EMPLOYEE ENGAGEMENT & EMPLOYEE EXPERIENCE
Designing employee-centred workplace experience
In today’s world, people spend on average 40.7 hours a week at work. This is a significant part of a week and of our time. Sometimes, it extends beyond this amount of hours. The working day is all about our daily agenda concerning work duties and private matters alike: going for lunch, securing a place for a meeting with a client, transporting yourself to a meeting with a client, taking clothes to the laundry service — all the activities take up considerable time.
We were approached by client to find a solution and enhance their current application. The primary aim of the app is to create a more efficient environment, give access to all exclusive services available in a certain project. We looked for a solution that would achieve these objectives:
- improvement of functionality and usability,
- showcasing efficiently available services so that the user fully understands what and how he can use,
- creating a workplace experience that will make the user´s life simpler and more pleasurable.
We did not want to devise an application based on its previous version or merely based on our client’s requirements. We recognized an opportunity to design a product that the target users will want to use. Therefore, the end user was at the centre of our interest, and we aimed to connect with as many people using the client´s properties as possible. We had to, essentially, dive into their daily routine. Thus:
- several workshops with stakeholders were organized
- in-depth interviews were conducted
- these tools also helped us to understand the client’s business challenge
Collectively, we identified risks, set expectations and developed a common vision of the application. Following this approach, we created the experience strategy for each phase and the direction of the design process as a whole.
PEOPLE, HR & WORKFORCE ANALYTICS
Advanced Analytics for Agile Organization Design: 5 Principles
Organizations are more than a list of people. Agile Organization Design is much more than redrawing the org chart. Analytics are central to understanding the organization and designing its future. But organizations often end up with the wrong data and ineffective analysis in preparation for organization design. As a consequence they pay the costs of getting their design wrong. In this article, I propose a number of solutions, including: New data models, better analytics, non-defined space and feedback loops are needed for organizations to get organization design right.
Any large organization faces the need to change from time to time. Efficient routines are the heart of a good business, but when the outside world changes, organizations have to adapt. Customers demand new products, service offerings and activities.
The business has to deliver changes to routines, skills and people. Sometimes its people leap at the chance to take on new challenges and learn new skills. Other times, the organization must let people go & rehire for other skills.
The complication: Agile Organizational Design
Designing a future organization is challenging, for five good reasons:
- Imperfect information: You don’t know what you’ve got
- Complexity: The future organization is a complex system
- Bundling: People are a bundle of Skills; Roles are a bundle of Activities plus Behaviors
- Immeasurability: Costs are known; potential benefits are hard to estimate
- Change: Even the right answers are only temporary
What are these challenges, their consequences, and how should you address them? We will discuss them one by one.
1) Imperfect Information: You don’t know what you’ve got
First, how can this be? The organization’s managers and auditors are meant to control how physical and intangible resources are allocated, measured and tracked. Yet the reality is that most organizations have only an approximate idea of their resources.
Errors creep in from imperfect record keeping, and also from continuous evolution. As new demands emerge, new roles or departments serve them and the old definitions of cost, headcount or FTE by department are no longer accurate. We were not surprised when one major financial institution said its operational headcount was ‘between 60,000 and 85,000’ – because the definitions used in different parts of the business were continuously shifting. At the same time, the actual work that is required can change – and processes can evolve without a formal record being kept.
What’s needed here? Every organizational design project has to decide what data to extract from multiple existing systems, and what extra data to collect about its people. A pragmatic approach is recommended here to data collection: get the basics right, and only seek further information if it really adds value. By the basics, I mean, an accurate list of current people and an accurate list of future roles. Too many organization design projects are undertaken without even this basic data present.
