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Analytics translators perform some of the most essential functions for integrating analytics capabilities in a company. They define business problems that analytics can help solve, guide technical teams in the creation of analytics-driven solutions to these problems, and embed solutions into business operations.

It’s specialized work, calling for strong business acumen, some technical knowledge, and project management and delivery chops.

Deploying translators is especially important during a company’s early efforts to use analytics, when much of its analytics know-how resides in a small cohort of data leaders and practitioners. We’ve seen companies hatch ambitious plans to apply analytics in dozens of situations—only to pull back because they employ too few people who can deliver solutions.

That gap should shrink in the long term, as analytics pervades business and analytics training becomes a standard part of employee development. But in the face of competitive pressure, companies cannot wait to work with analytics on a large scale. Translators can help businesses climb the analytics learning curve quickly and roll out more use cases than they might otherwise.

While translators can acquire some of the requisite knowledge for the job through coursework, they make the most impact once they have developed practical skills through on-the-job experience.

Yet it is all too common for executives to assume that employees can act as effective translators, capable of delivering analytics solutions, once they complete a class on the rudiments of modeling. In fact, employees who only receive classroom training are more like teenagers who sit through a driver’s-education course, then walk outside and try to drive away—with no behind-the-wheel training, supervised practice, or road sense.

Translators can only master their trade by observing seasoned colleagues at work and then working on actual problems with expert guidance. This progressive, real-world learning approach prepares translators to manage diverse teams of specialists, create replicable workflows, and apply business judgment while assessing trade-offs.

None of these steps can be skipped if a company hopes to apply analytics widely and generate significant value.

Recruiting translators and positioning them for impact

Before launching a translator-training effort, executives should map out a company’s analytics strategy and priorities. Then they can determine how many translators are needed in each part of the business—and target recruiting and training programs accordingly.

Translators typically sit within business units, in proximity to day-to-day operations in stores, plants, mines, call centers, and other sites where employees make products or deal with customers. These vantage points let them spot uses for analytics and ensure that analytics solutions are embedded into the business for impact.

Ideally, translators will have spent time working in business operations before starting translator training. Existing business staff often make better translators than new hires because they have an important quality that is hard to teach: knowledge of a business domain where analytics will be applied. To put this another way, business operations are the typical translator’s “mother tongue.”

In addition to business acumen, other qualities companies should look for in internal translator candidates include comfort working with numbers, project management skill, and entrepreneurial spirit. Training curricula can then concentrate on the technical knowledge and practical methods that translators need.

Building basic analytics awareness

The first stage of a translator-training program should equip employees with fundamental analytics knowledge: a basic understanding of how analytical techniques can help solve typical business problems, as well as general familiarity with the process of developing analytics use cases.

This level of knowledge is readily attained from a week or so of classroom training covering:

  • The potential to use analytics broadly within their industry and, more specifically, across the business’s value chain.
  • General techniques for prioritizing analytics use cases and defining their scope.
  • An overview, and ideally a simulation, of the lifecycle of an analytics use case: defining a business problem, selecting target variables, brainstorming features of a potential solution, and interpreting results.
  • The roles that translators and other specialists (such as data scientists, data engineers, technical architects, and user-experience designers) play at each stage of an analytics use case.
  • The major types of analytical approaches (descriptive, predictive, and prescriptive), with deep dives into a few common algorithms (such as decision trees, neural nets, and random forests) and how they apply to business problems.
  • Methods for evaluating the performance of analytics models and understanding the trade-offs associated with particular models.
  • Agile ways of working—testing and learning from short development cycles, or “sprints”—that help multi-functional teams to deliver effective solutions swiftly.
  • Practices for embedding analytics solutions in the business and overcoming implementation difficulties, such as cultural barriers.

Translators also need the technical depth to hold their own when discussing problem-solving approaches with data scientists. Many take online tutorials to learn common programming languages, such as R or Python, and learn more complex algorithms. To lead delivery of use cases, though, translators must hone their skills through hands-on practice—much as language students reinforce their classroom learning when they are immersed among native speakers.

Click here to continue reading Louise Herring, Helen Mayhew, Akanksha Midha, and Ankur Puri’s article.

The post How to Train Someone to Translate Business Problems into Analytics Questions appeared first on Analytics in HR.

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At UNLEASH Amsterdam I had the pleasure of interviewing Amit Mohindra. Amit has a background in engineering and economics before becoming People Analytics lead at Apple. In this interview, we speak about people analytics nirvana, the importance of education, and the future of analytics. Enjoy!

UNLEASH: Interview with Amit Mohindra - YouTube

Erik van Vulpen: Hello everyone. I’m here with Amit Mohindra. Amit, very glad to have you.

Amit Mohindra: My pleasure. So happy to be here.

Erik: We’re here at Unleash, and you just gave a workshop on People Analytics Nirvana. Can you tell us what was the workshop is about?

Amit: Yeah. So, until a few years ago, you wouldn’t use people and analytics together in a sentence, let alone nirvana. So, the conference had so many sessions on analytics and some of the technical aspects. I thought it might be refreshing to take a bit of a different track, and think about analytics from a more reflective standpoint. And I feel I could do it, because I’ve been doing people analytics for quite a while. I decided to talk about this very esoteric notion of nirvana, as a destination, and try and help people at the event, sort of, sit back and reflect, because we often don’t have time to reflect.

We’re so busy, even at the conference, going from session to session. At work we’re busy working from project to project. And it’s only when you reflect, and think about what’s happened, that you can learn and improve. And not just what’s happened in the past, but what’s going to happen. That’s called reflecting on the future, and that’s what this conference is about, the future of work. What’s the goal of analytics? What’s the goal of technology?

So, I, sort of, had a bit of fun with it. It was a bit of a risk. I wasn’t sure if people would really come to this.

Erik: But, I think, it played out though. I heard that the room was packed. Because you’ve been, for the viewers that don’t know, you’ve been doing people analytics for quite a while already. Can you explain something about your background?

Amit: Yeah. Yeah. So, originally I am an electrical engineer. And I went to graduate school, to study labor economics, because I always felt growing up in India, that the solution to many of our problems was not necessarily more capital, but what do you do with so many people? Very real, sort of, situation. So, I became a labor economist. I didn’t really anticipate ending up in HR. I didn’t really know what HR was.

So, I became a research economist, studying labor markets, and somehow then I got into the private sector, and into consulting. And I worked in compensation and benefits for a long time. Then my first really formal people analytics job, was as the head of workforce intelligence in a healthcare company. And then Apple. But, I started out, the first time in 1999. I created a group called HR Strategy and Analysis, at Lehman Brothers.

