ResourcesBlog3 Methodologies to Making Data the Heartbeat of Your Business 

3 Methodologies to Making Data the Heartbeat of Your Business 


The more your business leverages data, the better equipped you are to operate efficiently, make informed decisions, and develop products and services that meet the needs of your customers. But how do you best leverage various types of data to your greatest advantage? Let’s discuss how a balanced approach to applying the various data methodologies enables optimal workflows and desired outcomes for Product Managers. 

Data-Driven, Data-Informed, or Data-Inspired? 

Data can be defined in the following ways:  

  • Qualitative data is descriptive and characterizing, often recorded in words, and typically gathered through methods such as empathy interviews, shadowing, focus groups, and thought-leadership articles. 
  • Quantitative data is counted or measured, often recorded in numbers, and materialized through statistics, product metrics, and financial records. 

Thought-leaders and their teams often work with different methodologies for using qualitative and quantitative data. While some take a “data-driven” approach, using collected data directly to make decisions, others take a “data-informed” approach, analyzing multiple data inputs in context, leveraging individuals’ experience and knowledge. Still others are “data-inspired,” particularly when there is little to no data and only proxy information, experience, and intuition on which to rely. 

Let’s take a look at each of these methodologies in greater detail: 

#1: The Data-Driven method  

These strive to be objective and rely on the data to speak for itself. They use springboards such as well-defined problems and clear KPIs for designing and executing a solution to achieve a specific outcome.  

Pros: Using this method is fast and objective, because it removes roadblocks that can result from differing opinions and debate. KPIs provide teams with a defined outcome, and can often be applied across various scenarios. 

Cons: Data-driven methods are often rigid and myopic in nature. They are not as adaptable when new factors or information is introduced, and they may miss accounting for important qualitative data. They can also be biased: applying the data is subject to individuals’ perspectives and may be used to serve personal agendas. 

#2: The Data-Informed method  

These consider multiple perspectives to make decisions based on context. Besides data, thought leaders and teams bring in experience and knowledge to create a holistic approach to decision-making.  

Pros: Data-informed methods offer a balance between subjective and objective analysis, and provide insight into how disparate decisions align with each other. There’s also room for creativity, because data-informed methods are flexible and can be adapted for different scenarios.  

Cons: They can also be complex, taking considerable time and effort to optimize. Because these methods involve personal backgrounds and interests, they are subject to human bias that comes with those insights. 

#3: The Data-Inspired method 

These lack data and rigid frameworks, and instead rely on intangible knowledge and experience.  

Pros: They require teams to adapt to new scenarios and insights, inspiring collaboration and eliminating the red tape that can inhibit progress.  

Cons: The downside is that they often lack direction and can’t guarantee a desired outcome will be achieved. Teams will need to adjust their expectations with each new scenario and stay open to a range of paths and endpoints. Additionally, they may be biased, because they lack the objectivity of tangible data points. 

Regardless of the data methodology used, bias will inevitably sneak into every scenario, because no human decision can escape the impact of past experiences. For example, cognitive bias is the tendency to make decisions or take actions in an unknowingly irrational way. It takes form in different ways, including:  

  • Confirmation bias: Decision-makers trust the data that supports their initial beliefs and reject the data that contradicts them.  
  • Recency bias: Decision-makers place greater value on the most recent data while disregarding the information that could also be valuable. 
  • Authority bias: Decision-makers value the highest-paid person’s opinion (HiPPO), regardless of its accuracy or helpfulness. 
  • Anchoring bias: Decision-makers jump to conclusions based on the earliest-returning data. 
  • Survivorship bias: Decision-makers only consider the most successful and more optimistic data.  

It’s important to mitigate bias by seeking out opposing data points that may challenge your initial belief but will have value in strengthening your processes. You should be careful to consider historical data and how observed changes provide insight into how scenarios in question have evolved over time. Authority bias can be mitigated by reframing the HiPPO’s opinion as a hypothesis and testing it. When data is lacking, it’s a good idea to delay decision making until more data is available, and taking into account pessimistic data points and scenarios for a more holistic analysis. 

Choosing the Right Data Methodology 

Product Managers, Product Leaders, and Product Owners must make many decisions and strike a balance between driving desired outcomes, getting buy-in, and mitigating constraints on time and data access. The following considerations can help identify which data methodology best suits your project – or if a balanced approach is the best approach:   

  • Is it operational or optimized-based? → Data-driven 
  • Is it strategic or considering pivots and innovation? → Data-informed 
  • Do you need to make decisions without data or metrics? → Data-inspired 

Additional factors to consider are timeframes, decision frequency, and the experience level of your team members. Data-driven methods are fantastic for repetitive decision-making, as frameworks allow teams to reuse them for changed scenarios for similar outcomes. They can also be used to support entry-level and junior members in situations where the scope of decision-making is smaller. Product Managers typically need to be data-informed, and you need access to data, time to experiment, and a clear idea of which KPIs are optimal. Keep in mind, lack of time is not an excuse to act recklessly – if your decisions require a lot of data, communicate with your leaders and team, and wait until you have it. 

Once you’ve identified desired outcomes and chosen your data methodology, it’s critical to identify the appropriate KPIs. Ask yourself the following questions: 

  • How much of our product do they use?  (Depth) 
  • How many active users do we have? (Breadth) 
  • How often do users log in or visit? (Frequency) 
  • What do users do, and in what order? What is their journey when using our product or features? (Paths)  
  • What are users’ impressions of our product? (Sentiment)  
  • How do you rate the product at that moment? (Feedback) 

Use Data to Optimize Product Management 

Having a deep understanding of data’s role in our decision-making and insights provides clarity and structure, enabling you to design project flows with the confidence that your cache of data, knowledge, and experience will lead you on the right path.  

Techniques for data-based decision-making are especially powerful for digital products. If you’re looking to be strategic or are considering pivots and innovation, take advantage of 280 Group’s Insights to Digital Product Management (IDPS) Suite. You’ll get the tools you need to make data-informed decisions that tell a compelling product story. The suite includes: 

  1. Digital Product Management Assessment 
  1. Digital Product Management Self-Study Course 

Stop drowning in a sea of data and start making data-informed decisions. Get the hands-on practice you need to promote your product strategy and make decisions based on real product data.  

Take the Digital Product Manager Skills Assessment Now
Assess Your Skills
Roger Snyder
May 31, 2022