2) Complexity: the future organization is a complex system
Every organization is a complex system. It adapts to its environment with multiple elements, multiple interconnections and feedback loops. The elements include:
- People – who is employed, for how long, at what pay rate, past performance
- Roles – job descriptions, grades, spans of control, responsibilities
- Activities – work definitions, level of detail, strategic importance, risk levels attached
- Skills – formal qualifications, technical, social, personality types
- Objectives – clusters of objectives, priority order
- Systems – especially IT systems
When you change an organization, we sometimes say, you intervene in one dimension, but have an effect on many. Removing a ‘layer of management’ seems attractive, but too often, people then have to be re-hired because their particular skills, or particular processes were not transferred. Or ‘redesigning our customer journey’ seems like a great change to make – as long as the implications for systems, roles, people and support functions are well worked out.
Systems thinking implies the analytics needed for organization design sometimes take place on one dimension (e.g. roles and their costs), sometimes on another (e.g. activities or skills) and sometimes on the interrelations between them – the fragmentation of work across roles, or the clusters of skills needed for activities. Sometimes not knowing how to analyze these complex relationships causes trouble.
In a voluntary redundancy situation, people suspect trouble ahead, but they usually have no method for calculating the service-level impact from the combination of skills of the people who choose to accept redundancy packages.
What’s needed here? A choice from the organisation as to whether it’s ok to design the future based on headline ‘roles’ only, or whether specific activities, systems and skills need to be thought about. You wouldn’t design a building without water pipes, or knock out a wall or two without considering the consequences. If activities, systems and skills are important, model them.
3) Bundling: People are a bundle of skills; roles are activities plus behaviours
People are the saving grace of modern society. The bodily engrained habits of activity, movement, location, and behaviour lend a layer of stability that would not be provided by rational economic entities.
As entities in organizations, people are not, thank goodness, fully divisible or interchangeable. You cannot swap in or out a pure ‘skill.’
The fact that multiple skills are bundled up in human beings has its counterpart in the bundled idea of a role. A role is also a most peculiar combination. It is more than a task; it is more than a set of tasks or even deliverables. Its very name is theatrical: to take on a role is to play a part. That’s why the role is a bundle of activities and behaviors. Behaviors are descriptions of the style of action, and are used when the precise nature of the action cannot be defined.
Why is this important? Understanding roles as ‘activities plus behaviors’ is important because it indicates that precise design of future work will not be possible. We need ‘behaviors’ precisely because the future work we would want cannot be defined now. We want people to fulfil a role – and that might require improvisation. A little or a lot, we can’t be sure. But it’s a role we want, not just activities and deliverables. We know that we don’t know. As Nordtsrom’s employee handbook used to say ‘Rule #1: Use best judgement in all situations. There will be no additional rules.’
What’s needed here?
An analytical approach can be applied at the stage of delivering a new organization design. At this stage, with well-defined roles, you can use recent developments in personality data collection to propose people for positions.
Historically, personality data has been slow to collect and unreliable to use. But now accelerated surveys are offered by companies like Ixly and Cut-e, and game-based profiling are offered by companies like Arctic Shores and Pymetrics. These generate a ‘fit profile’ of applicant, and claim to improve predictions of the suitability of each candidate to each role. This is especially appropriate for jobs with ‘discretion’ over delivery, where you might recruit for personality, and train for skills: such as service roles, sales roles and developers.
A less analytical response may be more appropriate when roles are still to be defined, or are still evolving. In this situation, it makes sense to talk again and again about the culture of the organization and the behaviors that bring it to life. Question it. Burnish it. Make it your aim that people should talk proudly in their retirement about the magic of having worked together in your organization, and what they achieved for each other, their customers and their community. And – as no human organization lasts forever – be prepared for it to have its day and to hand over to the next organization, as long as its values are even better.
4) Immeasurability: Costs are known while potential benefits are hard to estimate
It is a lot easier to calculate recent costs than future potential benefits. This gives a dangerous inherent bias in organization re-design work in favor of cost reduction. Spans of control and layer reduction are classic design interventions which risk being over-used because they are easy to measure. There is still value in benchmarking and comparisons of organization’s structures, but the cost optimization story has its limitations: no organization started from nothing and then cut costs…
Certainly, you should use the data – there will be all kinds of nuggets hidden in the information. A great principle is ‘catching people doing things right.’ One of our clients identified teams half the size on one site of those on another but producing the same volumes of output, using better working methods.
