that compare similar stores along union vs. non-union dimensions. Navigating complicated waters like this requires good information. Challenge #5: Making Data-Informed Workforce Decisions Beyond being a bit circular in the context of this paper, this last challenge should be considered very real and concerning. Every retailer with over 10 stores has the volume and velocity of people data in their systems to support data-informed workforce decisions of HR and business leaders. Each store and warehouse leader should have basic workforce data and analytics that are a couple of clicks away. Additionally, those leaders should be expected to use data and insights when making workforce decisions. It is not a minor undertaking to change the decision-making fabric of a large organization. There are multiple levels of the organization involved in this type of change, and naturally the right tools and support are essential. Starting at the top, the C-suite must set the expectation by integrating data into workforce decisions in ways that are visible to their teams. CHROs need to promote and hire HR retail leaders with analytical aptitude and curiosity. They need to drive change into their own function and groom HR business partners who truly shape the business. This is impossible without integrating people data and providing vehicles for sharing it. Organizations that have done this right have built a People Analytics Center of Excellence or similar sub-function within the Human Resources department. But the people analytics COE is not simply a reporting or HR tech team. The people analytics team is dedicated to driving better, more data-informed talent decisions at all levels of the organization through content, products, insights, and advanced analytics. Data is the foundation of any people analytics COE. It is crucial that data from multiple HR and non-HR data sources is integrated to create a high-quality data asset. Maturity in people analytics should be considered in organic, non-linear terms. You should not plan to perfect the data first, then proceed to reporting, then to analytics, as you may see in a Gartner maturity model. Instead, first gather as much data as you need to create the content and analytics that will generate the most decision-making value for the organization. Then repeat the process for additional decision domains. People Analytics Questions to Consider for Workforce Decisions: Which Data, Where, and How? Evaluate your company’s top priorities to determine what data is necessary to improve talent decisions. Locate both the HR and non-HR data required to inform those decisions. Explore how these multiple sources can be integrated. Determine which data sources are easily integrated and which integrations require heroic efforts. The Capabilities Ask how capable your HR function is in leveraging data for talent decisions. Ask if your business leaders are prepared to do the same. The Tools. Determine if a visualization and reporting platform is available to extend people analytics content across the entire organization. Is it flexible enough to accommodate our needs as they evolve? The retail industry is facing a dramatic inflexion point in its ability to make brilliant talent decisions that propel profit growth, reduce risk, and deliver an incredible employee experience in which people thrive. The key challenges of HR in the retail sector cannot be understated. The next era of people analytics for the retail industry is now – and you are here to lead it. We’re looking forward to exploring this with you. Would you like to learn more about people analytics obstacles in retail and how we can solve them? Sign up for a Demo:
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3 min read
Nicholas Garbis
We wrote this paper because we believe that AI/ML has the potential to be a very valuable and powerful technology to support better talent decisions in organizations – and it also has the potential to be mishandled in ways that are unethical and can do harm to individuals and groups of employees. In this paper, we provide some process-thinking substance to the conversation that has too often been dominated by hyperbolic “AI/ML is great!” and “AI/ML will destroy us!” headlines. In the paper, you will find a set of Guiding Principles … And, most importantly, a set of Processes for Ethical ML Stewardship that we believe you should be discussing (immediately) within your organizations. Each of these processes (and sub-processes) is defined in the paper in plain, readable language to enable the widest possible readership. We believe we are at a delicate and critical point in time where AI/ML has been embedded into so many HR technology solutions without sufficient governance amongst the buying organizations. Vendors (like One Model) need to have their AI/ML solutions challenged to provide sufficient transparency into the AI/ML models – model features, performance measures, bias detection, review/refresh commitments, etc. One Model has built our “One AI” machine learning toolset to enable the processes that our customers can use to ensure ethical model design and outputs. To be clear, this paper is not a promotional piece about One Model, but it is absolutely intended to challenge the sellers and buyers of HR technology to get this right. Without the appropriate focus on ethics, AI/ML products and projects could become too risky for organizations and then summarily eliminated along with all the potential value for individuals and organizations. DOWNLOAD PAGE: https://www.onemodel.co/whitepapers/ethics-of-ai-ml-in-hr
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11 min read
Nicholas Garbis
Only humans would bother inventing something as complex as the concept of species. Attempting to organize every living thing around us into distinct buckets has been a massive and never-ending enterprise. We like categories and we love to argue about them! What's the Difference between People Analytics and Reporting? HR Reporting and People Analytics are intertwined concepts which are more valuable when they are clearly articulated for their distinct purpose and value to the organization. Both are necessary for the effective management of the workforce and the HR processes that are aiming to achieve efficiency and employee/manager experience. HR Reporting and People Analytics have been debated as being the same, as being different, as being parent-child and child-parent. So what’s all the fuss, and how can I offer some thinking that helps? Stirring up muddy water to make it become clear seems foolish, but here it goes. Consider the word origins of the two key words: “Report” is based on the word “port” which means to carry something from one place to another. Reports share information. “Analytics” is