So, 19 years ago, and at that point, it was just me. And I convinced my boss, “Let’s start this group, because there is so much work to do.’ And single-handedly I did predicting efficiency, predicting success. We looked at qualitative and quantitative information. We did some rudimentary text analytics. What companies did for the next 10, 15 years was already, sort of, done. And if you think about it, now there’s artificial intelligence and machine learning. But a lot of the mathematics haven’t changed. Survival analysis, it’s the same from 20, 30 years ago. Even some machine learning techniques. They were just called nonparametric econometrics, but now it’s artificial intelligence.

Erik: So, you’ve worked at Apple, but recently you’ve made a change. Tell us more about that.

Amit: Yeah. So, I was really fortunate to work at Apple for a few years. I was hired to set up the people analytics team. Which I did and built it from just a handful of people, to a bigger team. And it was time for me to move on. I decided I’d like to go back and work for myself again. And I’ve launched a company called “People Analytics Success” And it’s got three pillars so far. One is advisory. So, helping companies get started in analytics or do specific projects. It’s helping HR Tech startups that are applying machine learning, for example, to people’s situations, and how do you hone that as a product.

The second pillar is exactly what you … So, how do you help HR leaders who aren’t comfortable with analytics, get more comfortable, because now it’s pervasive, and you really have to be comfortable. Also helping people analytics leaders show up as HR leaders, so they can have a seat at the HR table. Otherwise, they’re just tools … they can be ignored. But if you’re actually making … you’re there making decisions, it’s helpful. I’ve also found it helpful for the analytics leader to also have some, sort of, other responsibilities. Whether it’s compensation or ability or operations. To have some, sort of, stake in the function of HR.

Then finally, education. So, that’s one big need, and I know you understand that as well, because you’re in that business too.

Erik: We’ve been dabbling in it.

Amit: It’s very impressive, and it’s so important to help HR business partners, and others, again, just get more confident and more comfortable. Hopefully, some of them will get so interested that they want to learn the mathematics, and the econometrics, but you don’t have to do that for everyone. It’s really, ultimately, what’s really important is how can you make the best use of the existing people analytics team, as a business HR person. And again, show up as a more strategic, business oriented HR person.

Erik: Yeah. And the education of course, is very important to, in the end, shift the culture and to actually implement the change. I think, that also goes back to why the analytics leader needs to be involved with the top HR team. They need to make an impact, and their voice needs to be heard in order to be truly effective.

Amit: Now, the talk I gave on the People Analytics Nirvana, I was talking about the newness of analytics, and what happens in the future. Maybe it’s not even a thing. Maybe it’s just pervasive, and you don’t need analytics team. And there is a way of thinking about it, using, I think it’s a Buddhist approach. So, think about it. There is no mountain. There is a mountain. There is. So, just think about that.

There is no people analytics. There is people analytics. There is. And it’s just pervasive. It’s part of how we do business. And that’s importance of the education piece. So, that everyone is equipped, and it may take a while …

Erik: Probably. Do you see analytics as an integral part of doing business? Does people analytics still exists as a separate unit? Or will it just be analytics? Will people analytics, in the end, disappear?

Amit: So, that’s a topic of debate. And, I think there’s a risk, I considered it a risk that the people analytics is consumed or subsumed within a broader analytics team. There are some advantages to that. I think, you’d probably get more resources, and you’d have different ideas. So, I think, you could get some creativity and innovation through that. But, I think, people analytics is different, because you’re dealing with people. You need to have some sort of understanding of how HR works. You have to have a stake in people and in their success. I think, people who get into HR have that. And if it’s just about the numbers, then you’re not doing justice to the function.

Erik: So, the passion for people, even if it’s through data, is still important.

Amit: Because, I think, you can just use numbers and make decisions that are not right. They don’t make sense. You still need the human interpretation. So, the advocacy of somebody who’s within the HR function, for people, to do that interpretation.

Erik: Any closing thoughts before we wrap this up, Amit?

Amit: This is an amazing event. There’s so much going on. Very frenetic. There’s a lot of talk about the future. About AI, and I see people getting caught up in that. So, what I would recommend is just, again, reflect. Sit back, think about things, don’t necessarily jump to action quickly. But, at the end of the day, action is what’s required from people analytics. And we’ve come a long way from having no data, to some and some useful data. We’ve come a long way in going from having data to insights. But the missing step, the last mile, if you will, is from insights to action. And that’s where, again, I think if the people analytics leader has a stake in the function, or is part of the leadership team, you can have more influence in actually making it happen.

Erik: Perfect. Amit, thank you very much.

Amit: Thanks Erik.

We will also join UNLEASH London. More information about this event can be found at the UNLEASH website.

The post Amit Mohindra: An Interview appeared first on Analytics in HR.

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What are 3 key things brands should focus on in creating a high performing digital analytics program?

1. Identifying an analytics leader who can clearly see the business problems that analytics can address and propose solutions that will generate positive ROI.

Successful analytics programs take a consolidated group effort. Success hinges on how well the organization utilizes internal domain knowledge, supplemented at times by external expertise to monetize data opportunities.

Large enterprise companies may be lucky enough to have a Chief Analytics Officer in place with strong executive sponsorship and oversight. A Director/VP of Analytics should be knowledgeable enough to play the connective tissue between the executives and true subject matter experts. 

This Director or VP role will make the business case for the programs in play (and the technology that deploys them) and can answer to the ROI of the program. This individual will be tasked with assessing people, processes, and platforms needed. He or she must also be able to decide when to buy, borrow, or build talent and technology to address the opportunities at hand.

The most common failure point for an analytics program stems from not having a clear and universal alignment on what problem the analytics team is trying to solve for. Companies invest in staff and technology to pump out models and insights – but left unguided, those solutions may not specifically address a business problem.

Those squandered hours reduce the expected ROI of the program. Once it becomes a frequent issue, the Finance Team (or even the CFO) starts questioning the value of the program and the technology driving them. Without clear alignment, analytics programs may see their future budgets reduced and their perceived value in the organization diminish. Getting the right person involved up front, and identifying the problem the team is looking to solve will help drive successful outcomes.

2. Focus on the foundational elements of measurement. Evaluate and define the business requirements, create and update a Solution Design Reference (SDR) that maps to the requirements and then implement it.

An SDR is a Solution Design Reference spreadsheet that documents all business questions and proposed variables that capture data relating to those questions. It is the foundation of any analytics implementation and its quality can be the difference between success and failure. 

The SDR is a blueprint of a digital analytics implementation. SDRs provides many benefits to an organization but three acute benefits are they help: 

  • Define business requirements identified by stakeholders
  • Map those requirements to variables in the analytics solutions
  • Facilitate staff understanding the implementation and variable definitions

Creating and consistently updating an SDR is a critical first step in creating a high performing digital analytics program. The process will enable organizations to capture the right data, instill trust in that data, and then allow downstream actions to be taken with confidence.