At another client, high accident rates were correlated both with individuals working too many hours – and too few hours. Best practices can quickly be shared once identified. So analytics are vital, but it is also vital that data-led interventions are balanced with the need to create an incompletely-defined creative space.
We are all aware that the future is continuously being created – sometimes by an individual, but more often, by people working in groups. Michael Lewis’s book The Undoing Project describes beautifully how creative interaction drew more from two people than they ever knew they were capable of themselves. People working in groups define new products, new services, new concepts and new ways of working. By doing this they create new value, and that value itself cannot be fully known in advance, even by the people themselves.
The lack of control of one’s own creativity is of course, why it is vital for artists to have agents.
What’s needed here? For organization design, it is important to lay the foundations with core analytics, benchmarking and cost reduction, especially in well established, standardized processes.
On top of this, organizations must give people permission to explore, with processes and behaviors that protect them and believe in their ability to grow. The organization has to take the best view on benefits that are not yet known, while continuing to collect information and to adapt quickly to any signals received. It must liberate the entrepreneurial animal spirits to act, and manage the fine art of being wrong in an Agile fashion.
5) Change: Even the right answers are only temporary
First, even in a defined strategic landscape, other players may yet have to decide their plans. For example, European retailers now look at Walmart and Amazon and try to work out what is coming. What is coming will depend partly on what they expect. This is a recursive loop of feedback that cannot easily be modelled.
Second, value is determined interactively. Some goods’ value depends on them being relative goods. Some depend on whether they are perceived as being valuable. Larger and larger parts of the economy (arts, brands, luxury goods, consumer goods, service experiences) are created in this uncertain way. As Keynes said, in business life, the necessity for action compels us to behave ‘as if we had a good calculation of advantages and disadvantages,’ but the reality is often that “We simply do not know.”
Valuations are temporary, reflexive and may be unpredictable. There have been times when 20 bitcoins were needed to buy one Gucci handbag, and when one bitcoin could buy 20 Gucci handbags. It may not be meaningful to ask what is the ‘correct’ value for either.
What’s needed here?
Because right answers are only temporary, the organization has to get the customer’s best current view of the opportunity, and – in the Agile model – adapt the organization’s activities and roles continually (e.g. every two weeks). The great entrepreneurs are not the ones who bet on the ‘right’ vision, but those who build an organizational willingness to invest, monitor, identify issues fast, and adapt.
The last two points – immeasurable potential plus continuous change – naturally fit together. If you can create, but not know the value in advance, your org design needs an innovation selection mechanism to ‘support or shelve’ (but learn from!).
The 5 Principles of Analytics for Agile Organization Design
So what kinds of analytics do you need for organization design? Taking the five challenges and analytical responses together, let’s try to gather some recommendations that can be included in your design strategy.
|Org. Design Challenge||Analytical Response|
You don’t know what you’ve got
|At minimum: an accurate list of people, and an accurate list of future roles; a decision on any extra data needed|
The future organization is a complex system
|Decide explicitly if activities, systems, and skills are important. If they are, model them|
|3||Bundling: People are a bundle of skills; roles are activities plus behaviors||Talk frequently about the culture of the organization and the behaviors that bring it to life|
Costs are known; benefits are estimated
|Do the simple analytics well. Believe in your organization’s ability to grow – giving a process and a space|
Even the right answers are only temporary
|Use Agile design, accepting you don’t know what is possible; but seek signals to respond to what comes out|
The analytics we need for great organization design are therefore data-led, multidimensional and adaptive.
You need (1) hard analytics of what is well understood and definable. (2) The analytics must cover many dimensions while understanding that (3) some aspects are undefined – which is why employees must keep talking about the vision and culture. (4) Your analytics must calculate some investments in time and space for the unplanned to emerge, while (5) analyzing the real time signals of which ones to back.