based on the word “analyze” which means to decompose and recombine something into something that increases understanding. Analytics facilitates insight. Both HR Reporting and People Analytics are built from a foundation of data that is generated by the multitude of systems-processes that exist within every organization. (See image below) Most systems are built to facilitate processes (eg, hiring) and generate data as a valuable by-product (eg, job open and closed dates). Other systems such as survey systems exist solely for the purpose of generating valuable data. What is HR Reporting? HR Reporting can be generalized based on the traits it most commonly displays (accepting that these lines can be blurry, just as some plants can behave in ways that are quite a bit more like animals). HR Reporting typically is ... Designed to provide information (versus insights) Simple in format, often as list or tables, possibly in a multi-tab spreadsheet Data is often from a single system (often, not always) Raw data, occasionally with calculations applied (ie, metrics) Rather fixed in structure and limited in terms of user interactivity Used in monitoring transactional activity (eg, list of currently open job postings) A source of data that is extracted for analysis in another tool (eg, Excel) Could be a part of a larger people analytics project HR Reporting is used by: Process and technology owners (eg, recruiting ops) HR functional leaders (eg, learning or talent management leaders) People leaders (eg, managers of teams) Executives (eg, C-Suite, CHRO, VPs, DEI leaders) HR Reporting is Valuable HR Reporting is an essential point of access to the data within a given system, enabling the owners of the related processes to retrieve data for review and analysis. I can’t think of a system that doesn’t provide at least some access to the underlying data via reporting. Reporting from these systems is typically organized into some set of pre-defined tabular views, each of them providing users with some options to filter the data to specific parts of the organization or process steps that they want to view (eg, course registrations for the Finance department). Metrics, which are calculations based on the raw data, may also appear in reports but will tend to be a summarization of the transactional data (eg, average time-to-fill for each recruiter). Trends of the data, perhaps displayed with different time periods in different columns, are also common and valuable. Human Resources Reporting is distinguished from “analytics” because analytics tends to be aimed more at generating insights rather than sharing information. Let’s look at People Analytics next. What is People Analytics? People Analytics is more complicated to define. To begin with, it can represent: category of deliverables (eg, interactive dashboards), a team within the HR organization (eg, PA COE), a set of activities (ie, consulting & advising), or a combination of all of these. For comparison with HR Reporting, which is a type of deliverable, we will focus on People Analytics as defined as a category of deliverables. Maturity Continuum? No, Sorry. HR Reporting and People Analytics do not belong on a maturity continuum, as they are both vital parts of running an organization well. Sure, if an organization has no People Analytics, you could confidently say they are less mature than another organization that does. You could even say that one organization’s People Analytics deliverables are more advanced (ie, mature) than another organization. The point is, you don’t move from one level (HR Reporting) to another level (People Analytics) -- you need to deliver both and do them well, even if we agree that People Analytics will create more value for the organization. Here’s a chart that may help orient the two: Notice the difference in the objectives of each: People Analytics will be focused on generating insights. In fact, some advanced analytics solutions will have insights directly within the solution, but most often the insights are expected to occur when the user views and interacts with the content in the deliverable. The value of People Analytics is more in the strategic realm, whereas HR Reporting generates more operational value focused on delivering information to keep the business running. Of course, there is some crossover, but generally, reporting helps with operational items such as efficiency, process monitoring and improvement, auditing, quality control, etc. Analytics is aimed at generating insights that will lead to decisions and actions. Analytics content is designed to facilitate valuable insights “at the speed of thought”, and in online settings this is achieved through interactive user experiences, issue highlighting, embedded insights using natural language. Analytics content facilitates hypothesis generation and testing simultaneously and is a learning and discovery vehicle for users. Deliverables: People Analytics Projects, Products, and Services Let’s outline People Analytics deliverables in terms of products, projects, and services. These are all aimed toward generating insights at scale that will drive the best quality, data-informed talent decisions. Systems and technology are not listed here because they are not deliverables, but enabling elements that help generate the deliverables. Products analytics content is most often distributed online via an analytics platform (like One Model), including metrics that may be sourced from multiple systems, and sometimes will have output from AI/ML predictive models. Dashboards / Storyboards -- an interactive collection of metrics with an explicit design goal of generating insights by or for the user. Some of this may be data science (AI/ML) results that are packaged for broader consumption. “Storyboards” are a variation designed, often with a question format, to elicit a story or path of thinking. Embedded Data Science -- AI/ML and modeling results that use HR data and are embedded within other products (eg, time-to-fill prediction that is consumed by recruiters directly within the recruiting system/ATS, or a restaffing projection rate within a project planning solution). HR Reporting -- while this is not ‘analytics’ in our working definition, it’s important to recognize that these deliverables are often part of the People Analytics team’s responsibility, so they are listed here so as to not be forgotten. Projects Deep-dive studies: covering a given topic, possibly testing a hypothesis, usually culminating in a presentation or delivered document which contains data, metrics, visualizations, written insights, and even conclusions and recommended action steps. These may include advanced analytics and data science methods. Experiments and explorations: digging into the data to