HR Analytics and the challenges



If I may, I'm going to start with a somewhat base level analogy, but one that I think serves a purpose!

 
Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it...

HR analytics seems to be exactly the same

We started Harnham in 2006, and reflecting on our 8 years, the market and industry is almost unrecognizable from the early days of our business. The use of data is of course growing exponentially, and companies are placing more importance on using data to make intelligent decisions. The era of just following gut instinct seems to be over.

With an increase in the volume of data available, companies begin to look at new ways to use the data at their disposal, both for external and internal gain. With that in mind, I attended a conference on HR analytics recently and got a fascinating insight in to this growing sector.

Of course the term HR analytics is not new. This has been around for a long time – but I learnt that there is a real desire and a real need to use the data available in new ways, and to change the analysis that companies are performing to add additional value.  

The buzzword from attendees at the conference seemed to be "journey". Companies have spent years gathering and collating data on employees, but now seem to be at a crossroads - do you continue just compiling this data and generating excel reports on whatever metric has been asked for by a manager that week, or is there a better way to use this data? Are we at a point where we can start going further? Can we start using this data for predictive analysis?

Here is where I feel we hit three challenges.

1. ROI

There was a fascinating presentation at the conference in which a case study was shared by a major retailer. They found that in a 3 year study, they could prove a link between an engaged workforce and the profit of a store. I'm sure you'll agree that this is a great find and a useful exercise, but I wondered how many organizations would be willing to spend 3 years analyzing the relationships in their business to find out if it would have a small increase in profits at a local level. Herein lies the problem, - as the actual analysis of this data seems to be a new thing but how many management teams are willing to invest in it?

It seemed to me that the analysts and data managers that I met were confident that they were able to give amazing insights to their businesses, but did not have the time and resources to get in to the data and have a look around.  Instead they were simply required to submit excel reports to show what has happened rather than predict what this means for the future or make recommendations on what the organization to do as a result.

 

2. Data Quality

The systems where this data is stored largely seem to require input from a manager within the business, and at the point they're inputting this data it is likely that they are either just dealing with someone leaving the team or joining the team. In either of these situations, I would imagine they're busy and stressed, so will potentially not be too worried about what they're putting in to a workforce management system. 

The upshot is that the data you're then analyzing may not actually give you a true reflection of the fact. Again, it seems like the person inputting the data needs to understand the value to them of making sure that it is correct and I was not convinced any of the presenters had quite got this right in their business.

3. Skill Set

I listened to a fascinating debate at the conference about what skills would be needed to grow a team of HR analysts, and I would say the room was split almost 50/50 between the "data is data" opinion and the "HR knowledge is essential" group.

I reflected on this afterwards based on a comment made during the discussion.

It was noted that HR analytics is probably 5-10 years behind other analytics disciplines such as logistics and marketing analysis for example. I would agree with this, but rather than worrying about this fact, the teams should look at the reasons why and also how to utilize those skills.

Analysts in marketing or credit risk don't study marketing or credit risk at University. They study mathematics, physics, statistics or similar and then apply these techniques to an industry. They don't know everything about marketing strategies on their first day in the role, but they can model data to tell you with a very high degree of certainty what the propensity for something to happen is. Data is data - you just ask it the questions.

So if companies don't utilize these data skills, my concern is that they will stay behind other analytical disciplines and only be able to do a small proportion of what could be possible with the data available to them.

The Ethical Standpoint

People are more aware of their personal data now than they were 5-10 years ago, and also more willing to share it - as long as they get something in return. Therefore the data available for HR and workforce analysis now is vastly different to 5-10 years ago - you only have to look at the growth of Facebook and LinkedIn in that period to know that you have more opportunity to know more about your teams than ever before.

Here is another question then - the interests on someone's LinkedIn profile or Facebook page will give you a huge amount of insight into skills that you may not see on a day to day basis in their role, and may mean that you consider them for roles that they wouldn't normally be considered for, but is it ethically right to look at this page for the potential benefit of the candidate? Where do you draw the line?  It is potentially for their benefit, but does that make it right?