Click here to continue reading Reid Bryant’s article.

The post 3 Keys To Creating A High Performing Digital Analytics Program appeared first on Analytics in HR.

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Successful organisations understand that ROI isn’t just generated by the sales and marketing team, nor is it only a concern for the finance department. Generating Return on Investment should be a priority for every facet of a business; especially HR.

After all, your people are what create a great product or service, which in turn generates a profit (or loss), so ROI should naturally be a priority when it comes to managing people.

Every single person employed in a business is an investment, and by guiding our people to perform at their peak, reducing attrition, and investing further in our people to develop them, we can maximise productivity and the subsequent returns.

The following guide will offer a practical introduction to understanding how people generate ROI in different stages of their employment, and how to maximise and extend this.

We will cover:

  • Getting new starters to break even faster
  • Increasing performance post-break even
  • Accelerating and extending peak performance
Understand your break-even point

Naturally, the first area where organisations can maximise their profit potential is generating greater outputs and productivity levels from their workforce.

Performance naturally peaks and troughs along with the employee lifecycle, so by modelling this we can start to determine where new hires start generating returns for the business. This is demonstrated in the Employee Lifecycle Performance Curve.

Pre Break-even Onboarding

When an individual starts a new role, they go through a phase that requires investment by the organisation to get them to the point of break even, at which time the employee generally starts to generate more value for the organisation than it costs to employ them.

Perfect the onboarding process

A consistent and high quality onboarding process delivered consistently to all new starters will help limit wasted time at the beginning of an employee’s tenure while also giving them a good first impression of the organisation.

Where possible, get a head start by kicking off the onboarding process before an employee starts their first day. Most paperwork or compliance tasks can be completed over email in the lead up to their start date so the employee doesn’t have to spend time filling out forms when they start work.

This is just one way you can always be prepared for new starters and ensure they have everything they need to hit the ground running in their new role. Once the new starter has officially joined your organisation, all necessary training should also be provided as soon as possible, allowing them to start productive work sooner.

Build a solid foundation with probation

Probation checkpoints can easily be built into your onboarding process to further set new starters up for success in their first 3-6 months. Using a people management system like intelliHR you can set up automated check-in forms to go out to new staff during their probation, gathering insights for managers into any areas they may need to provide further support or recognition. This allows managers to deal with potential problems early on, giving the team member maximum opportunity to pass their probation, while ensuring performance-limiting issues are not carried forward into their ongoing employment.

Speed up the learning and ownership curve

One way the onboarding phase can be accelerated is by introducing new starters to their role in a phased approach. By starting them off with information they need to know, and simple tasks they can master fast, and gradually increasing complexity over time, onboarding costs stay low and ROI is achieved sooner. Encourage them to set achievable goals in these areas as soon as possible, so they start to gain personal rewards from achievements more quickly than they would have otherwise.

Post Break-even Performing

People can be considered to be in the “Performing” phase once they have reached the break even point. At this time, the organisation’s objective now firmly turns to help the employee both quickly reach and sustain peak performance. It is while an employee is at peak performance they are deriving maximum profitability for the organisation.

It is possible to take control of this performance curve to accelerate and even extend performance throughout the employee lifecycle. In order to achieve this, organisations must be equipped with the right tools, while also having an understanding of what drives performance in their people.

Click here to continue reading this article on intelliHR.

The post The Ultimate Guide to Generating ROI in HR appeared first on Analytics in HR.

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We are one month into the new year and as a reader of AIHR I am sure you have stumbled on many lists showcasing what the latest HR trends are. These lists consist of themes that come and go – from year to year. Nothing strange about this, because while the world is changing, HR changes with it.

There was, however, one HR trend in many of the 2019 HR trend lists, that didn’t fit this reasoning. It was the focus on organizational networks. The talk around organizational networks has been a part of HR and its trend lists for many years.

Organizational Networks is the talk of the town

In the article 9 emerging HR trends for 2015, Tom Haak from the HR Trend Institute already outlined that fewer people could be captured in the traditional org chart and that there were “weak signals” that the org chart was fading away.

These weak signals were turned into strong signals after the 2017 Global Human Capital Trends by Deloitte was released. This report showed that 94% of all surveyed companies mentioned that agility and collaboration were critical to their organization’s success and that 32% would be focusing on “designing their organization to be more adaptable and team-centric”

Organizational networks were the talk of the town and Josh Bersin – the co-author of the Global Human Capital Trends – highlighted in the article HR Technology in 2018: Ten Disruptions Ahead that organizational network analysis would be a promising method to help companies to design these new organizational structures based on the insights from the networks.

Josh Bersin did not leave it with that. One year later, in the article 5 HR Predictions for 2019, he was asked to share a new shortlist of HR trends. Surprisingly, he again argued that “HR must focus on team-based work and organizational networks”.

That’s why I was asking myself: how come we haven’t gotten to the point that organizational networks are mainstream? Also because the information shared around the need to understand organizational networks goes beyond the yearly trend lists. Even thought-leaders and practitioners try to help HR in making it mainstream and a part of every company.

Take for example David Green – a well-known speaker and writer in the field of HR analytics. In 2018 he published an article about the role of organizational network analysis in people analytics. In this article, he moves away from informing why companies should understand their organizational networks, to informing how companies can do this. The focus was to point HR to vendors classified by what type of data they use to understand organizational networks.

This brings me back to why it was unexpected that even Josh Bersin called out organizational networks as a 2019 HR trends – once again. Has HR not been informed enough? Or, on the contrary, has HR been overwhelmed with all that has been written about this topic? No matter what the answers are to these questions, there is a more central question:

How can we get rid of organizational network analysis on next year’s HR trend list?

Or in other words: how can companies become successful in enabling their workforce with insights into the organizational networks in 2019? These are the three key questions you need to ask yourself (so that the 2020 HR trend list will be about something new):

1. Why do you want to know the organizational networks?

It is crucial to understand what insights you want to gain from understanding your organizational networks. You need to know what you are expecting to achieve. Only when you have a clear understanding of the purpose, you can decide what data you need in order to get to the right organizational insights.

2. What types of data do you need to make actionable recommendations?

Organizational networks can be analyzed through many different types of data. Some data bring, however, more context to the relationships in the network than others. And with more context, you can get better actions.

Will you only rely on your organizational networks from data that you can track from the IT system such as email or enterprise social network and observe the relationships in the network without intervening and thereby risking the influence of personal biases of the observer? Or will you rely on the input from people through surveys and ask them about for what and why they are connected? Or do you want to combine the two? And what about benchmark data?

If you start this data-driven approach of looking into your organizational networks, how does then your organizational networks compare with other similar size networks in the same industries? Is it normal or are you off par and radical changes are needed?