understand relationships further, test hypotheses, generate mock-up content that may go into productions, etc. Services Evaluation: creating learning opportunities to elevate the analytics skills of partners (eg, HRBPs) in generic terms or specific to the People Analytics platform. Change Management: developing communications and user engagement plans to help drive adoption of tools and methods. Consulting: offering guidance on strategic decisions and programs that may be evaluated in response to insights generated through the PA team’s products and projects. From Data to Deliverables Let’s return to the diagram we shared in the previous section and expand on it a bit. The systems and processes that generate data are foundational to both. Data from the multiple systems is selectively extracted into a data layer where data from multiple systems is integrated. This layer can be a standalone data warehouse (eg, in Azure or Snowflake) or can be part of a solution. Metrics are calculated (eg, headcount, turnover rate) and dimensions are created (eg, organization unit, company tenure) by applying business rules against the data. Dashboards and Storyboards are designed and developed within a visualization layer (eg, Tableau) or within a People Analytics solution. Data Science will be done using data extracted from the warehouse into tools such as R or Python, or within the data science module in a People Analytics solution (only available in One Model at time of this article). Analytics Projects will be combining elements of all the underlying pieces in what will become a presentation (written and/or verbal), usually on a key topic of interest to leadership. Concluding Thoughts As demonstrated above, HR Reporting and People Analytics are intertwined concepts which are more valuable when they are clearly articulated for their distinct purpose and value to the organization. Both are necessary for the effective management of the workforce and the Human Resources processes that are aiming to achieve efficiency and employee/manager experience. You don’t need to have perfect reporting before you begin doing analytics. The two can mature in tandem and are often mutually reinforcing. A robust, integrated, and flexible data foundation is going to provide the greatest value by ensuring the analytics deliverables do not ‘hit a ceiling’ where the next tier of value becomes unachievable without going back to the architecture of the data warehouse. Think “value first.” Obsess about how your team can generate the most value at the fastest pace for your organization, not about the arcane differences between commonly used terms. To learn more about people analytics, download a free copy of the eBook Explore the Power of People Analytics (value $8.99 in paperback on Amazon) I co-authored with Heather Whiteman, PhD. Download My Free Copy
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4 min read
Nicholas Garbis
Our team recently published a whitepaper which explains the "how and why" of our approach to getting data out of Workday. In it we share a lot of challenges and a heap of technical detail regarding our approach. There are also a couple of embedded videos within the paper (unless you print it!). We produced this whitepaper to share the knowledge and experiences we have gained working with our customers, many of whom have Workday as their core HCM. With these customers, we use our proprietary 'connectors' to extract the relevant data through Workday's APIs (adding in data from RaaS reports where needed). But that is just the beginning, because, while the extraction is critical, what comes out of it is essentially 'dull data' that lacks analytical value in its pre-modeled state. We don't stop there. One Model's unique expertise kicks in at this point, converting the volumes of data from Workday (and other HR and non-HR systems) it into what we like to call an "analytics-ready data asset". So, that begs the questions, "What exactly is an 'analytics-ready data asset'?" and "How does One Model create this data asset from Workday data?" So, here's a definition ... DEFINITION of an "Analytics-Ready Data Asset" A structured set of data, purpose built to support a variety of analytics deliverables, including: Metrics that are pre-calculated, can be updated centrally, and have relevant metadata Queries that can range from simple to complex Reports that contain data in table format (rows and columns) with calculations Dashboards and Storyboards that deliver data in compelling visuals that accelerate insights Data science such as predictive modeling, statistical significance testing, forecasts, etc. Integration of data from multiple sources (HR and non-HR) leveraging the effective-dated data structure Data feeds that can be set up to supply specific data to other systems (eg, data lakes) Security model that enables controls over who can see which parts of the organization AND which data fields they will see (some of them at summary, others at employee-level detail) One of the key elements of building such a data asset from Workday is the conversion of the source data into an effective-dated structure which will support views that trend over time (without losing data or creating conflicting data points). This is much more difficult than you'd expect, given that we are conditioned to think of HR data as representative of the employee lifecycle, and many systems of the past were architected with that in mind. This is not a knock on Workday -- not at all -- it's a great HCM solution that has transformed the HR tech industry with it's focus on manager and employee experience. They are not a huge success story on accident! However, delivering a great experience in a transactional HR system does not directly translate into an analytics capability that is powerful enough to support the people analytics needs of companies today (and for the future). To accelerate your people analytics journey, and to ensure you don't run out of runway, you need a solution like One Model to bring your Workday data to life. Download the whitepaper to get the full story. Go to www.onemodel.co/workday ABOUT ONE MODEL One Model’s industry-leading, enterprise-scale people analytics platform is a comprehensive solution for business and HR leaders that integrates data from HR systems with financial and operational data to deliver metrics, storyboard visuals, and predictive analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which One Model simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co
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6 min read
Nicholas Garbis