I heard of tools where members of staff could link their Facebook to a talent management tool within the business - the take up was very low. Let's be honest, you may not want your company seeing your personal photos and online conversations, even if it could mean more chance of an internal promotion!

Putting the onus on the employee seems to be the best course of action then. If they fill in their internal profile similar to how they would a Facebook or LinkedIn account, then you have all you need to be able to draw better analysis. They have a vested interest in the outcome, and as long as this is understood you should get pretty accurate data to use. However, building an internal platform to match the functionality of the likes of Facebook and LinkedIn can be costly and once again requires more data experts to analyze the new data it will generate so we are back to the ROI challenge.

In conclusion

I agree that HR analytics could and should have a direct impact on business profit, but just in the way that all new concepts need to; HR teams need to harness the skills of other analytical disciplines to achieve all that is possible to prevent falling further and further behind. It is also going to take companies being brave and setting the trends for the use of this data to show what is possible before others will follow.

As someone put it when discussing how companies improve the potential and usage of analysis in HR and workforce planning - we should talk about it a lot in conferences and meetings to share as much as we can to make sure that we all learn as much as possible from each other, but we don't want to share too much so that nobody can do it better than us…!

At the end of the day, being better at anything within the sphere of analytics and data gives you competitive advantage and you don’t want to lose that.
 
By David Farmer - Partner, Harnham

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Sean Byrnes, CEO Of Outlier.Ai, On Creating A Business With Values

