I recently read an interesting article discussing the results of PWC’s 22nd Annual Global CEO Survey. Not shockingly to many, even with all of the data that we have access to these days, leadership teams are suffering from gaps that hinder them from being able to do what they need to move the needle. With operational efficiencies being highlighted as a key strategy and the lack of actionable intelligence also noted as having not changed in several years (across areas including customers and employees needs), it reminded me of why I even started my company in the first place — to measure any goal in a better way.

Many years ago, I was handed a very big report that was called “The Voice of the Workforce” or “VOW” as we called it. It was several questions, the results of which showed us the magnitude of how much employees agreed or disagreed with a variety of statements with respect to their experience. The goal was to increase employee satisfaction. While these types of insights highlighted areas that we needed to focus on, there were clear gaps. A lot more effort was required to unpack the results before we could decide on which actions to take and to understand the level of prioritization based on what would make the most impact to our employees. We needed to spend yet more time conducting focus groups and deep dives before we could actually act. Worse still, some teams moved straight into action. While this sounds great, because the data had the aforementioned gaps, the decisions were often reactive and based on assumptions. My background was in A.I., with a love for data and algorithms, so my first reaction was — there simply has to be a better way.

Sadly, over years that followed, year on year, I saw the same issues arise over and over again. A number of years ago, you may remember that Customer Experience became the word of the day. While many already knew the importance of experience and used design thinking in their efforts, it became somewhat of an industry buzzword. As with all buzzwords, out came the plethora of frameworks, metrics, and job titles. Then, as reported by Forrester, CX became stagnant. No shock there, as you can often take care of the low hanging fruit, for example, deciding to implement that one thing that customers have been complaining about for ages. However, while this may elevate your customer experience scores for a moment, it is often fleeting and it doesn’t mean that you are operating with customer experience at your core.

The years that followed also saw the “data hype”. People started collecting any and all data, then creating pretty dashboards to give them “business intelligence”. Yet, when we asked clients about their organizational challenges in their transformation efforts, words like “reactive” were still coming up. This stemmed from the fact that while there was a lot of data, people didn’t know what to do with it, how to work with it, or were led down a rabbit hole. While you can have all the technology, you still “need to know what to look for in the first place and how to connect one piece of information to the other”.

Seeing all of this led me to apply a range of techniques to create a methodology called success modeling. The point was to understand the conditions and ecosystem for a goal to most naturally occur, with a focus on the actionable metrics — the elemental ones. My mission was to purposefully use technology, data, and algorithms, to enhance human capabilities and expedite the pace of sustained change in achieving the desired outcome. Now, we apply it to the areas of customer experience, workplace culture, leadership development, and wellbeing, often focused on goals that seem nebulous or difficult to quantify such as productivity. From our learnings along the way, here are some things to think about when creating your data and insights strategy.

1: What to Measure

Your goal

Before starting to collect any and all data, you want to think about the reason you are collecting data in the first place. What is your higher-level goal? For example, we want to have a hassle-free check in process.

Your audiences

You also want to think about which data points may need to converge to discover meaningful insights. For example, do you understand all of your audiences that play a role in the scope of this goal? Including your customer, employees or even vendors. Who are they as people? How do they operate? What are their needs and challenges in achieving the goal? Connecting these types of data points, with a deeper understanding of all of your audiences, can help better discover the true root causes and limit costly assumptions.

The definitions

From here, you can move into thinking about your definitions. Start with your vision for your goal and define what you would need to know to see how well you are doing and take action. Using the hassle-free check in process, for example, this would be defining what a “hassle-free” check in process means tangibly. Then, a step people often miss, is validating if your definitions are accurate. Do they really indicate what you think they do? This validation step also limits costly assumptions and wasted effort from measuring the wrong thing.

The focus

Having all the data in one place is very useful, for seeing patterns you probably didn’t even know exist and for one source of the truth. However, depending on your role in the goal, and the sub-goals that you are currently trying to achieve, there are different measures you will want to zoom in on at a given time. As different data points can be used for different purposes.