Let me share a case to explain why the type of data is key to the success of your insights into your organizational networks.

A global leadership team of 150 leaders conducted an analysis of its organizational network. Rather than to do this based on relationship data from email, they shared a survey with five questions to all leaders. Each leader was asked questions such as who they collaborated with and who they would go to, to make sense of decisions. So, five questions about five different purposes. Data showed that only 4% were connected in all of these five types of networks.

3. What types of analytical approaches do you need to get to the right actions?

Organizational networks are more than only connections between people. It is also about knowing the sentiments and feelings of the people inside the networks. Understanding your organizational networks will then not only help to identify the right people; it will also help to engage the right people in the right actions.

For example, if the smallest group of employees who can reach the largest share of the organization is positive, you obviously need to do other things than when this similar group is negative.

Dealing with these data that go beyond the relationships itself and move into sentiments and feeling require different analytical capabilities as well as a secure way to handle personal data. As an example, being on the edge of a network does not mean you are not bringing value to the company. On the contrary, such a person could have been on a client assignment for the past 12 months. So, would you want to use internal resources to get organizational network insights? Or do you want to use an external party to bring you the right organizational insight into your networks?

Instead of talking about the importance of ONA, we need to start talking about how we can add value with ONA. In this article, I’ve proposed a 3-step model. This model is a tested and integrative part in the conversations I have with companies who want to run an organizational network analysis. And when the company makes the decision based on these steps, it has always resulted in accurate and actionable workforce insights:

  • Accurate, because the company combined the right data representative to the whole company
  • Actionable, because it is clear for what they want to use the insights – which is also a GDPR requirement to get consent on before you can process personal data

So, get it right with organizational networks by carefully considering these three questions. And who knows, the 2020 HR trend list may look different next year!

The post How can we get rid of ONA on next year’s HR trends list? appeared first on Analytics in HR.

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One of the most common models in HR analytics is shown in Figure 1. The model starts with the data, then you draw insights, and finally tell a story. This isn’t entirely wrong; it’s just not the best way to approach analytics and it’s caused some heartaches for HR professionals trying to do the right thing.

Figure 1. The hard way to an analytics story

Here is how this model usually plays out: An HR professional is given a report, usually data drawn from the HRIS, and is asked to look at the data, derive an insight, then tell a story. Normally, HR professionals find this very hard to do, not because they lack the right competencies, but because it’s genuinely difficult to find legitimate insights in rows of numbers and figures. Even if you were a data scientist with lots of data, tools, and time, it would be a challenge to find insights that would have an impact.

The secret to success is this: The story lies in the business issue, not in the data.

The story lies in the issue–not the data

Let’s imagine you have the following business issue: customers at a consumer electronics chain complain that the staff don’t know very much about the products. Everyone seems to have an idea why this is happening: Some think training is poor. Others think customers have unrealistic expectations. The regional head thinks it’s because turnover is high, which has left the stores with inexperienced staff who don’t know the products. The regional head is pushing HR to improve retention so that stores have more highly experienced staff.

We now have the right starting point for analytics: a business issue followed by several hypotheses about the cause of the issue.

Since the regional head is the senior leader in the area, we might test her hypothesis first. HR pros are asked “Is there any data showing that inexperienced staff generate more complaints than experienced ones?”

HR may be lucky and have complaint data for each associate so that they can directly test the hypothesis. However, it’s more likely that such fine-grained data will not be available. If this is the case, they might instead look at the number of complaints by store versus the average tenure at each location. This isn’t perfect, but let’s see how the insights and story might play out.

If the regional manager was correct, then we’d expect stores with fewer experienced staff to have more complaints. But imagine that the data shows little difference. Now HR should go to the regional manager and tell this kind of story:

  • Issue: Customer complaints
  • Hypothesis: Inexperienced staff are the source of complaints
  • Decision we need to make: Whether to make a significant investment in retention
  • Data: Complaints by store plotted against average tenure at the store
  • Insight: Tenure doesn’t appear to have much impact on complaints
  • Conclusion: We can’t be sure, but it appears likely that a big investment in retention would not solve the problem of customer complaints; something else is causing the problem.

This list of bullets is the story HR is telling. The regional head is sitting on the edge of their seat because they care a lot about the business issue of reducing customer complaints. HR had little trouble drawing an insight from the data because they knew exactly what hypothesis they were testing. The story flows from the business issue, which then guides the data collection and analysis.

Why is the Data-Insight-Story model hard to give up?

If it’s much easier for HR to start with an issue and derive the story from that, rather than staring blankly at a page of numbers hoping for insight, why is the Data-Insight-Story model so popular? I’m guessing here, but I think there are two reasons:

Click here to continue reading David Creelman’s article.

The post Why Workforce Analytics Shouldn’t Start with the Data appeared first on Analytics in HR.

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Social Network Analysis (SNA) is among the most trending topics in People Analytics. HR analytics professionals are now starting to conduct SNAs to unearth collaboration, detect silos, and identify potential leavers.

Even though SNA is only now starting to become a standard tool in People Analytics, advanced network and graph theory can already provide various theoretical insight that can inform SNA and demonstrate how important it is to understand organizations as what they really are: an interconnected accumulation of social groups. In this article, I will argue that People Analytics teams should pay more attention to the dynamics and quality of social networks. Real-world networks are never static and so our analyses of them shouldn’t be either. I will address three streams of theory that provide a new approach to analyze organizations through the lens of network theory.

Dynamic Network Theory

Only very few networks in nature are static, however most conducted SNA create networks that only visualize the current state of interconnectedness within the organization. This single snapshot of the organization can already provide lots of insight about numerous metrics, such as the network’s density or its number of distinct clusters. However, these analyses cannot tell the larger and perhaps more important story of how they evolved.

Actively comparing two states of the same network brings about a whole new dimension of network metrics and parameters. The insight which nodes (employees) of a social network produce more connections than others and how new nodes become integrated within the network can provide valuable insight for any people analytics team. Helpful for practitioners is the knowledge about critical thresholds in the development of (social)-networks. When networks grow (or shrink), a certain amount of connections and members is critical for the establishment of its effectivity.

The underlying idea, the so called network effect states that the relationship of nodes (employees) to edges (connections) is not linear, but exponential. As the number of connections in a network grows by the function:

A network with 5 members can only have 10 possible connections:

The addition of just 4 more members allows the organization to build up to a network of already 36 connections. For a company, this means that despite a linear headcount growth its interconnectedness can grow exponentially!

The critical threshold that enables efficient collaboration and robustness with n members of a network is the average number of connections of log(n). At this threshold of average connections, the members typically start to be at least indirectly connected to each other.