Sean Byrnes, CEO Of Outlier.Ai, On Creating A Business With Values

We sat down with Sean Byrnes, CEO of Outlier, an Analytics solution provider, to learn more about Outlier and its values. He also shared us with the power a candidate has when applying and interviewing for jobs in the tech industry today as well as how employers can retain their top talent. How long has Outlier been in business? We’ve been in business about four years. Prior to starting Outlier, I had another company called Flurry which we sold in 2014. When we sold it, I took a year off to reset my internal counter. I needed to reorient my work/life balance and then, when I felt I was in good shape to get back into things, we kicked off the launch of Outlier.  When you began Outlier, did you plan to map out your values of diversity, inclusion, transparency, and work/life balance? Or did it evolve as the company grew? Planning out our values is one of the first things my co-founder, Mike Kim, did when we were starting the company. It was intentional. We sat down and wrote down those values you see on the website.   I learned a lot in my previous company and there were a lot of things I did which followed the values we have which we didn’t follow intentionally. It just felt…right. Growing up in New York, a very diverse place, having a diverse team always felt more natural to me than a homogenous team. Add to that, having just spent a year with my new daughter and adjusting to being a parent and what that meant; it put things in perspective.  If there’s one thing I’ve learned it’s this. There’s no reward for running a company well. There’s no reward for following good practices and treating your employers with respect. In fact, the reason I started my first company was that I’d worked a lot of places that treated employers like resources or like widgets.  You put salary in one side and productivity comes out the other. I wanted to work somewhere that treated people like people. So, I started Flurry, and now have come into Outlier with a solid idea of our values and company culture. As important as tech skills are, there’s no data to support a feedback mechanism and predict success. Success comes from unexpected places.  So, by sitting down and writing out those values, we weren’t just signing up for a contract for how we would treat the people.  In our way, we were standing up as an example of how you can build a tech company that didn’t follow these bad habits.   Someone said, not too long ago, that the best employees stay in the same company for about 20-months before moving on to their next project. Often, it’s because the employee no longer feels creative.  In thinking through your company values statements, what would you say to a business who’s hoping to both attract and retain their top talent? Or do you think it’s better for these individuals to rotate off in order to keep things moving?  Tech companies typically have two problems: Companies are so desperate to keep going, they do whatever they can to survive. They promise themselves they’ll make compromises and fix the short cuts when the time is right.  So, they hire people who need not share their values or may not meet their criteria with a promise that when the company is successful, they will fix it and that time never comes. You’re never at a point where you’re so successful you can go back and remake those mistakes or fix them.For many, recruiting is a one-way system. You search for and hire a candidate, then another, and another without really putting much thought into it.  The reality is that you can’t build a high growth business that way. What you have to believe is that if you spend the time to find the great people that enjoy working on what you working on, where you treat them with respect, you give them not just responsibility but also the authority to do things that you create gravity. You create a world where the people you hire pull in the next group of people because people really want to work them, they want to be in that environment. A lot of our most recent hires here at Outlier are people that came to us, who wanted to work in this environment and when they saw how great our team are they want to add to that and it becomes a self-fulfilling cycle where the larger your core of great people the better your gravity is, the more great people it pulls in and so the gravity needs to expand and that’s how you build a high grow organization. You don’t build a high growth organization by having the best sourcing process, by having the largest recruiting team, the best employ onboarding. You grow the fastest if you can create a community and an environment people want to work in and when it becomes self-evident of that then you start to pull people in. And so those become the culmination.  How do the values that you’ve initiated affect your company? Have you found that those values foster a deeper loyalty and higher moral than in other places and other business that sound a little bit like yours?  We have a very low attrition rate. Yeah, people have left due to life changes, but otherwise we still have the same people we started with though their roles may have changed as we grew. There was nothing we set out to do, nothing on purpose to keep them, but just did things which felt right. In retrospect, it comes down to this. If you create an environment where people are learning, where they feel valued, and they enjoy the work, why would they leave? Where would you go? What could you prefer to that environment? I think by focusing on this early, it’s led to more employee retention.  My hope is that everybody’s who’s here will continue along the journey as we build the company together. Another example is our work/life balance. Both my co-founder and I are parents of young kids. My first few hires were parents of young kids. So, being family friendly has always been a core value of ours from the beginning.   Being family friendly is both a conceptual and a practical concept. Our policy is to be in the office three days per week and work from home two days per week. This gives our parents a chance to do things like take their kids to the doctor or attend a parent/teacher meeting. It’s become an enormously strong aspect for us because there’s a lot of people who have young kids and they don’t want to work in your stereotypical tech company. You know the one, the company who expects you to be in the office for twelve hours a day seven days a week.   Our employees like the challenge of building tech companies, but they need the flexibility we offer, too. So, just by having a simple stand of valuing people as people and parents as parents, means we have a competitive advantage in recruiting for roles. The senior people who can contribute at a very high level where they suddenly didn’t have any options and this is a chance for them to keep doing what they doing. They’re able to do what they love and not feel they have to compromise their family on its behalf.   It’s the kind of thing where very simple principles becomes an enormously strong competitive strong advantage in todays market. While it hasn’t been true for very long, it has definitely been the case so far for us. If you’re interested in Big Data and Analytics, we may have a role for you. Take a look at our latest opportunities or contact one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