WATCH THE VIDEO! Conversation with our Chief Product Officer, Tony Ashton, on the topic of insight generation and he shows how One Model’s new insight function works. Insight Generation I believe that a key element of People Analytics should be on insight generation, reducing the time and cognitive load for HR and business leaders to generate insights that lead to actions. Many people analytics teams have made this a priority from a service offering, some of them even including "insights" in the naming of their team. With artificial intelligence, higher quality and faster insight generation can be driven across an organization. An organization with a mature people analytics capability should be judged on the frequency and quality of insight generation away from the center. Why I Stopped Liking Maturity Models Humor me for a moment while I share a very short rant and a confession. I have grown to despise the “maturity curves” that have been circulating through people analytics for over a decade. My confession is that I have not (yet!) been able to come up with a compelling replacement. My main issues? The focus is on data & technology deliverables, not on actions and outcomes. They are vague and imply that you proceed from one stage to the next, when in reality all of them can (and should) be constantly maturing and evolving without any of them ever being “done” or “perfect.” Too many times I have heard (mostly newer) people analytics leaders saying that they need to get their data and basic reporting right before they can consider any analytics. I personally don’t believe that to be true -- things will get easier, faster, and better with your analytics but you do not have to wait to make progress at any of the stages. Action Orientation For example, getting to “predictive” -- being able to foresee what is likely to happen -- is shown in many maturity models. It is easy to imagine, and you may have examples, where very mature predictive analytics deliverables have had little or no impact on the business. In my opinion, true maturity is not about the deliverable, but about the insights generated and the corresponding actions that are taken to drive business outcomes. Going further, getting to “prescriptive” means you have a level of embedded, artificial intelligence that is producing common language actions that should be considered. This would assume the “insight” component is completely handled by the AI which then proceeds into selecting or creating a recommended action. This is still quite aspirational for nearly all organizations, yet it is repeated often. Focus on Designing for Insight Generation at the “Edges” People analytics teams are typically centralized in a COE model, where expertise on workforce data, analytics, dashboard design, data science, insight generation, and data storytelling can be concentrated and developed. The COE is capable of generating insights for the CHRO and HR leadership team, but what about the rest of the organization? What about the HR leaders and managers farther out at the edges of the org chart? The COE needs to design and deliver content to the edges of the organization that enable them to generate insights without needing to directly engage the COE in the process. A storyboard or dashboard needs to be designed with specific intention to shorten the time between a user seeing the content and them having an accurate insight. A good design will increase the likelihood of a “lightbulb" moment. Humans and Machines Turning on “Lightbulbs” Together We need to ensure that the HR leaders and line managers are capable of generating insights from the people analytics deliverables (reports, dashboards, storyboards, etc). This will require some upskilling in data interpretation and data storytelling. With well-designed content, they will generate insights faster and with less effort. Human-generated insights will never be fully replaced. Instead, they will be augmented with machines in the form of AI and machine learning. With the augmentation of AI, the humans will get a boost and together the human-machine combination is a powerful force for insights and then actions. When we have augmentation of AI, we can stop trying to teach everyone statistical regression techniques which they will never use. The central PA team can manage the AI toolset and ensure it is delivering valid interpretations and then focus on enabling insight generation and storytelling by the humans, the HR leaders and line managers. One Model Lights Up Our Customers’ Data Visualizations One Model has just introduced a “lightbulb” feature that is automatically enabled on storyboard tiles that contain metrics that would benefit from forecasting or statistical significance tests. This is not just limited to the content our team creates, it is also automatically scanning the data within storyboards created by our customers. This is far more than basic language attached to a simple regression model. By integrating features of our One AI machine learning module into the user interface we are automatically interpreting the type & structure of the data in the visual and then selecting the appropriate statistical model for determining if there is a meaningful relationship which is described in easy-to-interpret language. Where a forecast is available it is based on an ARIMA model and all the relevant supporting data is just a click away. With this functionality built directly into the user interface, each time you navigate into the data, filtering or drilling into an organization structure, the calculations will automatically reassess the data and generate the interpretations for you. With automated insights generated through AI, One Model accelerates your people analytics journey, moving you from data to insights to actions. About One Model One Model’s industry-leading, enterprise-scale people analytics platform is a comprehensive solution for business and HR leaders that integrates data from HR systems with financial and operational data to deliver metrics, storyboard visuals, and predictive analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which One Model simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co One Model’s new Labor Market Intel product delivers external supply & demand data at an unmatched level of granularity and flexibility. The views in LMI help you to answer the questions you and your leaders need answers to with the added flexibility to create your own customized views. Learn more at www.onemodel.co/LMI
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3 min read
Nicholas Garbis
