What makes people analytics such a powerful discipline and a domain that venture capitalists and startup founders can really leverage is that it gives them an anchor from an objective standpoint – in terms of where you currently are in the business, where you’re trying to go, how you interface with your customers and how your culture is developing.
The first step in analytics is to instil a culture of objectivity and data-driven decision-making as part of starting your organization. If done consistently, objective decision-making will get fully embedded into your culture as you scale as an organization.
Whether you have a risk management decision, a culture-based decision, if you’re trying to scale your brand, if you want to understand how a certain acquisition or a certain growth target will fit into your culture, this is going to ensure that such decisions won’t be based purely on subjective experience. This will include a data set, which will allow founders and HR leaders. I will walk you through what the people analytics value chain is and how it would benefit your startup.
I’m David Swanagon, a passionate data scientist, executive coach and HR leader. I am currently serving as the Head of People Analytics at Ericsson (North America) and have over a decade of experience in Analytics.
It could be because I’m passionate about the subject but I believe it makes sense to introduce the analytics function even if you are the only founder in your sole proprietorship and you’re trying to obtain funding.
The technologies (fairly inexpensive) that are available today that help you become more efficient, connect with people, organize your work, ensure that you’re operationally efficient from a tactical perspective and also, with executing your customer strategy.
Initially, you would want to figure out a way to store your data. For instance, if you’re a founder, you likely have a business plan and a checking account. You can create a simple data set from these by using something like Microsoft Access.
As a startup founder, you could develop a baseline set of metrics, create simple visualizations and then as you scale, you can add layers of sophistication to match your business plan. In the initial stages, it’s probably best to outsource your analytics function. You could use a baseline tool that connects to your finances to show how you’re using your cash flow and perhaps, to your LinkedIn profile, which will help you understand how you’re connecting with your customers.
As you scale, you can start doing more custom visualizations and eventually, if you’re big enough, you can integrate machine learning. So it starts with one employee and the sophistication increases as you scale.
If we go through a life cycle of a business, what’s important to understand is whether you’re a fully mature operation or you’re just starting, the people analytics value chain is the same. What differentiates the embedding of the analytics function at various stages of your business is how you’re activating it, how you’re moving it through a sequence of activities and what type of technologies you use.
Initially, the analytics chain starts with workforce planning and moves into diversity and inclusiveness. D&I has gained increased significance recently but with most companies, it seems to sit on the periphery of the core strategy whereas it should form the building blocks of your culture.
You walk through this full chain starting from talent acquisition to learning and development, closing the loop with performance management. Interestingly, most leaders start with performance management. They want to ensure their employees are productive and they tend to lead from a task-driven perspective where you know how much your employees are working versus how much you’re paying them. However, that’s the last step in the process.
The focus should be on making sure you have the right skills, that you’ve embedded the right culture and you’re developing your employees and engaging them. From a founder perspective, the initial step would be to create the technology platforms that allow you to have that base set of analytics, which can be scaled at a later stage.
This is one of the first steps a founder needs to take – understanding the downside risks their organization faces at any stage. These could include things like professional brand reputation, where you potentially have a cash flow exposure, legal and regulatory in the markets you are operating in or looking to enter.
You can establish a governance model around your risk management and the visualizations can be done through a simple one-page dashboard. Imagine you’re in the venture capital phase – you have the initial seed funding, you’re developing a demo and you’re trying to understand how to connect with emerging markets.
Typically, what ends up happening is you’re so focused on growth that you’re not paying attention to the regulatory framework or the potential risks associated with the partners you’re dealing with.
As a founder, it’s like your company is growing really fast and you’re trying to prove to your investors that your idea is viable and also improving your market share. As you scale and continue saying yes to vendors and customers to drive your topline, you might end up with a downside regulatory problem if you haven’t properly vetted your vendors or haven’t ensured that your business practices are compliant with the local laws of markets you have entered during your rapid scaling journey.
Uber is a classic example of this scenario. It has been dealing with this issue of – should it classify its drivers as its employees? They’re playing catch up from a strategic standpoint because they actually never did the analysis of the challenges and risks related to the big question: should their drivers be classified potentially as employees?