MACHINE LEARNING ENTERS BIOINFORMATICS AND ITS FUTURE IS BRIGHT

Machine Learning Enters Bioinformatics and its Future is Bright

Ever wondered how your email system knows which emails to show you and which to put in your junk or spam folder? Enter Machine Learning. It learns what you open and read and after a time can differentiate what you ignore, toss, or move to spam. Now imagine that same type of learning in the life sciences. As scientific advances move toward Data and Machine Learning to scale their knowledge, you can imagine the possibilities. After all, as you read this, trends in the life sciences, specifically with an eye toward bioinformatics showcase machine learning such as genome sequencing and the evolutionary of tree structures. Human and Machine Learning with a Common Goal There has been so much data provided over the past few decades, no mere mortal could possibly collect and analyze it all. It is beyond the ability of human researchers to effectively examine and process such massive amounts of information without a computer’s help.  So, machines must learn the algorithms and they do so in any number of ways. For the most part, it’s a comparison of what we know, or is already in a databank, with the information we have and don’t yet know. Unrecognized genes are identified by machines taught their function. The Future is Bright Machine Learning is giving other fields within the life sciences both roots and wings.  Imagine scientists being able to gain insight and learn from early detection predictions. This type of knowledge is already in play using neuroimaging techniques for CT and MRI capabilities. This is useful on a number of levels, not the least of which is in brain function; think Alzheimer’s Research, for example.  The hurdle? It isn’t the availability of such vast amounts of data, but the available computing resources. Add to that, humans will be the ones to check and counter-check validity which can in turn become more time-consuming and labor intensive than the computer’s original analysis. And it’s this hurdle which leads to a caveat emptor, or “buyer beware” of sorts. Caveat Emptor: Continue to Question Your Predictions In other words, how much can you trust the discoveries made using Machine Learning techniques in bioinformatics? The answer? Never assume. Always double check. Verify. But as you do so, know this. Work is already in progress for next-generation systems which can assess their own work.  Some discoveries cannot be reproduced. Why? Sometimes it’s more about asking the right question. Currently, a machine might look at two different clusters of data and see that they’re completely different. Rather than state the differences, we’re still working on a system that has the machine asking a different kind of question. You might think of it as a more human question that goes a bit deeper.  Imagine a machine that might say something noting the fact that some of the data is grouped together, but if different, it might say while it sees similarities, but am uncertain about these other groups of data. They’re not quite the same, but they’re close.  Machine Learning is intended to learn from itself, from its users, and from its predictions. Though a branch of statistics and computer science, it isn’t held to following explicit instructions. Like humans, it learns from data albeit at a much faster rate of speed. And its possibilities are only getting started. Want to see where Bioinformatics can take your career? We may have a role for you. If you’re interested in Big Data and Analytics, take a look at our our current vacancies or contact one of our recruitment consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