There are whole books written about Workforce Planning. I read them and enjoy them (maybe even more than I would like to admit). I will include a short list below for your reference. So, what can be added to the body of thought leadership on this topic? My former SWP colleague, Phil Mische and I got together (in person!) to discuss some elements of SWP and decided to create a video hitting on a handful of topics in rapid succession. We decided to call it "Lightning Round Learning." As background, Phil and I worked together on successfully designing and implementing SWP at scale at a global financial services firm. It was an intense experience but was the greatest test of everything that I had wanted SWP to be. Nothing is perfection, especially in year one of a multi-year journey, but it was world-class SWP and we each have an abundance of learnings to share. We listed out several topic ideas and then selected these in real-time, then hit each of them for a few minutes each: Operationalization of SWP Technology (data, tools, models) Granularity of Skills data Change Management Strategic v. Operational workforce planning There is way more depth on each of these elements -- we could easily have filled most of a day unloading our experiences -- and there are many other elements of SWP that we didn't cover. So, sit back and check out the video below. Then do these 2 things: Schedule time to chat with me on SWP, People Analytics, or One Model more generally. https://meetings.hubspot.com/nicholas-garbis Let me know what SWP elements you think we should cover in Part 2! You can message me here or post your comment to LinkedIn here. SCHEDULE TIME TO CHAT! Workforce Planning Book List: Agile Workforce Planning, by Adam Gibson Strategic Workforce Planning, by Ross Sparkman Strategic Workforce Planning: Guidance and Back-Up Plans, by Tracey Smith ... and one day I may write an SWP book to add to this list! .... :)
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3 min read
Nicholas Garbis
As part of a recent People Analytics course from the Future Workplace, Nicholas Garbis joined forces with course leader Heather Whiteman, PhD to co-author an eBook on People Analytics called "Explore the Power of People Analytics: A Guide for Business and HR Leaders". While the book was specifically aimed at a general HR and business leader audience, we quickly found that a number of well-accomplished People Analytics leaders were getting value out of it as well. Whereas some of the HR and business leaders may be entering this content for the first time, the more mature people analytics leaders are always searching for that same introductory content that can help them to increase understanding and adoption of their team's work. We are here to accelerate you people analytics journey. As titled, the aim of the eBook is to "explore" the topic of People Analytics. In terms of a journey, this is a guidebook that highlights various "points of interest" that make the journey interesting and worth pursuing. Download the eBook (.pdf) Explore the Power of People Analytics We hope this eBook sparks ideas for how you can apply people analytics in your organization and makes it more accessible for your teams. We invite you to start building greater capability in this area so you can take advantage of the opportunities people analytics makes possible. Paperback edition is available on Amazon.com. About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co. One Model’s new Labor Market Intel product delivers external supply & demand data at an unmatched level of granularity and flexibility. The views in LMI help you to answer the questions you and your leaders need answers to with the added flexibility to create your own customized views. Learn more at www.onemodel.co/LMI.
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3 min read
Nicholas Garbis
Yes, it's 'Whiteboard Time' again! In this blog post, we are sharing a video recording on the topic of modeling future diversity levels using a basic example explained in a whiteboard learning session. We will also include a simple, downloadable tool in Excel (link below). The video starts with a quick look at a Diversity storyboard from One Model's demo environment where sample data has been set up for sharing design ideas with current and prospective customers. This is aimed at a broad audience of HR leaders and managers who would automatically see just their own areas of responsibility (with ability to filter further). This structure is a 'storyboard' in that it uses clearly stated questions followed by relevant metrics in a set of 'tiles' intentionally designed to shorten the time from question to insight. VIDEO: click below to launch the video. Beneath the video you will see the download link for the basic diversity modeling worksheet. PROJECTION MODEL: click below to download the Excel-based tool that is referenced in the video. It includes some basic instructions as well. Please reach out with any feedback or suggestions for this topic area -- and to let us know of other topics you would like to see us covering in a future session. About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co.
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2 min read
Nicholas Garbis
On July 28, 2020, Nicholas Garbis of One Model and Cary Sparrow of Greenwich.HR delivered a press conference from the floor of the old Minneapolis Grain Exchange to discuss emerging trends in the US labor markets based on daily job posting data in the Covid Job Impacts website. Below is an excerpt of some of the main points, followed by a link to the video of the event. "Next Friday, August 7th, the Bureau of Labor Statistics will release the July jobs report. (Link to latest release here.) While there’s an ongoing debate regarding what will be in the July report, our recent analysis demonstrates that despite the dramatic increases in Covid-19 infections in many states over the month of July, the positive trends in the job market in many industries have continued through today. As a quick summary: The June jobs report was quite positive -- with a gain of 4.8 million jobs driven by big gains in Retail and Hospitality, the unemployment rate dropped 2.2% to 11.1%. Moving into July, the same two industries continued their upward trend -- and they were joined by others: Healthcare, Wholesale, and Construction. Our projection is that next week’s jobs report will be very strong -- much stronger than what you might expect given the gloom and doom in the news. Getting more specific, we project an increase in employment of about 6.0 million, which will drop the unemployment rate to around 9%, possibly below 9%. While we are optimistic for the July numbers, we are very cautious about what will happen in August. There is certainly continued momentum, but a change in state policies could easily reverse the gains." Cary and Nicholas proceeded to highlight various industries' results and then answer questions from the online audience. Be sure to sign up for our One Model blog and follow us on LinkedIn to learn about the next Labor Markets Press Conference!