When companies scale, there are some common risks that they typically run into. The first risk, which is actually counterintuitive, is that your company is successful. Most startups reach a place where even if the idea is viable, for whatever reason, they’re not able to connect with the customers.
There are barriers to entry that limit their ability to successfully create a brand for themselves, anchor their company and grow. When they achieve initial success, what ends up happening as the first step is your supply chain typically cannot keep up with the success, even if it’s a digital company. They have to scale up their supply chain, which lead to significant strains on cash flows
By establishing analytics in the background when you’re a founder in the early stages, you would be able to tell your concerned stakeholders, especially your investors, the kind of scale challenges you’re facing with respect to your supply chain. This assumes importance especially when your customers move from the trial to the adoption phase and it’s going to hurt your company if you’re not able to ship the products to them. Or, from the point of view of additional infrastructure, it could be that you don’t have enough staff to handle the scale.
Second, founders tend to focus on the strategy of their business and they might think things such as cybersecurity sit far on the periphery and that they don’t matter. However, cybersecurity and the posture you take on protecting data (especially your customer data) is something you have to do once your company’s successful. If companies don’t manage their cyber posture correctly, this affects their brand and is one of the easiest ways to lose momentum.
The third common risk is scaling your employees and as they increase in size, you have to ensure the culture that’s developing organically is the type of culture you’re looking for. This comes back to diversity and inclusiveness but also, the values of the business. You need to develop your employees to ensure that the values of the business and the culture of the business align with the founders’ vision.
Data has a way of making a complex issue even more complicated and most companies continue to be challenged with this, irrespective of their size. I think engineers have this challenge: once you fall in love with numbers and you have a passion for mathematics, you can’t help yourself. So, you end up making a very complicated analysis. I mean, you might do something like a principal component analysis that is statistically robust and beautiful to you but nobody understands it.
As you layer up complexity in the business, what ends up happening is data fatigue where people declare they don’t understand the numbers and that they want to go with their subjective experience or intuition. When this happens, it’s difficult for the Chief People Officer to recapture the credibility around analytics.
Therefore, the most important thing for companies to do is keep it simple. You need to understand what the core objectives of the company are, what the overall strategy of the business is and how your company makes money. Based on these, you need to figure out what those people enablers are that would help you execute your business. There are usually around 3-5 such things that the people function contributes to enabling the overall business strategy and fulfilling its core objectives.
From those 3-4 people enablers, you need to create metrics that are easy to understand and easy to communicate to frontline leaders. Following this, you need to create a consensus around the business goals in terms of where the company is at currently, where it plans to go and all the steps in between that would help the company to grow.
The CEO can create a call-to-action where everyone in the company understands they need to drive these 3-5 things. Create a simple dashboard for everyone to understand those metrics. As you see progress, you can scale your operations and make them more sophisticated. The last thing you want to do is use advanced statistics in the beginning because it will just scare everybody away.
The first step in establishing an analytics function is the governance process. Sometimes, people start with the technology and in that, with the metrics. Or, they’ll probably assign it to the person best at mathematics and ask them to generate some reports for the company. Even though that might tactically be efficient, it doesn’t allow the function to scale as the business grows.
So, it makes sense that you treat analytics the same way you would treat any activity in your company that you’re planning to grow over time. That starts with creating a governance function and the first thing is to establish an analytics committee championed by the founders and chaired by either the CEO of the Chief People Officer.
When you’re a smaller company with just a handful of employees, it might seem a bit funny to have an analytics committee meet once a month to evaluate the metrics you’re tracking. However, this committee framework ensures the CEO has visibility into the analytical models and ensures they match the vision and mission of the company.
One major problem with organizations is that they rely too much on complex algorithms, purchase a lot of tools, create too many metrics without a governance model in place. If the founder or the chief people officer is not championing analytics and it’s not serving as an executive steer, then you end up with a bunch of fancy reports.
