March 21, 2022

Data-Driven from Day One: How to nudge your team towards a data mindset

If you aspire to build a data-driven company, stop thinking 'processes' and start thinking 'ecosystem.'

Square Peg Team

May 3, 2021

Data-Driven from Day One: How to nudge your team towards a data mindset

If you aspire to build a data-driven company, stop thinking 'processes' and start thinking 'ecosystem.'

Square Peg Team

If you aspire to build a data-driven company, stop thinking 'processes' and start thinking 'ecosystem.'

I have spent most of my time working along the intersection of data and decision making, at roles in Unit 8200, Google and Riskified. I had the chance to work with data-savvy teams and teams that don't know what data they have at their disposal. I learned that the difference between those teams was not necessarily the access to data or ability to analyze data; it was the relationship they had with data.

While the data-naive teams viewed data analysis as a separate being that belonged to other teams, the data-savvy teams had a sense of ownership over their data and saw data as a necessary part of their daily work, regardless of their role.

Making data an integral part of your everyday decision-making starts with your mindset.

The difference between processes and ecosystem is essential to understand if you are to lay the foundations of a data-driven organization:

  • Processes are series of actions with a discrete goal that has a starting point and an ending point.
  • An ecosystem is a complex and interconnected network of different processes that constantly evolve.

In my view, looking at data-informed and data-driven decision making starts with understanding that data is working within an ecosystem versus working in processes; it is deeply intertwined with all areas of your organization, and it is constantly evolving.

If you recently started a company, you probably feel immense pressure to be making intelligent, data-driven decisions but have too much on your plate to feel like you're on top of this. This article won't shame you into implementing ambitious practices on how to build a data-driven organization. Instead (hopefully), it will give you a fresh way of looking at what it means to be in a data-driven environment and suggest minor changes of attitude that can compound into a great deal of difference later down the road.

Start the Oxygen Flowing now

#1 Set the right attitude - make data-informed decisions the rule and not the exception by building the relationship between decision making and data from the very start

A data-driven mindset is a mindset that is naturally inclined to base decisions on facts rather than intuition through critical thinking and data literacy.

At an early stage of a company, there is limited data, and everyone is pretty much hands-on and able to sense the business environment first hand. This approach works initially for many startups, but trusting only on "gut feeling" can become a dangerous habit as they grow and the stakes get higher.

Even when there is not much data, you can build the right attitude to data, through awareness of data. Data awareness is knowing what data exists, what data does not exist, what data limitations exist, and what analysis possibilities exist at a given point in time. This 'data awareness' is independent of what analytics resource you have right now. Data awareness is important because it shifts the habit of intuition only and builds the foundations for a data-driven mindset later on.

When you're working towards a decision, ask what data is available to support it, even if there is not much yet–this will reinforce the habit of a structured analysis-driven process.

#2 It is never too early to start using data - start small grow with time

Just because you cannot measure everything does not mean you should measure nothing.

In today's world, data is constantly being collected, even at a very early stage. Start small, carefully pick what is relevant for your business to know right now. Initially, you could focus on product or website-related performance. Ideally, you will build towards ensuring that each department or team has the relevant metrics they need to make decisions. The important part to remember is that each stage has its most valuable information. When resources are limited, it is totally fine to be picky about what is measured, as you will still gain valuable and sometimes critical insights about your business.

Preserve Energy

#3 Stay Focused - consciously decide what your company is measuring and also what it is not

One thing that is always true about data is that there is more of it than you can handle. It is pretty easy to have data FOMO and be tempted to make as many analyses as possible; data abundance makes us feel we are missing something. The truth is that if we are clear on what the most important business goals are, there are very specific data sets we need to make informed decisions. At any given time, consciously choose what is crucial for you to know right now and draw the relevant metric. Don't hesitate to visit and change those on an ongoing basis.

#4 Communicate - Once you've decided what matters, make sure everyone is informed

Regardless of data, clear communication is important for an aligned organization. We have already established that data FOMO can waste your team's time and energy. Help your team focus by sharing with them what matters.

Embrace constant evolution

#5 Iterate - iterations are a healthy sign for learning based on insights, don't hesitate to iterate

When it comes to decision making based on data, iterating is an essential part of good data analysis. Iterating allows you to fix mistakes; it is the natural evolution of insights-based learning since each iteration provides something new that was learned and makes the analyses more accurate and refined. You can iterate on a single analysis level or on an organizational level by allowing the team to focus on what matters now while providing the flexibility to adjust as you go.

#6 Chaotic order - Insist on having one source of truth and don't ignore data discrepancies. Start with a dedicated data champion or simple documentation, and expand as the organization grows

In my experience, as data and teams grow, different teams within the same organization develop their preferences of data sources, calculations methods and models, meaning that data discrepancies are almost inevitable.

Data discrepancy is a big problem because it results in inconsistent reporting, poor decision-making and inter-department confusion. When a company is small, discrepancies are manageable, but gaps can become too hard to handle as the company grows. Don't ignore data discrepancies, have a standard for data alignment such as documentations of metrics, calculations and data sources as well as ownership of data mapping by the relevant team leads. In some cases, nominating a data champion at an early stage can help ensure data practices are aligned until there is a dedicated data or analytics team in place.

It's all intertwined

#7 Data is for everyone - data access is a cultural statement and not only a technical challenge - aspire to share data access when possible and provide all teams data literacy when they ask

One of the most significant differences between teams or companies with a data mindset and those without is whether staff view themselves as data owners or not. In many companies, teams hold the general notion that:

  • data analytics is too complicated, and can only people with rare technical skills can do it (magical)
  • data analytics is only used to make occasional executive decisions only (secretive)
  • data analytics is not available to view by everyone (belongs to a few). That is an unhealthy approach as it encourages segregation between the analytics mindset and the decision mindset.

It is obvious that some analyses are more complex than others, and some data needs to be carefully shared. However, founders must learn to manage this risk if they are to help their teams perform at their best. Data scientists and business analytics teams are valuable due to their core expertise in working with data and their ability to see the broader organizational picture.

However, data can lose much of its value and even be misleading without the proper context. Some of the most insightful tools and analyses I have ever seen were provided by the teams themselves and not necessarily from the dedicated analytics team. Ultimately, you'll only benefit by teaching your organization data literacy.

#8 It starts and ends with you - you should think of data culture like any other organizational culture you wish to install in your company, and as such, it starts at the top.

Take five minutes to consider how you can plant the seeds of a data-driven organisation now. Future you will thank you for it!

If you’d like to continue the conversation or have any feedback, I can be reached online or at

Good luck!