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10 min read
Nicholas Garbis
SUMMARY: June was great at 4.8 million new jobs. July will be over 6 million. The June 2020 employment report from the Bureau of Labor of Statistics showed an increase of 4.8 million jobs. It was welcomed as good news, but asterisks were quickly added based on the recently surging Covid infection numbers. July, it is feared, will shown softening in the market. Contrary to many, our data indicates that July jobs report will be better than June, likely exceeding 6 million new jobs. Our analysis is based on the positive signs we have been tracking in the Hospitality and Retail industries over the past several weeks through our Covid Job Impacts site, where we show new job postings on a daily basis by industry, state, and job family. Within the site’s commentary we have been commenting on the progress in these two industries specifically over the past several weeks. The BLS highlighted Hospitality and Retail in their June comments. These industries made up ~60% of the new jobs (2.8 million of the 4.8 million) while they make up ~20% of the total employment. Both were severely impacted in the early stages of the pandemic downturn and are now working their way back toward normal staffing activities. We see Hospitality and Retail combining to create over 4 million new jobs in July, as their job listing activity continues to surge. Most other industries are also showing increased hiring activity, so we estimate they will contribute another 2 million jobs. The August jobs numbers are a bit more difficult to estimate at this point. As states pause and reverse their opening plans, market uncertainties will drive job listings downward though to what extent and how quickly. At the early stages of the pandemic, companies’ reaction times were a bit slower. Perhaps now they have built up more rapid reflexes. Job Listings as a Leading Indicator A company’s decision to advertise a job opening is a clear indicator of their business outlook. If they were not confident at some level, they would delay or cancel the decision to hire. This is true for new, growth hiring as well as backfilling of current positions vacated by resignations or illness. The aggregate of these decisions to hire, as seen in the new job listings data on our Covid Job Impacts site, is therefore a clear and leading indicator for the economy overall. The site provides views of new job postings by industry, state, and job family, indexed to the job listing levels of March 1st, providing a high-resolution lens on the impacts of Covid-19 on the labor market and overall economy. Some views of the Covid Job Impacts site are below as reference: Fig. 1: Overall view of new job listings across all US industries. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Fig. 2: Industry view of new job listings across all US industries. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Trends in Hospitality and Retail industries New job listings in Hospitality hit a low in mid-April at around -80% verus March 1st , and have been on a steady path upward since. This is big progress, albeit far below pre-pandemic levels and still around -45% off versus March 1st. In Retail, the drop was also significant in March, regaining some ground in April and May, then demonstrating strength in June. It is the only industry that, even if for just a moment, has crossed into the positive terrain, exceeding the new job postings figures from March 1st on June 21. Fig. 3: Hospitality and Retail industry trends show continuing improvement. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) BLS summary of Hospitality and Retail in June In the June BLS report, Hospitality and Retail combined to create 2.84 million of the 4.8 million increase in employment. This is roughly 60% of the added jobs coming from two industry sectors that comprise about 20% of the workforce (roughly 10% each). From the June BLS report: Estimating the July Figures for Hospitality and Retail Emerging alongside this good news from BLS are escalating concerns regarding the employment impacts of states’ policy responses to recently increasing infection rates. These actions will certainly have a downward pressure on job creation across industries, Hospitality and Retail notwithstanding. However, the trend data for new job listings for Hospitality and Retail indicates that they will further increase employment in the month of July, at least during the period which will be covered in the next BLS report (which for a single week, generally the week including 12th day of the month). To demonstrate this, we are showing the Hospitality and Retail industry job listing trends along with timing windows to support our estimates for the July report. Fig. 4: View of Hospitality and Retail industries and June BLS reporting week. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) The very positive results from this particular week in June (Fig. 4) would not be from new job listings within that specific week, since job listings take some time to fill and for a new hire to begin working (and therefore be captured in the BLS data). While higher paying jobs can require a few months or more to fill, the jobs in Hospitality and Retail that were so significant in the June the report are relatively lower paying, so we would expect that they are requiring only a few weeks to fill. We have added a box to indicate the period of job listing activity that we assume to comprise most of the new jobs in the June report (Fig. 5, Box A). Fig. 5: Hospitality and Retail industries, with Box A indicating period aligned to June BLS data. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Looking at a similar time window of job listing activity that will correspond to the employment levels on July 12th (Fig. 6, Box B) provides two key observations: the job listings that will be related to new jobs in that week have, for the most part, already been created, and many of them filled; and the volume of job listings in the time period that will be reflected in the July report are considerably higher than the levels that drove the very large job numbers in the June report. Fig. 6: Hospitality and Retail industries, with Box B indicating period aligned to July BLS data. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Our estimate of 4 million jobs in Hospitality and Retail in the July BLS report is based on the analysis of the job listing volumes in these two industries, focusing on Box A (June report) versus Box B (July report). We can see far greater volumes in the later period. Where Hospitality gained 2.1 million new jobs in June based on the period in Box A, we estimate that the new figures will be around 3 million. Similarly, where Retail created 740,000 new jobs in June, the increased job listings figures in Box B versus Box A lead us to estimate that this industry will create over 1 million new jobs in the July report. Fig. 7: Hospitality and Retail industries, with average lines inserted in Boxes A and B. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) The next few weeks will be critical to watch As state policies regarding Covid-19 are adjusted over the next couple of weeks, we will be closely watching the changes in businesses’ hiring plans as seen through their job listing activities. A closer look at state-by-state results on the Covid Job Impacts site will provide a leading indicator and a way to gauge the August BLS report well before it arrives. About One Model, Inc. One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co