How networks grow

The likelihood of new edges within a social network can also be seen as a statistical probability, given the characteristics of members that are already connected in the network. Knowledge about these characteristics can be used to identify patterns and derive rules of preferential attachments in your organization.

Typically social networks form under the influence of homophily, which describes the tendency of connections to establish between similar members of a network (McPherson, Smith-Lovin & Cook, 2001). If a SNA e.g. reveals that employees of the same age, or the same educational background tend to stick together, dynamic network analysis allows to calculate the probability of how likely it is that any two members will form a new link, given their educational and age attributes. Homophily can actually reveal itself as a driver of silo constitution. When trying to enhance diversity within an organization, such analysis could be an insightful starting point.

Percolation Theory

A good theory to explain how (social) networks grow is the percolation theory, which describes how connections in any network develop through the paths of least resistance: the network then becomes the product of the resource constraints the environment places on it (Newman, & Watts, 1999).

Put differently: The network is shaped and limited by the environment’s characteristics. This can be observed in companies that occupy buildings with multiple floors. Social connections are usually more likely to develop between coworkers on the same floor. The social network is then showing numerous clusters of coworkers on identical floors. In People Analytics, understanding which resource constraints are influencing the development and topology of the organizational network can be tremendously important and, for example, inform organizational development projects.

Robustness of Social Networks

Networks cannot only differ in their size or density but also on a dimension that is not as easy to spot on a first glimpse: its structural robustness. The robustness of a network describes its ability to remain functional after it loses members, or connections within the network dissolve. For People Analytics teams the level of robustness can be a good indicator how vulnerable the organization is to potential turnover.

A key figure that correlates with robustness is the networks connectivity – a highly interconnected network can easily cushion the loss of a single connection, while a loosely connected network has a higher chance of becoming disintegrated by the reduction of a single edge or node. In business terms: the identification and number of bottlenecks become one of the key outcomes of a social network analysis.

  1. In a high density network the reduction of one edge is not damaging its overall structural integrity: the network is able to compensate trough two other direct connections
  2. In a low density network the reduction of one edge can disconnect two parts of the network that can only be bridged by a longer chain of indirect connections

The measure of network closure describes the extent to which two connected people have one or more mutual connections – in the given example above, this closed relational triad on the left side compensates for the loss of the connection – the network on the right side fails to do that.

When thinking about the reduction of nodes or edges, the degree of betweenness of a social network is crucial to understand its robustness. It describes the amount of critical bridges in a network that connect two clusters and which removal can thus cause vast damage to the average distance between nodes within the network. Understanding and fostering the betweenness, density and connectivity of a network should thus be the aim of every People Analytics team, conducting SNA.

The ambivalence of high density networks

Strong ties and bonds between members are preferential in social networks. In People Analytics they often correlate with employee satisfaction and innovation (Hulbert, 1991). Therefore, highly interconnected organizations are often seen as desirable.

But what makes high density networks so powerful can also reveal itself as a weakness: high density does not only empower the collective intelligence of an organization, but can also accelerate the spread of negativity or low levels of engagement. Toxicity has in fact been found to be contagious within organizations (Dimmock, & Gerken, 2018). This is aligned with findings in biology, where high density networks are more prone to be disrupted through virus infections.

Furthermore, the density of organizational networks does only partly correlate with performance: every manager knows – too many stakeholders and feedback loops can also hinder effective work streams. In People Analytics the density of organizational networks should therefore be seen as both, a chance for higher collaboration and diversity but also as a risk for the collective spread of low engagement and toxicity. As per usual, the truth lies somewhere in between, which brings me to my last argument.

Why weak ties matter

What organizational design (and its analysis) should aim for, is a network with links of different quality: Teams, tribes and cliques within companies are usually characterized by strong mutual connections, but are also often subject to group think or a lack of innovation (Kim & Choi, 1999).

In these cases, weak connections to members of a different cluster that are only held by one or two members of a clique are extremely important as they can contribute different perspectives and constitute bridges between clusters. In short, weak ties can contribute fundamentally to the diversity of a network, while not putting it at risk of inefficiency or turning it into a highly contagious environment.

People Analytics or SNA can help us to better understand this diversity and the quality of collaboration and connectedness within an organization.

Concluding thoughts

Many concepts, ideas and methods of (dynamic) network theory can become useful for people analytics, especially when trying to understand the origin and future of an organization’s social network. Real world networks are never static and so shouldn’t be our analyses of them. People Analytics must develop methods and processes to pay sufficient attention to the dynamics of social networks and differentiate between different forms and strengths of interactions.  

References

McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1), 415-444. http://aris.ss.uci.edu/~lin/52.pdf

Newman, M. E., & Watts, D. J. (1999). Scaling and percolation in the small-world network model. Physical review E, 60(6), 7332. https://journals.aps.org/pre/abstract/10.1103/PhysRevE.60.7332

Hurlbert, J. S. (1991). Social networks, social circles, and job satisfaction. Work and occupations, 18(4), 415-430. https://psycnet.apa.org/doi/10.1177/0730888491018004003

Choi, J. N., & Kim, M. U. (1999). The organizational application of groupthink and its limitations in organizations. Journal of Applied Psychology, 84(2), 297. https://psycnet.apa.org/doi/10.1037/0021-9010.84.2.297

https://hbr.org/2018/03/research-how-one-bad-employee-can-corrupt-a-whole-team

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Last year, analyst Josh Bersin noted in a Forbes article that his studies have shown 69% of companies are embarking on a people analytics strategy. However, many human resource (HR) leaders constantly intermingle people analytics with workforce analytics and talent analytics. Although the terms might mean the same thing in people’s minds, there is a fine distinction among the three.

In order to understand the difference, it is necessary to go back a few years and look at some of the prior drivers that pushed HR technology into the current era. Although the first human resource information system (HRIS) was introduced back in 1987, it was really the introduction of workforce planning and talent management solutions that gave both clients and vendors the current road map for development, innovation and practice.

The Evolution Of Workforce Planning And Talent Management Solutions

Workforce planning had its genesis from the simple practice of building organizational charts. This gave companies the ability to visualize their employees and open positions in a hierarchical and understandable format. It was this hierarchical construction that allowed the obtaining and rolling up of key workforce data by various filters, such as department and location, as well as seeing where the most critical hiring needs were.

The data workforce planning generated was always static, however, in that it showed only the situation at a current point in time. Some of the more progressive companies in the space, such as Ingentis, Aquire Solutions and HumanConcepts, started to present reports that could trend this data over time, which led to the advent of workforce analytics. But workforce planning or analytics was not designed to follow the life cycle of employees — that came with the introduction of talent management solutions from such pioneers as SAP SuccessFactors, HRsmart, Taleo and Cornerstone OnDemand.