DATA SCIENTISTS MOVE THE NEEDLE TO SCALE BUSINESS

Data Scientists Move the Needle to Scale Business

Today’s companies know how important it is to add Machine Learning and AI into their business, but without a plan, things can easily go sideways. Hiring Data Scientists for your business involves more than just hoping for the next Facebook or Google success. It’s about moving the needle on your own business. So, how do you do that?  Well, first you’ll want to think about what is you want from your business and from there, build a team to help get you there. We know that sometimes these two things pose challenges as well. So, where should you should begin? Ensure you have the right leadership structures in place In their role as Stakeholder, it’s important for top executives to have a clear understanding of where their business is and solid framework for where they want it to go. In other words, it’s important to ask yourself, is your business ready for that sort of growth and transition? Before you say, yes, and start looking for that unicorn employee, there’s a few things you need to know. Data Scientists aren’t magicians. They cannot wave a magic wand and make you ten times profitable overnight. Efficiency will come, but it will take time. In reality, if you don’t have the right Data sets, the right people in place, or the right backing and investment, then it will be a hard road to success and can lead to failed initiatives. So, how do you avoid that and get yourself set back on the right path? Here are a few key steps to consider: Don’t Put the Cart Before the Horse Understand your focus and the why of your business. Align your teams with a clear view of where you are and where you’d like to go in your business. Set clear expectations for yourself and your team.Decide What Your Team Should Look Like Ask yourself, “How much talent do I need?” Well, that depends on how much Data you’ll be working with and where you want those initiatives directed. For example, if you’re building a team just to focus on work recommendation systems, then you’ll need a far smaller team than if you were overhauling an entire platform or product line. Stop Chasing Shiny Objects Be realistic in your expectations as you build your team. So many businesses, when they think of Data Scientist, focus on the word scientist. And their first thought is they’d like to get someone with a PhD from Stanford who’s worked ten years at Google. A couple of things come to mind here, when I hear this and the first is this: When businesses first reach out, they talk to me about how they want someone from Google or Facebook or Netflix, but the reality is 9 times out of 10, you’re not going to be able to access that kind of talent.Be realistic about what sort of talent you can gain access to and ask yourself, if you could get someone from Google, Facebook, or Netflix, why would they leave that job to come work for you? What can you offer that those businesses cannot? It's important to understand the goal here is not to chase the shiny stereotypes, but to have clarity and desire to set up for success those that you do hire. For some businesses, the initial reaction is to just throw Data Scientists at a problem and believe they can fix things or move you forward faster. But like everything you need to put steps in place to get you from where you are now to where you want to be.  Learn, Grow, Pivot For most people who don’t come from a Data Scientist background, there are two schools of thought. Data Scientists are bright shiny objects who will fix all of their business problems overnight orA waste of time. To build a successful team, begin by educating those in the dark about what Data professionals are capable of and ensure everyone is aware of what is realistic. It’s important to understand that it may take six months to a year for a business to see any real outcomes. This doesn’t mean things aren’t working. It’s about investing time and not getting itchy, if something doesn’t instantly showcase results.  Rethinking Stability The gold watch after 40 years of working for the same company is a thing of the past. Good Data Scientists today, typically stay on for about 20-months, then move on to their next creative endeavor. This can be scary for a world that expects people to stay in positions for four or more years, particularly if you’re not from a tech background. However, the definition of a scientist is someone who researchers, someone who tries to find new ideas and new concepts, so these are people who are naturally inclined towards learning and towards being in new situations and these people get very, very bored very quickly. Understand that if you build your team well, people will move on and drop away. This is a good thing. It means you built a strong team and the ones who have moved on have got you to this place, now you need the next team to help you get to the next level. A constant state of flux is scary, but it can also mean your business is scaling faster, and your team of Data professionals are doing their job to move the needle. Give your team the freedom to learn, give the freedom to work on projects outside of their natural scope to be able to bring value to your business, although even within that year 18-month framework you might not see proof straight away. You need to be ok with the fact that you going to lose people. That typically means you’re doing something good.  If you’ve got a Data Scientist who is happy to just stay at your company and work on the same projects for six, seven, eight years, that’s probably a red flag; how will they keep improving? “What Got Us Here, Won’t Get Us There” The needs of your group are going to change and shift and so the people you need are going to change and shift. Again, it’s about being adaptable and being able to ride those waves and change as and when we need to. If you’re looking for more guidance in scaling your business using Data Scientists and building your Data team, Harnham can help. If you’re a candidate interested in Big Data & Analytics, we may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