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Nicholas Garbis
Two weeks ago, we released a new publicly available Covid Job Impact website to share daily data and insights on US job listings by industry, job family/role, and region/state. As we are looking at this data each day, we have watched some hopeful signs of recovery slipping away over the past week. This observation arrives on the same day that news reports are calling out reductions in the unemployment rate (see Forbes, "The Headlines are Dead Wrong - Unemployment Dropped 16% to 21M"). The total number of people on unemployment insurance has fallen from 24.9M to 21.1M in the past week according to the US Dept. of Labor reporting. A chart of this data is include in our site. So it's true, but it's in the rear view mirror and we want to look through the windshield. For quick reference, our site tracks US job listings with all figures being indexed to March 1st (ie, March 1 = 0%). Since job listings are decisions by businesses to hire, we believe this is reflective of their prospective outlook. And since job listings precede actual hiring by ~3 months, we see this data as a leading indicator of unemployment. Slipping Away? While we saw job listings up through mid-March, they quickly tumbled by ~50%, then proceed to bounce around a bit until they seemed to be holding steady in mid-May at around -27%. But over the past week (the last several dots) things have slipped down below -43% (see image). Together, Apart, and Together Again Another trend we spotted early on was that all industries fell together in mid-March, then began to diverge as different industries returned to hiring at different rates. While all industries were still down below March 1 levels, the range was wide, with some around -20% and others closer to -50%. In the most recent week, however, we see them all bending down together again. We think this is signaling a negative business outlook across multiple industries. (See image below with the periods marked out.) School's Out for Summer? In the midst of this, we can also see that the Hospitality industry, which fell fastest and stayed down the longest, has started to creep back up a bit. It appears that the Hospitality industry is expressing some confidence, perhaps betting that families will begin travelling now that stay-at-home orders are being lifted and the school year is wrapping up. The exception, viewable on the site, is the state of Nevada which continues to show Hospitality job listings down around -95% compared to March 1st. So, Will Unemployment Continue to Improve? As we see unemployment insurance claims dropping a bit, we believe a good portion is people returning to work from temporary unemployment (furloughs, temporary layoffs). These 'hires' will not require job listings. (Some of this was discussed with our guest, former Chief Economist of GE and Deutsche Bank, Marco Annunziata on our recent webinar.) We believe the reduction in job listings is indicative of a broad-based decrease in the business outlook and that unemployment may drop a bit more as temporary layoffs/furloughs end, but will remain stubbornly high for at least the next couple of months. If job postings begin to bend upward again toward -25%, there would be good reason to anticipate some material decreases in the unemployment numbers. One thing for certain: we will be watching this play out daily on the site. Keep an eye out for additional insights on the Covid Job Impacts site: https://covidjobimpacts.greenwich.hr -ng About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co or email us at info@onemodel.co
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7 min read
Nicholas Garbis
Our objective in this series is to offer our team’s expertise to the public during this crisis in the same way that Walmart delivers trailers of water to natural disasters. The “water” we have to share is our expertise in workforce strategy, HR processes, data orchestration, and people analytics. This is a follow-up to our first COVID-related blog post People Analytics for measuring the impact of Coronavirus (COVID-19), in which we laid out a set of critical “Level 1” questions that organizations should be able to answer during the onset of this pandemic. Later in this blog, you will find information regarding a very quick-and-dirty tool we have developed. Before we get there, however, let’s step back for a moment to assess the broader topography of today’s situation. We are in a massive global pandemic that has already demonstrated its exponential potential. Organizations will have a variety of people analytics to help them make data-driven decisions regarding their workforce and their overall operations. We have not experienced anything quite like this, so we should expect it will require great empathy and creativity, a willingness to lead, and the agility to test, fail, and learn. If we consider breaking this out into phases that organizations may pass through over the next several months, we can start to anticipate the shifting needs for people analytics. These phases will not come with clear markers, so the only way to know where you are is to step back at regular intervals to reevaluate the situation and reallocate your efforts. We have developed a quick tool for the current phase. The tool we assembled is focused around the first phase of the crisis -- where we are right now. It is built to answer some basic questions, acknowledging the unfortunate reality that our HR systems are unlikely to possess the information we need. As a result, a process will be needed to capture and consolidate the needed information. In these situations, it’s critical to seek out only the most critical data elements (ie, “KISS”). A simple dataset, updated daily, has the potential to provide business and HR leaders with the “situational awareness” they need to make decisions quickly based on facts. The “COVID-19 Workforce Tracking” tool that we developed