Incorporating the five stages of an employee in an organization, talent management solutions track employees from their status as job candidates through hiring and onboarding to provide methodologies that measure performance and develop career plans. All along the way, these solutions provide metrics on both individual achievement and the progress of groups of employees in aggregate.

As with workforce planning, this information was originally presented solely as static data. And although the applications can provide some primitive analytics for each cycle, most of what is provided does not go across all the various cycles to capture a complete and holistic view.

Gaining A Full View With People Analytics

People analytics has evolved to combine elements of workforce planning and talent management by providing consumers with that holistic and hierarchical workforce view, while also allowing the trending to go through all the phases of the talent life cycle.

By aggregating all relevant HR data, both from internal systems and external big data benchmarks, into key metrics, people analytics provides rich historical trending that can project into the future and provide top-level and granular projections of where an organization is heading. Then, by analyzing that historical data, predictive models can be built out to see which changes in current practice forecast the best results.

People analytics can also fold financial data into projections. Despite having ownership of an organization’s most valuable asset — its employees — HR has often had a difficult time being included in the most important business decisions. However, by having the ability to apply monetary weights to their actions, accomplishments and investments, human resource leaders can gain needed credibility by showcasing financial returns.

Finally, the incorporation of artificial intelligence (AI) enables analytics to be built on repetitive calculations. With AI-based machine learning, those calculations can not only be run ad infinitum, but can also be programmed to correct themselves. Thus, models become more intelligent as they rule out negative eventualities. Natural language-based commands pick up nuance and slang. And corrections are made intelligently to any flawed calculations.

Click here to continue reading Tom McKeown’s article.

The post Identify Your HR Needs To Determine The Appropriate Analytics Solution appeared first on Analytics in HR.

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In early January 2019, a headline on the McKinsey Leadership and Organization blog caught my eye – “The critical importance of the HR business partner.” A key point made in the piece is that HR continues to struggle to deliver effective talent strategy. The disconnect centers on the lack of capability of HR business partners (HRBP), those who counsel managers on talent issues. The article remarks that the value of great HRBPs remains unquestioned. However, a great HRBP is hard to find and the structure of the HRBP role requires re-engineering. 

For me, the article does not go far enough in describing the disconnect. From my research, not only must these senior partners deliver strategic advice to business leaders on talent issues, they must also support these leaders in getting optimal performance from their talent. They must be able to measure talent performance. But to do this, HRBPs must manifest superb data and analytical skills.

Research says HRBPs are not data-savvy

In my research, HRBPs are not yet succeeding with the challenge to be comfortable using data. Indeed, they may not even be the right evangelists! (but that’s for another article)

Early last year, I embarked on a research effort to determine critical practices to succeed with people analytics. I also looked into key roles that contribute to the success of analytics.

A Vice President responsible for HR operations at a top US bank told me that his HRBPs weren’t up to the task of evangelizing people analytics. And, despite the transformation work the bank had done to enhance the strategic capabilities of its HRBPs, they were not even yet truly strategists.

Further, maybe only 10% were comfortable and competent with data! It was a refrain I heard often as I continued to do interviews last year looking at great people analytics practices. We also confirmed this with survey data.

Survey says HRBPs are a key obstacle to the success of people analytics

In our Age of People Analytics Survey, we discovered that a key obstacle to success of people analytics was “we do not have a ‘data-driven’ skillset within HR and / or our HR business partners.”

This was particularly prevalent in organizations just getting started with people analytics! When I present these findings at conferences and in workshops and ask audiences if they have similar issues, the audience laughs! “Of course! It’s our primary challenge too!”

Why is people analytics important?

Let me come at this topic from another direction from some long-running research based on the Sierra-Cedar HR Systems Survey that I managed for its first 16 years. It speaks to why people analytics is important.

This survey, that looks at HR technology adoption and the value achieved from adopting technologies, reports that starting back in 2000, organizations with some form of workforce analytics outperform organizations without. The characteristics of these organizations includes:

  1. High level of people analytics process maturity including the use of a people analytics solution
  2. Higher than average number of data sources integrated
  3. Higher than average number of analytics topics (metrics) available for analysis
  4. Higher than average use by managers as opposed to just the HR community

Organizations typically start to enable people analytics usage primarily among the HR community, but the above shows a correlation between managers as people analytics users and improved financial performance. Thus, when beginning to enable people analytics, the question I think is important is, “do you enable usage directly to business leaders and managers or through HRBPs?”

If it’s going to be the latter, we need to better prepare them. Keep in mind that not only does the HRBP role require re-engineering (which means expansion of their role definition to include these data and analytical skills), individual HRBPs also can benefit from having HRBP managers that manifest these skills as well!

In any case, here are a few ways to prepare HRBPs to be data-savvy so they can evangelize people analytics:

Define their roles and responsibilities related to data and analytics

First ask, what do you want them to do? In talking with numerous executives at organizations, they suggested the data-savvy HRBP of the future must be able to do the following.

Summarizing from an article I wrote last year on the roles and responsibilities of the HRBP manager of the future but applicable for HRBPs too, here’s what they need them to do over and beyond what they already do:

  1. Link the business strategy with talent strategy and back this up with data on how the workforce is doing in meeting the key business outcomes of the strategy.
  2. Be open to using (and also becoming) a champion of people analytics. Learn to use people analytics solutions and then use your change management skills to champion people analytics among business leaders and people managers.
  3. Use analytics to become a “data-driven” strategic HR business partner. Come prepared to any discussion with your business leaders and managers to show how the workforce is meeting business goals.
  4. Apply analytics to improve talent processes. For any part of the employee lifecycle, be prepared to show measures of the process: “So, how is the organization doing with acquiring, developing, and retaining the best workforce?”
  5. Stimulate other HRBPs to work collaboratively to use and promote people analytics. Learning together breeds comfort with people analytics. Take on a project as an HRBP team to address a key challenge of the business, such as where to open a new distribution center or how to be a more agile organization.
  6. Learn to tell a story with data. Don’t just report metrics but tell the story about how the data relates to a business challenge. For example, don’t just report turnover of 15% and how it is trending but talk about how that translates into increased costs of hire and decreased revenue from those leaving the organization.
  7. Put the right HRBP Manager in charge of the team. In addition to having responsibilities for the HR aspects of the job, this person should enable people analytics within the organization.

Now not all organizations are here – far from that. A good way to check readiness is to look at the current HRBP’s skillsets.

Assess HRBP skills

Start by assessing your HRBPs capabilities, and then work to help them develop necessary skills, whether that be how to use a people analytics solution, how to use data and analytics, or how to tell stories with data.

At Visier, we suggest using a gap analysis to determine which HRBPs have the skills, who they need to develop, and even who they need to replace to get the skills needed. In figure 1, we show the key capabilities that HRBPs need to be most effective as promoters of people analytics usage. We use this model to assess the capabilities of a customer’s HRBPs and identify which capabilities to enhance.