People sometimes say of a job definition ‘it’s not the name on the box, it’s what’s in the box that counts.’ I find this too ambitious (do you really imagine that you can define what’s in the box?) and too restrictive (are your definitions all that could be in there?). Yes, we should analyze everything we possibly can. At the same time, however, Agile Organization Design strategy has to be humbler in its analytics and more ambitious in its culture. It’s not just what’s in the box, it’s also what could be. How do we give it its chance?
And it comes from saying no to 1,000 things to make sure we don’t get on the wrong track or try to do too much. We’re always thinking about new markets we could enter, but it’s only by saying no that you can concentrate on the things that are really important.” Steve Jobs interview Business Week, 2004
 As Steve Jobs said: “Innovation comes from people meeting up in the hallways or calling each other at 10.30 at night with a new idea, or because they realised something that shoots holes in how we’ve been thinking about a problem. It’s ad hoc meetings of six people called by someone who thinks he has figured out the coolest new thing ever and who wants to know what other people think of his idea.
PEOPLE, HR & WORKFORCE ANALYTICS
3 Complexities of People Analytics and How to Create Order Through an Ecosystem
If you are looking for that silver bullet, that unicorn of a solution to understand and optimize people at work—stop looking. People are complicated, organizations are complicated, and work is complicated. So is the people analytics market that exists to help you make sense of it all. Here’s an approach to manage that complexity so you can turn the promise of people analytics into actual insights for running your business.
People analytics is a hot topic, with 84 percent of surveyed executives seeing it as a key business priority.1 According to BersinTM, Deloitte Consulting LLP’s study of people analytics, organizations with the best people analytics functions see 96 percent higher revenue over a three-year period, compared to their less effective peers.2 Getting to this state requires various different factors, including data governance, analytical capabilities, a data-savvy workforce, scalable delivery of insights, alignment with the business, and an overall data culture. But on top of all those factors, organizations need the right tools and technologies to make it all happen.
Not surprisingly, the people analytics technology market is equally hot. Go to any HR conference, and you’ll see a dazzling array of shiny tools all promising to use artificial intelligence (AI), cognitive tools, machine learning and other next-generation capabilities. Whether it’s organizational network analysis (ONA), natural language processing (NLP), robotic process automation (RPA) or some other three letter acronym (TLA), it can be confusing to navigate this market and determine where to focus.
Complexity 1: People data come in many types.
It would be easy to describe people through simple demographic and employment data – but it’s not true. People are not one-dimensional. They have lives outside of work, relationships with other organizations, interests and passions, capabilities and skills. Their health and well-being are intrinsically related to their performance and productivity. High-performing organizations acknowledge this and create a more holistic picture of the entire person, expanding the types of data beyond job and demographic information to include data on health and wellness, external employee information, even geospatial insights. For example, geospatial data can be used to understand skills available in different locations, determine commute patterns for answering questions on stress and engagement, or understand how people use different space options to optimize office configurations. This multifaceted approach is necessary to create a more holistic picture of people.
Complexity 2: People data reside in many places.
Unconventional types of data reside in many different places. High-performing organizations use an average of seven different data sources (compared to just three for their low-performing peers).3 Beyond traditional employee surveys and HR systems, they mine data from emails or meetings, publicly available sources, posts on internal and external social media sites, and even unexpected places like performance goals or HR support tickets. For example, analytics on performance goals can create alerts for goal overlap, pinpoint voids that need focus, or suggest collaboration of people with similar goals. This allows organizations to “listen” more effectively to people’s input about their experience and productivity, and address issues more effectively
Complexity 3: People analytics solutions are available in many (overlapping) categories.
People analytics solutions are everywhere today – and not just in vendor exhibition halls. The capabilities that power people analytics applications—like identifying high-potential employees or addressing retention trends—boil down to analytical tasks such as data gathering, reporting, analyzing, and suggesting actions. Most organizations already employ solutions that perform these tasks…think of spreadsheet software, statistical analysis tools, and data visualization solutions. In addition, new “pure-play” people analytics solutions offer specialized capabilities like organizational network analysis and natural language processing to generate insights from previously untapped data types and sources. And even as new solutions are available, so too are similar capabilities increasingly embedded in other vendor offerings. Talent acquisition suites, for example, offer predictive analyses of which individuals are best-fit candidates or likely to take a new position, and employee engagement solutions help identify factors that indicate employee attrition and propose actions to managers to counter that.
