Ditch the Gut Feeling, Grab the Spatula: Data-driven decisions are way tastier

8 Min Read • Mar 27, 2024

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Larisa Bortă

Product Manager

Author Image

In the world of cooking, a chef knows that throwing ingredients together rarely results in a culinary masterpiece. The same applies to data. Today’s most valuable ingredient can overwhelm business without a clear recipe for success: the analytics strategy.

By embracing an analytics strategy as your recipe, you can transform raw data into actionable insights that fuel your business growth. The key ingredients are clear objectives, the willingness to experiment, and the relevant data.

Analysing Ingredients: The Role of Analytics in Data Science and Modeling Solutions

Much like a chef relies on recipes to craft culinary delights, data scientists leverage analytics to develop predictive models and algorithms. In modeling machine learning (ML) and artificial intelligence (AI) solutions, analytics play a critical role in training algorithms. Data analysts fine-tune algorithms using iterative processes and refine models to enhance accuracy and efficiency, in the same manner a chef adjusts cooking techniques to achieve the desired taste and texture.

Stirring the Pot: The intersection between Product Management and Data Science

While seemingly distinct, Product Management and Data Science share a fundamental goal: utilising insights for optimisation and innovation. Product managers, like a chef, are the visionaries. They identify the customer needs and define the product’s goal. They are responsible for creating the recipe, outlining the ingredients (features) and the desired outcome (user-oriented). On the other hand, data scientists gather and interpret data, providing insights into user behaviour and market trends.

The blend of product management and data science creates a product that not only tastes good (delivers a great user experience), but is also cooked perfectly (achieves business objectives).

Balancing Flavours: The Importance of Qualitative Data over Quantitative Data

To understand how to create such a product, you need both quantitative and qualitative data. While quantitative data serves as a foundational element in assessing numerical metrics, it is the qualitative data that unlocks the Why behind the What. Equivalent to the contrast between simply following a recipe’s measurements and understanding the blend of flavours, qualitative data adds layers to understanding the raw data. It’s the difference between knowing that a dish received five-star ratings and understanding the specific aspects of taste and presentation that led to those ratings.

Cooking up success: Crafting personalised experiences with Recommendation engines, Cohorts, and Behavioural Analysis

Just as you wouldn’t serve the same dish to everyone without considering their individual preferences and dietary restrictions, achieving success in today’s digital environment requires a similar level of personalisation. This is where recommendation engines, cohorts, and behavioural analysis come in, serving personalised experiences that resonate with your audience.

Recommendation engines function similarly to experienced chefs, who meticulously suggest dishes based on past preferences and culinary trends. They analyse data, identifying patterns for personalising suggestions. Cohorts, on the other hand, function like groups of friends with similar palates (E.g. vegetarians or seafood lovers). 

By segmenting your audience into cohorts based on shared characteristics or behaviour, you can shape your approach to resonate with each group’s unique preferences. Finally, behavioural analysis plays the role of an insightful observer, carefully studying how users interact with your offerings.

From Recipe to Results


  • Data
  • Tools
  • Team

Ensure your data is fresh and relevant to your goals, evolve your data analysts, and use the software and the proper techniques to analyse it.

The Recipe

As a chef, you wouldn’t use a spoon to stir a delicate sauce. Similarly, choosing the right tool for your data analysis is fundamental. Explore various techniques, from simple simmering (data visualisation) to complex pressure cookers (machine learning). As you will try different herbs and spices, you should try different analysis methods in order to mix things up.

Tips and Tricks

  1. Data is like salt, sprinkle wisely! Aim for clear, focused results that inform your decisions, not a confusing, salty mess.
  2. Presentation matters: Data visualisations are the plating of your insights. Make them clear and visually appealing for easy digestion.
  3. Savour the journey, not just the dish! Remember, the most delicious discoveries often happen when we embrace the journey, not just the final bite.
  4. Think of your team as the secret ingredient that adds flavour to the dish. Need a qualified team? Contact us and let’s cook up something great together.
Larisa Bortă

Larisa Bortă

Product Manager

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