For example, if your overall goal was to be healthy, there are many ways to measure this. You could look at your weight, your body measurements, how you feel, your energy levels, your stress levels etc. Each data point reveals something. Which combination you are interested in focusing on at a given moment, depends on what you are trying to achieve. For example, if you are trying to be less stressed, you would be more focused on physical indicators such as your heart rate, blood pressure, cortisol levels as well as mental and emotional signs such as trouble sleeping, mood, and appetite. If you were focused on losing weight, you may be more interested in your weight and body measurements. Now, this is not to say the data points are not related. This is the beauty of data views, you can use all the points to see the patterns and also focus in on the ones important to each specific purpose.

You can start filtering the data by thinking about what you want to know about your goal? What questions are you looking to answer by using the data? What would you need to measure to reveal actionable insights? Ask yourself, “if I had [this] data, I would [do this action]”. If you do not know what you would be able to do with the data, it is a good indicator that you may not have the right data point.

2: Reminders for Creating the System

Maximizing the value of investments and finding gaps

Often, there will be many insights and data sources already within your data ecosystem that can help you. Using your purpose and definitions, find out what you already have access to. No one wants to waste efforts capturing data that already exists.

When gathering your existing data sources, remember to keep validity front and center. Is the source painting an accurate picture? Which combinations will make the insights clear and actionable? Sometimes data sources will be direct from an audience, such as feedback surveys, and sometimes sources can be those captured by certain systems or processes, such as the time taken to check in via a mobile App. After looking at your existing data sources you will see your gaps, which can inform any other insight gathering activities that may need to take place.


Organizations love benchmarks. It is almost a human trait that we are conditioned to from young. We love to know how others are doing and where we fall in comparison. While benchmarking against others can provide insights, they may not help you prosper. Ask any successful athletes, take swimming for example. They don’t win by turning their head and getting distracted with seeing where the other competitors are, they win because they keep their eye on the end of that lane and focus on what they are doing and how they are performing to get them to that goal faster.

Think about it, if getting to 80% of some goal is a great place to be, but the average is 62% for those around you, and you score similar…that’s great. Collectively, you are all average and below the optimal.

While it is good to know how others are doing, remember, if you want to be great, don’t forget to also focus on the bigger picture and don’t settle for average.

Moving into cohesive action

You need to make it easy for people across the organization to act on the data. This includes repeating the thought process with a lens to each person’s role. What do they need to see from the data? Take meeting effectiveness for example, facilities may be more interested in utilization rates, IT more interested in the uptime of the tools and leadership may be interested in overall employee satisfaction and productivity. The data system in place needs the bigger picture, but each person’s level of zoom and view should filter for their needs.

Making it easier to move into action, includes having the underlying systems and structures in place to make decisions and feed it forward. For example, when is the data reviewed? Which decisions need to be made? Who needs to be present? What is your threshold or data points needed to make a go-no-go decision? For instance, the police often need a threshold of evidence before deciding to take action. Great data systems help decision makers see how strong the indicators are so that they can make the best decisions based on the data they have to hand (and some gut, experience, and instinct of course).

It is about accessing the right data, to increase human understanding and support making the best decisions. Ask yourself, how exactly is your data system making your humans faster, smarter, and better? Or are you drowning them in data, increasing their efforts as they need to make sense of the data, or even providing them the wrong data?

In Summary…

Figuring out your data strategy can seem overwhelming. However, by applying a systematic methodology, enabled by tools, and enacted by people whom you have empowered with the right skills, you can move into a proactive state where you are making the best decisions and designing purposefully to achieve the goal at hand.

  • Start with your purpose, goal and definitions of what this means tangibly.
  • Understand your audiences, what is important to them and their role in achieving the goal.
  • Understand which data points to focus in on, when, and for whom. Work backwards, asking yourself what you would do with the data if you had it.
  • Look at data sources you already have access to and uncover the gaps.
  • Focus on the big picture and don’t settle for average.
  • Ensure that you have the underlying structures and processes in place to remove barriers to action.