Cutting through the complexity with an ecosystem approach
As we said, there’s no one silver bullet that can solve all your people analytics needs. Mature organizations manage an ecosystem of tools to generate rich insights and complex actions for business impact, pulling together the right tools for each problem (see figure 1). These tools have unique capabilities and strengths—from reporting to analysis, from visualization to action-oriented nudges, and from measuring activity to analysis impact.
Figure 1: Use of people analytics technologies
Source: Bersin™, Deloitte Consulting LLP 2019
Focusing investments to build capability and capacity
Many mature organizations also invest much more heavily in people analytics than their less mature counterparts, and the amount of investment in people analytics directly correlates to better business and workforce outcomes.4
But not only mature organizations are investing. Over 70 percent of organizations surveyed invested in building people analytics capabilities in the last year. One in three organizations built or improved a data warehouse—not surprising, as the vast majority of organizations are at the stage of building a solid people analytics foundation and getting to a “single source of truth.”5 Tools for aggregating data are at the tail end of the spectrum (see figure 2).
Figure 2: Investment in people analytics technologies
Source: Bersin™, Deloitte Consulting LLP 2019
Which specific tools an organization needs depends on various factors, including unique business challenges, existing technology infrastructure, and where they are in their analytics journey. For example, high-performing organizations are almost three times as likely to invest in data aggregation tools than low-performing organizations6—because they have sufficiently built up the infrastructure to have the right data to aggregate.
To separate hype from reality, organizations need a guide to navigate the people analytics landscape. At Bersin, we explore people analytics capabilities to create one such guide. Our research helps organizations understand which capabilities supplement existing tools and how analytics solutions can help them understand and optimize the people side of business.
PEOPLE, HR & WORKFORCE ANALYTICS
People Analytics and learning: Driving workforce development by delivering the right solution to the right people at the right time
12,762 likes, 3 million views, 100,000 clicks—all these measures provide some perspective into the reach of a marketing campaign. But the most valuable insight comes from deeper analysis—one that illustrates the connection between eyes on the content and dollars in the register, the true measure of effectiveness for a given marketing campaign. In the same way, the game-changing insights about learning come not just from identifying the before-and-after change in a given behavior, or observations between a test and control group, but also from combining learning data, business data, and behavioral data and conducting robust statistical analyses to personalize learning recommendations and career development interventions. Calibrated through multiple data points, these precise solutions drive business outcomes by delivering the right intervention to the right person at the right time.
The context for learning analytics
The desire for a more accurate and reliable measure of learning’s effect on business outcomes is not new, but access to the data points to create it is. And now it’s more than just desire—we live in an evolving reality that makes measurement an imperative. The rapidly changing nature of work is creating a nearly continuous need for upskilling the workforce. With the half-life of a learned skill at 5 years,1 the hunger for learning and development programs is ravenous.
Furthermore, as technology and learning platforms make information more accessible, people are now learning within the flow of work.2 While classrooms and instructor-led training may not go away entirely, the reality is that the majority of learning is happening informally—from an employee’s simple search for a how-to video to address a task in the moment to reading an article of interest posted by a former colleague.
But all learning solutions do not provide equal value, and as the level of investment required grows, so does the need to prune and focus on the most effective learning programs.
From creation to curation
The mission-critical need to upskill3 and the variety of content sources has sparked a move from content creation to experience curation4 further enabled by the learning experience platform.5 This growing need for upskilling,6 the expansion of learning beyond the classroom, and the proliferation of data sets are the building blocks of a new era for the learning function: an era in which analytics powers the learning function to operate as a driver of the business, providing valuable insight and guidance on how to develop the workforce to optimize business outcomes.