In such a scenario, the operations team doesn’t really understand how it’s connected with their day-to-day functions and it doesn’t feel accountable for the metrics. On top of it, if the dashboard is too complicated, it results in arguments between the analytics and operations teams. Surprisingly, even some multinational companies take this approach.
Ultimately, data must be used for something. It’s not an end in and of itself. With the governance process in place, you need to ask – “How are we going to use the data in business?” That’s why it’s important to make sure that operational leaders are part of this team, part of the governance framework and they help develop the metrics with you.
If you co-create an approach where the individuals closest to the customer have some inputs in terms of which metrics should the organization be accountable for, then they’re actually going to stop arguing with you about the calculation.
Though it’s situational, founders should ideally hire individuals they can work well with and who reflect the values of their organizations. I am biased but I think technical credibility is really important for chief people officers or for their teams to have a seat at the table in a company, no matter the size of the organization. It’s better to focus on hiring people that have strong analytical minds.
How to use analytics for people’s function is something that can be groomed. But what can’t be groomed is someone’s mind and how they actually approach problem solving. From an HR staffing perspective, the founders’ focus is usually on soft skills and they tend to focus on someone who has an extroverted personality that can engage with employees and customers, is event-driven and someone who can develop a positive culture.
These are important, of course. But, once your company faces a key inflection point where you have a difficult decision such as capital investment or which market to enter or if you want to acquire a smaller company and integrate it into your business, you’re going to need an HR leader that’s deeply analytical, that can actually solve problems and understand strategy and how people’s function can enable that strategy or disable it.
My recommendation is for the CEO to hire someone that is more technical than behavioral at first. As long as they have good values and they’re able to communicate, I think it’s easier to train soft skills than it is to hire someone with strong analytical skills. The more an HR leader can take part in a supply chain/ finance/ marketing or PR conversation, a founder is going to get more out of them than if it’s an individual that’s good at employee events and engagement-related activities.
Typically, there’s a lot of confidentiality around the financials, employee productivity, employee networks, how individuals are connected – this could happen organically or the result of a leadership gap. A lot of CEOs prefer to keep a tight control over their data sets, which include customer information or customer cohort information on which segment is working well and which is not. This hurts more than it helps.
If the data analytics governance committee is run well, you would have a data dictionary in place that shows you all the business-related data and one of the discussions that it would have continually is which data points are available for employees and at what level. Once you have that framework in place where you understand how you’re delegating and empowering your staff to review certain metrics, then you can find that sweet spot in terms of what we should protect and keep confidential and what we should democratize.
I think you should democratize data, especially in a startup, as it leads to buy-in from your employees who usually join a startup because they believe the idea will take off but they also recognize the risk that it might completely fail. One way for the founders to build trust with their initial team is by saying – “Here are the numbers, here are the challenges with our cash flow and customer segments and here’s where we are doing well”.
When you do that, the employees feel like they’re part of this initial battle to succeed and launch the product. But if you keep a confidential space around your core metrics, then people end up feeling like just employees. Sometimes, founders think if they share their problems with their colleagues, it undermines their credibility as leaders and want to figure it out all by themselves. However, the reverse is true – by demonstrating the problems and articulating a vision, employees are likely to respect you more and are probably going to want to dive into the problems more.
As a company gets bigger, there’s a sweet spot to it. Obviously, you need to make decisions around what pieces of information should be protected. I think it’s erring on the side of democratizing your data set, it’s going to lead to more innovation and making the employees more committed to finding solutions to problems versus the CEO wanting to do it all by themselves.
People Analytics is one of the most important functions in the company. If the Chief People Officer has the right analytical skill, a good communicator and is not shy of pushing their way into the business, then they should be touching every single conversation. Everything in the business does fall under people analytics.
A CFO or a CIO might scoff at this and say their departments belong to them. However, it comes down to the Chief People Officer’s credibility. If they’re a really strong technical person and the models they’re developing are statistically sound and the visualizations are clear and easy to understand, and they have the self-confidence to interject in every conversation respectfully and humbly, then they would be able to establish that all functions comes under the purview of people analytics.
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