Figure 1: HRBP Capability Mode
Understand how the HRBP delivers workforce insights in a transformed HR service delivery model

When it comes to using HRBPs as your evangelizers of people analytics, it’s important to understand how HR service delivery is changing. Until recently, the transformation of the HR service delivery model focused on simply leveraging automation for record-keeping and transaction management, while moving the HRBP into a strategic consultative role.

The emerging strategic services model elevates the ability of HRBPs to deliver workforce insights within and beyond HR so they are able to consistently use data to advise leaders and people managers on strategy.

For example, one of our customers, a not-for-profit healthcare organization with over 50,000 employees, developed a program to enable its HR business partners to deliver quantifiable business impact. Its evidence-based partner consulting model consists of a three-pronged approach to ensure this impact:

  1. It has a toolset that HR uses to bring data, analysis, and insights to the forefront of problem-solving.
  2. It is building a skillset in the appropriate use of the toolset, along with developing consultative HR competencies applied to problem-solving.
  3. It is also impacting mindset, a business-focused approach to problem-solving, one that uses the toolset and skillset in partnering with leaders to drive outcomes and success in meeting the organization’s goals and objectives.      
Develop HRBPs

Changing the mindset and skillset of HRBPs to have a business- and data-focused approach to problem solving requires development and training. This will vary by organization and its maturity with people analytics. If just getting started, develop and train pilot users or super users. Then take a train-the-trainer approach for others. If further along, the focus of development may need to be to help HRBPs develop a hypothesis and tell stories with data.

While HRBPs are learning and developing enhanced capabilities, it’s important to give them time to learn and excel. They need to be freed from other work. In the previously mentioned survey, among advanced organizations, we see they much more frequently free their HRBPs from other activities while they are helping them become effective agents of change.

Communicate at a regular cadence

Once organizations start on the journey to change the mindset and skillset of their HRBPs, it’s important to create a communication plan, and then to maintain a regular cadence to build momentum as you move your organization to a data-driven culture. We see organizations providing newsletters, brown-bag lunches, and wikis to build capability. Focus these on helping your HRBPs engage with their business leader and manager clients.

Provide HRBPs with support through a people analytics center of excellence

Training and communications is, of course, ongoing support. Over and beyond that, the organization needs to set up a way to support HRBPs and other users. Among advanced organizations, we see them establishing a center of excellence focused on people analytics support.

With this, they can apply a “fit for purpose” approach to both get data, education, and support to the broad set of stakeholders within the organization, including their HRBPs. This kind of support structure also ends up freeing the people with deep analytics skills to focus on the more sophisticated analytics needed within organizations.

Track HRBP progress and results and reward with recognition

We’ve all heard the saying, “What gets measured gets done.” And, not only that, what gets measured gets improved upon!

So HRBPs should be challenged to set goals they wish to accomplish with people analytics and then periodically review them and assess their success. Both individuals and organizations can benefit from a continuous process improvement approach!

More importantly, recognize your HRBPs for their progress and results. One of our customers gives their HRBPs recognition badges for completing tasks on their action plans and goals. That public recognition reminds not only the individual, but their colleagues, and their business and manager partners just how important it is to the organization to become data-driven.

We can make HRBPs ready for the challenge to be people analytics evangelists!

We have to re-engineer what HRBPs need to be doing going forward by defining their new roles and responsibilities related to data and analytics. We need to honestly assess the skills of what we do have. Maybe to jumpstart creating more applicable HRBPs, we even need to hire a new HRBP manager for the future.

As we go through HR transformation, whether of the HR organization itself or through HR digitization, we need to reassess the role of service delivery in a future with new people analytics tools and capabilities. We need to develop HRBP skills with sensitivity to the level of maturity of the organization with people analytics. Once we start on our people analytics journey, we need a regular cadence of communications to our HRBPs as well as to the organization.

We need to evolve to a support structure through a people analytics center of excellence. And, we need to track HRBP goals and performance against goals and recognize them for their achievements.

The post The HRBP as People Analytics Evangelists – Are They Ready – No! Can They Be – Yes! appeared first on Analytics in HR.

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In early January 2019, a headline on the McKinsey Leadership and Organization blog caught my eye – “The critical importance of the HR business partner.” A key point made in the piece is that HR continues to struggle to deliver effective talent strategy. The disconnect centers on the lack of capability of HR business partners (HRBP), those who counsel managers on talent issues. The article remarks that the value of great HRBPs remains unquestioned. However, a great HRBP is hard to find and the structure of the HRBP role requires re-engineering. 

For me, the article does not go far enough in describing the disconnect. From my research, not only must these senior partners deliver strategic advice to business leaders on talent issues, they must also support these leaders in getting optimal performance from their talent. They must be able to measure talent performance. But to do this, HRBPs must manifest superb data and analytical skills.

Research says HRBPS are not data-savvy

In my research, HRBPs are not yet succeeding with the challenge to be comfortable using data. Indeed, they may not even be the right evangelists! (but that’s for another article)

Early last year, I embarked on a research effort to determine critical practices to succeed with people analytics. I also looked into key roles that contribute to the success of analytics.

A Vice President responsible for HR operations at a top US bank told me that his HRBPs weren’t up to the task of evangelizing people analytics. And, despite the transformation work the bank had done to enhance the strategic capabilities of its HRBPs, they were not even yet truly strategists.

Further, maybe only 10% were comfortable and competent with data! It was a refrain I heard often as I continued to do interviews last year looking at great people analytics practices. We also confirmed this with survey data.

Survey says HRBPs are a key obstacle to the success of people analytics

In our Age of People Analytics Survey, we discovered that a key obstacle to success of people analytics was “we do not have a ‘data-driven’ skillset within HR and / or our HR business partners.”

This was particularly prevalent in organizations just getting started with people analytics! When I present these findings at conferences and in workshops and ask audiences if they have similar issues, the audience laughs! “Of course! It’s our primary challenge too!”

Why is people analytics important?

Let me come at this topic from another direction from some long-running research based on the Sierra-Cedar HR Systems Survey that I managed for its first 16 years. It speaks to why people analytics is important.

This survey, that looks at HR technology adoption and the value achieved from adopting technologies, reports that starting back in 2000, organizations with some form of workforce analytics outperform organizations without. The characteristics of these organizations includes:

  1. High level of people analytics process maturity including the use of a people analytics solution
  2. Higher than average number of data sources integrated
  3. Higher than average number of analytics topics (metrics) available for analysis
  4. Higher than average use by managers as opposed to just the HR community

Organizations typically start to enable people analytics usage primarily among the HR community, but the above shows a correlation between managers as people analytics users and improved financial performance. Thus, when beginning to enable people analytics, the question I think is important is, “do you enable usage directly to business leaders and managers or through HRBPs?”