Web Analytics Trends

Web Analytics Experts On The Year’s Biggest Trends

Size. Scale. Strategy. Metrics. The measurement of the customer experience is inherent throughout our marketing and advertising efforts in today’s world. So, we thought we’d take a step back and ask Data professionals about their thoughts on current and upcoming trends in the realm of Web Analytics.Their answers were as wide and varied as the people who gave them. But to a one, these are all in executive leadership and are exclusively focused on Web Analytics and its effects on consumer behavior. Personalization Gets More Personal  "With IoT, today service providers have a huge amount of data about their consumers. In Web Analytics, this has led to analysis of individual customer behavior, which is enabling companies to offer personalized services to their customers.  Therefore, on-page analysis has become popular because the aim here is to convert the visitor to a purchaser when the opportunity presents itself. With voice search on the rise, this has led to a push towards analyzing voices- so as to understand emotions and identify points of frustration which lead to abandoned carts."  - Avinash Chandra, Founder and CEO at BrandLoom  Privacy Focus  “Many people are concerned about how the likes of Google and Facebook handle the analytics data they collect. Google Analytics has been the default choice for most tech teams for years now, but it's being installed on fewer and fewer sites nowadays. Instead of handing your website's visitors' data to Google, many young companies want to be in control of their visitors' data.”  - Uku Tehrat, CEO at Plausible Insights Reading the Tea Leaves of Web Analytics “Access to deep information has a drawback; it’s made us reliant on the instantaneous raw data from each analytics channel to inform strategy. However, that data isn’t always accurate. To solve the problem, marketers and analysts will need to take a more holistic view of the data.  Instead of, for example, tracking revenue by channel solely in analytics, the analytics professional will be looking at the real revenue of the business and evaluating the changes in the marketing that have led to that result. By looking at the real business KPIs and creating narratives that reflect the whole of the data, an analyst will be able to avoid the short-term thinking that comes from the constant analysis of daily analytics reports.” - Doug R Thomas, Marketing Consultant at Magniventris  The Third Wave of Business Intelligence  Sean Byrnes, the CEO of Outlier, an analytics solution provider, is seeing an interesting shift in Data Analytics. This shift has us entering the third wave of Business Intelligence (BI).  The first wave, data centralization, aggregated business data in a single place, making it easy to know where to look for answers. Next, data visualization tools made extracting answers easy and accessible, allowing anyone in the organization to make use of them.  The third wave is Automated Analysis. This wave will have a big impact on data scientists and how they do their jobs. In this third wave of BI innovation, automated analysis systems will constantly examine all of a business' data and provide "curated" insights related to specific and actionable changes in the business. This is vastly different than today's dashboards, giving data scientists specific, daily direction on which parts of the business to focus on. This also helps data scientists elevate their own brand to one of a data counselor. Helping define how to use data insights to fine-tune the business.  - Sean Byrnes, CEO at Outlier. Video Marketing is Here to Stay “From a marketing perspective, the biggest trend I see moving forward is the continual rise of video consumption.  Consumers are shifting from reading blogposts to watching YouTube videos, and from reading books to watching Netflix. We know that the currency of the online world is engagement. This means that whichever large company, small business, or individual can engage their audience most effectively, wins.  When it comes to web analytics, it is important to identify the metrics that signal strong user engagement and to work to improve upon the mover time. Some of these metrics include watch time, average view duration, re-watches, returning viewers, etc. The better you can get these metrics, the more engaged your audience will be.” - Jeremy Lawlor, Co-Founder & Chief Strategist at Active Business Growth AI-Enabled Data Interpretation  “AI is constantly being mentioned as an enhanced way to interpret and help visualize data, and we are seeing various tools that promise to do that. I also see a lot of potential in using Web Analytics to predict consumer behavior and have better forecasts of performance. In general, we will have better ways to decipher them in order to better serve our needs in understanding the historical data and identifying trends and risks.”  - Gabriel Shaoolian, Founder and Executive Director at DesignRush A Three-Pronged Shift of Integration and Visualization Data connection web + mobile + offline (data integration)  DataViz development (data visualization) Development of the data-driven business model  "This shift towards a more integrated and data-driven approach should be important to business. After all, it’s the business model not just of the future, but now." - Krzysztof Surowiecki, Managing Partner at Hexe Data, a Data Analytics company A Growth Spurt   “Web Analytics should expect a growth spurt in the next decade. Technology has gotten so complicated, we need people – senior level people – with experience to understand and distill data to the business. Even something as simple as Salesforce, needs experts. Jobs aren’t going anywhere.  Businesses are desperate for educated individuals to help them make sense of all the data and filter out that which “fake” (read: bot traffic) in order to really understand the numbers and what that means for their business.”  - Daniel Levine, trends expert and keynote speaker at DanielLevine.  Web Analytics has been backstage long enough. As businesses aim to strike a balance between the data measurement of the customer journey, ensuring customers’ data privacy, and tailoring each experience, there’s plenty of work here to go around. And with the estimated growth spurt in the next decade, now’s the time to jump in. If you’re interested in Web Analytics, we may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to learn more. For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com. 

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