The nobrainer approach to better business decision-making for growth.
Data Strategy
Decision Making
Innovation
Human Understanding
A wide-vision data taxonomy.
Introduction: Data in the Modern Workplace
From the earliest developments in behavioral economics in the 1970’s and exploding into the scene with the Michael Lewis book (and later Brad Pitt-starring movie) Moneyball, the word has been spreading out that data is crucial to making better decisions. This shift has not only transformed industries like professional sports but also had a profound impact on healthcare, political strategy, technology, and business.
Today, most forward-thinking businesses lean on data to enhance performance, drive growth, and increase profitability. However, many decision-makers, especially those from traditional business backgrounds, were taught that financial data is the most crucial.
And while it is indeed valuable, it paints an incomplete picture.
Financial data is often seen as the most reliable because:
1. It is more concrete and precise than other data; money is either in the bank or it’s not, leaving little room for ambiguity.
2. It provides an objective, albeit cold, snapshot of business health.
However, financial data is backward-looking. It reflects value created long before the money enters the bank. While statements like Cash Flow, Profit & Loss, and Gross Margins are essential for understanding business health, they focus on what has already happened, not what lies ahead.
The money in these reports represents the result of a chain of actions—developing, marketing, selling, and delivering a product or service—all of which occurred in the past. This is why financial data is limited to reflecting past actions rather than predicting future outcomes.
Which is why I call financial data, backward-looking data.
Another type of data that is becoming more prevalent in companies big and small, is operational data, metrics that capture current activities. Often measured through Key Performance Indicators (KPIs) or other productivity metrics, operational data provides insights into what the company is doing right now.
That’s why I call it present-looking data.
While operational data is more uncertain than financial data, it serves as a powerful learning tool.
However, it also comes with challenges:
1. The business can’t be 100% sure that they are optimizing their operational data, So companies can’t always be sure they’re measuring the right operational metrics. As businesses evolve, their metrics may need to be adjusted.
2. Are we measuring the metric we intend to stimulate in the best way possible to get top results? KPI's should stimulate conversations and continuous learning. They are not definitive but dynamic indicators of performance.
By combining financial and operational data, companies gain a deeper understanding of their past and present, allowing for more informed decisions. Yet, for businesses seeking a competitive edge, this is not enough.
To truly elevate decision-making, companies must incorporate forward-looking data to the mix, as well.
What is forward-looking data?
You will notice that in all this data talk, there is a key element missing from this paradigm: PEOPLE.
As we say at nobrainer: “at the end of the day business is ultimately about human beings making judgments and decisions based on the needs and wants of other humans.”
Forward-looking data can be gathered from both external and internal sources within a company. The most valuable insights, those that truly drive value creation—come from data collected directly from your customers and employees. This type of data provides unique perspectives on specific business situations. However, because it involves human behavior, forward-looking data is inherently more complex and uncertain than financial or operational data.
Despite this complexity, the decisions driven by forward-looking data are where true value is unlocked, whether it’s entering a new market, making strategic moves, developing or enhancing products, or launching marketing campaigns. High-quality data in these uncertain scenarios helps reduce biases, guide decision-making, and improve the ability to navigate risk effectively.
This data can be derived through various methodologies in market and customer research, both quantitative and qualitative. It's important to evaluate this data carefully, understanding that different approaches have trade-offs in terms of accessibility, cost, time, and authenticity. What makes this data "forward-looking" is its focus on human behavior in relation to your specific business challenges, using it to fuel innovation.
As companies grow and evolve, so do their approaches to collecting, analyzing, and utilizing data. These approaches will naturally differ based on leadership style, risk tolerance, and decision-making sophistication.
Examples of forward-looking data
In our experience as market research professionals, we have identified roughly 3 types of commonly used and very valuable forward-looking data for business.
1. Deeper Customer Understanding: As companies grow, market conditions evolve, and while a company may still know who their customers are, they may no longer truly understand them. Data that offers deeper insights into customer behavior and motivations helps businesses develop and execute better strategies to reach them. This type of research leads to improved market segmentation, more targeted marketing campaigns, new product lines, and other customer-centric innovations. See how we do this with our Human Behavior Analysis.
2. Innovation and Creativity: Innovation is a complex, often messy process that can be influenced by internal politics and group biases. Collecting data throughout the innovation process enables teams to make more informed decisions when developing new ideas. Innovation is costly, so ensuring that valuable learnings are retained during the process is crucial. This type of data results in better decision-making around user experience, packaging, communication, and product features when launching new products.
3. Internal Data: Feedback collected from a company’s team is also incredibly valuable, particularly when gathered by an external party to ensure privacy and openness. This data can drive innovation and improvements in both company performance and employee satisfaction.
In summary, forward-looking data offers businesses the opportunity to make more informed, strategic decisions, particularly in uncertain and high-risk scenarios. By understanding human behavior and motivations, companies can better position themselves to create long-lasting value.
We will keep exploring this topic in our next article: Forward Looking Data Part II