If it’s going to be the latter, we need to better prepare them. Keep in mind that not only does the HRBP role require re-engineering (which means expansion of their role definition to include these data and analytical skills), individual HRBPs also can benefit from having HRBP managers that manifest these skills as well!

In any case, here are a few ways to prepare HRBPs to be data-savvy so they can evangelize people analytics:

Define their roles and responsibilities related to data and analytics

First ask, what do you want them to do? In talking with numerous executives at organizations, they suggested the data-savvy HRBP of the future must be able to do the following.

Summarizing from an article I wrote last year on the roles and responsibilities of the HRBP manager of the future but applicable for HRBPs too, here’s what they need them to do over and beyond what they already do:

  1. Link the business strategy with talent strategy and back this up with data on how the workforce is doing in meeting the key business outcomes of the strategy.
  2. Be open to using (and also becoming) a champion of people analytics. Learn to use people analytics solutions and then use your change management skills to champion people analytics among business leaders and people managers.
  3. Use analytics to become a “data-driven” strategic HR business partner. Come prepared to any discussion with your business leaders and managers to show how the workforce is meeting business goals.
  4. Apply analytics to improve talent processes. For any part of the employee lifecycle, be prepared to show measures of the process: “So, how is the organization doing with acquiring, developing, and retaining the best workforce?”
  5. Stimulate other HRBPs to work collaboratively to use and promote people analytics. Learning together breeds comfort with people analytics. Take on a project as an HRBP team to address a key challenge of the business, such as where to open a new distribution center or how to be a more agile organization.
  6. Learn to tell a story with data. Don’t just report metrics but tell the story about how the data relates to a business challenge. For example, don’t just report turnover of 15% and how it is trending but talk about how that translates into increased costs of hire and decreased revenue from those leaving the organization.
  7. Put the right HRBP Manager in charge of the team. In addition to having responsibilities for the HR aspects of the job, this person should enable people analytics within the organization.

Now not all organizations are here – far from that. A good way to check readiness is to look at the current HRBP’s skillsets.

Assess HRBP skills

Start by assessing your HRBPs capabilities, and then work to help them develop necessary skills, whether that be how to use a people analytics solution, how to use data and analytics, or how to tell stories with data.

At Visier, we suggest using a gap analysis to determine which HRBPs have the skills, who they need to develop, and even who they need to replace to get the skills needed. In figure 1, we show the key capabilities that HRBPs need to be most effective as promoters of people analytics usage. We use this model to assess the capabilities of a customer’s HRBPs and identify which capabilities to enhance.

Figure 1: HRBP Capability Model
Understand how the HRBP delivers workforce insights in a transformed HR service delivery model

When it comes to using HRBPs as your evangelizers of people analytics, it’s important to understand how HR service delivery is changing. Until recently, the transformation of the HR service delivery model focused on simply leveraging automation for record-keeping and transaction management, while moving the HRBP into a strategic consultative role.

The emerging strategic services model elevates the ability of HRBPs to deliver workforce insights within and beyond HR so they are able to consistently use data to advise leaders and people managers on strategy.

For example, one of our customers, a not-for-profit healthcare organization with over 50,000 employees, developed a program to enable its HR business partners to deliver quantifiable business impact. Its evidence-based partner consulting model consists of a three-pronged approach to ensure this impact:

  1. It has a toolset that HR uses to bring data, analysis, and insights to the forefront of problem solving.
  2. It is building a skillset in the appropriate use of the toolset, along with developing consultative HR competencies applied to problem-solving.
  3. It is also impacting mindset,a business-focused approach to problem-solving, one that uses the toolset and skillset in partnering with leaders to drive outcomes and success in meeting the organization’s goals and objectives.      
Develop HRBPs

Changing the mindset and skillset of HRBPs to have a business- and data-focused approach to problem solving requires development and training. This will vary by organization and its maturity with people analytics. If just getting started, develop and train pilot users or super users. Then take a train-the-trainer approach for others. If further along, the focus of development may need to be to help HRBPs develop a hypothesis and tell stories with data.

While HRBPs are learning and developing enhanced capabilities, it’s important to give them time to learn and excel. They need to be freed from other work. In the previously mentioned survey, among advanced organizations, we see they much more frequently free their HRBPs from other activities while they are helping them become effective agents of change.

Communicate at a regular cadence

Once organizations start on the journey to change the mindset and skillset of their HRBPs, it’s important to create a communication plan, and then to maintain a regular cadence to build momentum as you move your organization to a data-driven culture. We see organizations providing newsletters, brown-bag lunches, and wikis to build capability. Focus these on helping your HRBPs engage with their business leader and manager clients.

Provide HRBPs with support through a people analytics center of excellence

Training and communications is, of course, ongoing support. Over and beyond that, the organization needs to set up a way to support HRBPs and other users. Among advanced organizations, we see them establishing a center of excellence focused on people analytics support.

With this, they can apply a “fit for purpose” approach to both get data, education, and support to the broad set of stakeholders within the organization, including their HRBPs. This kind of support structure also ends up freeing the people with deep analytics skills to focus on the more sophisticated analytics needed within organizations.

Track HRBP progress and results and reward with recognition

We’ve all heard the saying, “What gets measured gets done.” And, not only that, what gets measured gets improved upon!

So HRBPs should be challenged to set goals they wish to accomplish with people analytics and then periodically review them and assess their success. Both individuals and organizations can benefit from a continuous process improvement approach!

More importantly, recognize your HRBPs for their progress and results. One of our customers gives their HRBPs recognition badges for completing tasks on their action plans and goals. That public recognition reminds not only the individual, but their colleagues, and their business and manager partners just how important it is to the organization to become data-driven.

We can make HRBPs ready for the challenge to be people analytics evangelists!

We have to re-engineer what HRBPs need to be doing going forward by defining their new roles and responsibilities related to data and analytics. We need to honestly assess the skills of what we do have. Maybe to jumpstart creating more applicable HRBPs, we even need to hire a new HRBP manager for the future.

As we go through HR transformation, whether of the HR organization itself or through HR digitization, we need to reassess the role of service delivery in a future with new people analytics tools and capabilities. We need to develop HRBP skills with sensitivity to the level of maturity of the organization with people analytics. Once we start on our people analytics journey, we need a regular cadence of communications to our HRBPs as well as to the organization.

We need to evolve to a support structure through a people analytics center of excellence. And, we need to track HRBP goals and performance against goals and recognize them for their achievements.

The post The HRBP as People Analytics Evangelists – Are They Ready – No! Can They Be – Yes! appeared first on Analytics in HR.

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