Measuring the effectiveness of solutions in an increasingly social learning model in which content is curated, not created, demands broadening metrics and indicators to include not only learning data, but business data and user behavior data, too. As learning moves in the direction of expanded user choice, like that found in on-demand TV, organizations need to update their approach and design analytics tools to capture the learning in these new platforms. This is the cue for real-time learning analytics and insights.
With learning analytics, the organization gains timely information and feedback on the efficacy and impact of various learning investments on individual development, organizational learning trends, and business outcomes. The mature learning function can use these analytics insights to drive decision-making about which programs, tools, and resources to continue, to expand, to terminate, and to initiate.
A sustainable analytics strategy starts with an effective data strategy
Evaluating the ROI of a learning solution by its impact on business outcomes requires robust statistical analysis. Even before the analysis can be complete, it demands the combination of independent data sets—data that live in different functions (and possibly even storage platforms) inside and outside the business. Effective aggregation depends on a clear data governance structure, standardized data sets, and a data normalization process. Once combined, disparate business, learning, and user behavior data can enable new insights about the impact that a particular learning resource is having on the business.
For example, imagine if you could see the change in investments made by your financial advisers after participating in a financial strategy lab; or if you could measure the increase in “followership” of your senior directors after attending a leadership seminar. To take it a step further, imagine knowing which links are most shared, which articles garner the most careful read, and who people actually go to when looking for new information. We already know the importance of lifelong learning on leadership,7 but what if you could identify your star performers based on learning habits, or preemptively upskill in the areas where learners are self-directing their focus? These insights are available. The data exist. They just need to be brought together and analyzed.
Aggregating and analyzing siloed data is not easy; it requires an intentional and well-developed data strategy. An effective data strategy should include five components:
- Alignment on the goals between stakeholders
- A hypothesis of which data is necessary for meeting those goals
- A plan for how to integrate the data
- Clear expectations for all stakeholders
- A built-in review process to evaluate progress and adjust as necessary
Learning functions that develop this analytics acumen are equipped with foresight—a capability that helps them see what information is most in demand by employees, which learning experience is most effective at changing target behaviors, which learning solution has the strongest correlation with performance, and in turn, which learning intervention is most valuable to their company.
While stitching together data can be difficult, it is a necessary prerequisite for gleaning insights into the impact of learning on the business. The beauty of an effective data strategy is that it lays the foundation for an L&D function to gain insights from the top down and from the bottom up. When the data is actively managed and integrated, Learning functions can ask: What impact did a learning solution or program of learning solutions have on the individual and business performance?
Simultaneously, an effective data strategy with real-time integration enables the business to realize trends among top performers and backwards engineer the common variable, potentially tracing it to a specific learning resource. For instance, imagine searching for a common learning experience among all of your most effective salespeople—perhaps, they all follow the same SME on your learning experience platform, read the same book, or completed the same online course! Stitching together data sets across different parts of the business and understanding the impact of specific variables requires a strong data strategy, statistical expertise, and a commitment to robust analysis.
A case in point
One public sector agency has already been able to optimize learning investments based on this type of learning analytics. Taking a broad-based data strategy, this client analyzed the relationship between L&D investments and KPI performance, quantifying the value of the investments made in their employees’ learning experiences. They examined operational efficiency metrics, regulatory metrics, and business KPIs to evaluate the effectiveness of the learning investment and its impact on organization performance. These investment analyses were provided in a digital, interactive dashboard providing a single point of access for agency leadership to continue to track the ROI of their L&D investments. It is the link to business impact that allows the Learning function to make data-driven business decisions to precisely deliver the most effective learning solution to the right people in the right way at the right time.
Making your spending count for more
The process of becoming a Learning function that leverages analytics to drive the business forward begins with understanding and anticipating the business’s needs and aligning the learning strategy to optimize learning investments. What is important for one organization to address may not be relevant to another, but the opportunity is the same. L&D functions that aggregate the right data sets and conduct the relevant analysis to yield valuable insights are drivers of the business, rather than simply providers of programs.