is a free Excel-based tool that can be used by organizations of any size, including those with limited people analytics resources. It aims at answering most of the questions laid out in our previous blog. One example is a mid-size medical device firm without a people analytics team. They are already getting started with the tool, standing up a daily update process with ~4 representatives from various parts of the business making updates into a shared worksheet. One person then ensures the dashboard is validated and then it goes to the CHRO for review with business leaders. It’s worth restating that our objective here is to get something out to the maximum number of people in the shortest amount of time. Hence, this quick solution in Excel. Our team obviously has an ability to stand up these metrics and a series of new ones in our One Model platform where we have robust data handling and visualization, but moving data to our clouds will require information security reviews by most organizations which takes time. (We have opted to deliver the “water” now and come back with sandwiches real soon.) The first iteration of this tool (v1.0) can be accessed at the bottom of this blog. You will be asked for your email so we can communicate when any new versions are released. Here’s a view of the dashboard: And here is a view of the dataset. It is a series of HRIS data fields (not all shown here) combined with a collection of 8 data fields (in yellow) which should be updated daily: Below are a few notes on the tool: Manage sensitive data according to the applicable laws and your company policies. This is built in Excel so it can be used quickly by the widest range of users. The data collection template is intentionally simple to ensure it can be sustained as a daily process, ideally by a single point of contact. A slim set of ~8 questions, combined with some basic employee data will enable you to answers to nearly all of the critical questions. The basic employee data will be taken as a baseline/snapshot. Terminations will be tagged (not deleted) and hires would be added to the list. The dashboard will provide a set of key metrics with ability to filter the results several ways. You and your teams can and should modify it to meet your needs. Future enhancements being considered (your input on this is welcome!) Macro to import the real-time statistics from the global COVID-19 tracking data as managed by Johns Hopkins University. Access to cloud-based solution within One Model technology to enable benchmarking across companies, reduce version control issues, and enable advanced modeling and forecasting through our One AI machine learning/AI platform. Below is the link to the latest version. As updates are made, the latest version will always be posted within this blog. Current Version: 1.0 To download the tool, please click the link below. About One Model One Model delivers our customers with a people analytics infrastructure that provides all the tools necessary to directly connect to source technologies and deliver the reporting, analysis, and ultimately prediction of the workforce and it's behaviors. Use our leading out-of-the-box integrations, metrics, analytics, dashboards, and domain expert content, or create your own as you need to including the ability to use your own tooling like Tableau, Power BI, R, Python as you need. We provide a full platform for building, sustaining, and maturing a people analytics function delivering more structure information, measurement, and accountability from your team. Learn more at onemodel.co.
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3 min read
Nicholas Garbis
Here at One Model, we have some great news regarding our team! Nicholas Garbis has joined us as VP of People Analytics Strategy, bringing over a decade of significant global experience in people analytics and strategic workforce planning roles at firms including Allianz, General Electric, and Target. We like to let our people speak for themselves, so we'll turn it over to Nicholas now ... Why I am joining One Model? As a leader of People Analytics and Strategic Workforce Planning teams over the past ~15 years I have seen (and been part of) the transition of this work from fringe concept to a core requirement of the human resources function. The early leaders of teams like mine are now entering their second and third roles in different firms, leaving their imprint on their former organizations. At the same time, there are now many CHROs that have the knowledge and appetite for analytics and planning, and they are also moving between firms and building new teams. The days of conferences where presentations covered theories and frameworks with minor variations are in the past. We are definitely in people analytics/planning 2.0 now -- and it’s great to be a part of it. “The future is already here -- it’s just not very evenly distributed.” -- William Gibson. Having moved between a few large firms, and consulted into several more, I can assure you that Gibson was right. Going from one company to another might look like going back in time technologically and/or strategically, or it might be like achieving warp speed. As a strategist and practitioner, one needs to assess the situation as it stands and chart a course that has the best chance for creating value for the organization. Then it requires a constant balancing of priorities of team size and skills, new talent processes, and securing the needed technology. It’s not easy work, and the choices are never simple, but dare I say, it’s “fun”? Maybe “fulfilling” is a better word. I am joining One Model because I believe that technology is key to unlocking the next wave of value in people analytics and planning. I believe I can draw upon my own experiences and synthesize the experiences of others to help shape this 2.0 world. I have seen where the technology constraints can slow things down and create immense manual work, and I have seen where technology creates speed and scale. (Trust me, the latter situation is preferred!) Data is the strategic asset of the future, and this includes human capital data! As organizations continue to set up new people analytics teams, others will continue to progress into more advanced analytics and data science. Capturing the fullest value possible from human capital data will require technology that meets organizations where they are: for some it will be foundational data, metrics and dashboards, and for others it will be a robust data analytics platform with AI and machine learning already built in. One Model does both and will continue to lead the way. This is why I am joining the One Model team. 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.
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