Embracing Lean Data Practices: Minimizing Waste and Optimizing Operations

Navigating the Data Deluge with Precision, Velocity and Responsibility.

For years now, organizations have been building a data swamp: a situation where there is a large amount of unorganized, unstructured, and low-quality data where only a few percent is actually used. Every click creates a digital footprint and data is continuously flowing. Should we continue messing around with data or adopt "lean data practices"? This concept, which I have been advocating since I started working in Corporate Finance, aims to revolutionize our data interaction and management. The essence of lean data practices lies in its simplicity: pull only the data you need, use it until you don't need it, then discard it. By adopting this approach, individuals and organizations can significantly reduce data waste and optimize their operations.

The Challenge of Data and Cognitive Overload

The digital age has given us easy access to a large amount of data, following the advent of mass production, and mass consumption of goods of services. However, like in the physical world, this convenience comes at a cost—data overload. With vast quantities of information available, it can be difficult to resist the urge to store excessive amounts of data “just in case we need it”. This tendency to accumulate data from the belief that more data equates to better insights and more money down the line is far from being true. The truth is this approach raises serious concerns about data operations sustainability, data privacy, and security. On top of that, we often see data producers spending time and money on data pipelines without understanding the underlying business needs, leading to data consumers being dissatisfied with the service. This leads to cognitive overload on both sides which is never good for business.

The Lean Data Philosophy

The lean data philosophy encourages a shift in perspective—from data stockpiling to data optimization. Instead of collecting every piece of information available, practitioners of lean data practices focus on gathering only the data necessary to achieve specific business goals. This involves identifying the key metrics, variables, or insights required to make informed decisions or draw meaningful conclusions.

Lean data practices are not about sacrificing depth for the sake of efficiency. Rather, they emphasize the importance of precision and relevance. The idea is to build a straight line between the data and the business problem and then let a technical infrastructure emerge. Individuals and organizations can streamline their data collection efforts and avoid flooding in irrelevant information by grinding in on the specific data points that directly contribute to an objective. In other words, how to make more money, reduce costs, or both. Sounds simple? I wish it was too.

Maximizing Efficiency, Minimizing Waste

At the core of lean data practices lies the idea of minimizing waste. And this is probably the hardest thing to do. Even if everybody knows that energy consumption and resource management are critical concerns, it's imperative that we extend this mindset to our data operations. We can reduce not only the environmental impact associated with data storage and processing but also by looking at problems differently, searching for economies of scale, and compounding interests across data projects and departments.

Lean data practices also mean delivering any kind of data analysis more efficiently. It should not take weeks to get a dashboard done, and if it does, a spreadsheet sent on the same day of the request might do the job better. The risk of overlooking crucial insights when you spend time dealing with an ever-growing stack of tools to manipulate even-growing mountains of data is huge. By focusing on delivering fast on very specific subsets of data that the business understands, analysts can focus their attention on the most pertinent information, leading to more accurate and actionable results and decision-making at the executive level.

Use Case: Implementing Lean Data Practices

Embracing lean data practices involves a series of mindful steps. It starts with setting clear objectives for data collection and analysis, at a strategic level. Define the specific goals you aim to achieve and identify the data points essential for meeting those objectives. Avoid the temptation to collect data "just in case," and prioritize quality over quantity.

At Naas, we use a very simple Google Spreadsheet we call it the BPP for “Business Plan & Performance” where we break down month by month all the metrics we want to follow in rows, including performance ratios. Each month has 3 columns associated: Actual, Target, and Variation.

Next, you should be mindful of establishing a system for data retention and disposal. Data should be held only for the duration required to achieve the intended purpose. Once the data has served its purpose, dispose of it securely. For us, it means pushing the data automatically, daily, at an aggregated level from the source system to a specific sheet and creating simple INDEX/MATCH formulas to feed the main sheet where all the indicators are gathered in a P&L-like approach, from awareness to cash. Once we no longer need it, we delete the sheet and the lines associated with it.

Finally, constant evaluation is essential. Regularly assess the effectiveness of your lean data practices. Are you achieving your objectives with the minimal data you're collecting? Are there ways to further optimize your data operations? Adapt and refine your approach based on these reflections. Sometimes, it means getting more data, sometimes less, or from another angle. We have been following only 2, or 3 metrics for a while and now, as we are launching Naas v2, we are slowly adopting the “AARRR Pirate Metrics framework” to gain more insights into a set of five user-behavior metrics that product-led growth businesses should be tracking: acquisition, activation, retention, referral, and revenue. We have now 30 metrics, but I hope we can reduce them to 10 very soon.

Final Thoughts

In an era of data proliferation, embracing lean data practices is a strategic and responsible move. By pulling only the data we need, using it effectively, and discarding it when its utility ends, reducing the number of tools we use (I will share more about that soon) we can minimize data waste, optimize our data operations, increase insights distribution velocity, and contribute to a more sustainable and ethically-driven data ecosystem. The path to a smarter, more efficient data future starts with the lean data philosophy—a philosophy that champions precision, relevance, and responsible data handling.

I hope you enjoyed this piece!

Feel free to contact me if you want to implement a simple BPP spreadsheet like we are currently doing, I’m happy to share tips and tricks during a short 15min meeting, or more if needed.