Data Analysis: The Key Step to Transform Your Data into Strategic Decisions
- Stephane Wald
- Feb 25
- 5 min read
We are moving to the 3rd stage of our Data journey, Data Analysis.
After Data Management, the art of structuring your data, and Data Governance, which defines the rules for its use, we now arrive at Data Analysis—the stage where your data becomes strategic asset.
Analyzing your data was a luxury reserved for large retail groups, such as hypermarkets or restaurant chains like McDonald's at the beginning of the 21st century. Today, it has become essential for all companies, from the largest to the smallest. According to a study by McKinsey & Company, 127 zettabytes of data will be generated by 2025, 10 times more than in 2018. Faced with this unprecedented growth, it is no longer enough to collect and consult data, it must be analyzed and activated to drive customer satisfaction, operational efficiency, and growth—or risk falling behind and disappearing prematurely.
What is Data Analysis?
Data Analysis is the art of interpreting and transforming the raw data, whether internal such as sales from checkout systems or external from platforms like Google or Uber Eats, or even collected through quantitative or qualitative studies, into actionable insights.
The analysis allows you to better understand your business operations and consumer expectations. The process can be enhanced by Artificial Intelligence (AI), which helps automate certain analyses, refine predictions and uncover hidden trends. From these insights, you can develop concrete strategies to improve your business performance.
What are the different types of Data analysis?
There are four main types of data analysis, each addressing different questions and needs. Let’s explore them through examples:
Descriptive analysis:
The WHAT, What Happened?.
➡️ Objective: Understand past events using data.
➡️ Examples: Identify which days generate the highest sales (cash register data); What are the most common product combinations; Find out which products are rated highest on Google, Deliveroo...
This is the foundation of all analysis: a clear view of the past.
Diagnostic analysis:
The WHY, Why did it happen?
➡️ Objective: Identify the reasons behind observed phenomena.
➡️ Examples: Understand that a sales drop might be caused by aggressive competition, supply chain issues, or changes in product perception. For instance, reviews can reveal problems, and further studies can provide a deeper understanding to help develop corrective solutions.
Predictive Analysis:
The WHAT IF, What might happen?
➡️ Objective: Anticipate future events based on trends and patterns.
➡️ Examples: Predict peak traffic during high-demand events to optimize your staffing ans ensure a smooth customer experience, forecast demand fluctuations based on weather trends (e.g. increased ice cream sales above 20°C or decreased demand below 5°C), anticipate stock needs for promotional campaign by analyzing past purchase trends or identify emerging trends such as Healthy or growing customer segments such as vegetarians, through ad hoc consumer studies or panels.
→ A way to make the future more predictable and prepare for it effectively.
Prescriptive Analysis:
The What should we do, What actions should we take?
➡️ Objective : Recommend actions based on previous analyses.
➡️ Examples: Recommend a targeted promotion for underperforming product, adjust prices to maximize sales and profits while remaining competitive and preserving long-term traffic or suggest specific team training to improve customer service based on Google review analyses.
→ This type of analysis focuses on decision-making and actionable steps.
What are the tools and methods to be preferred?
To avoid repeating myself, there's no need to create a complex system and deploy intricate tools to take advantage of your data.
Whether you are a freelancer or a large company, whatever your level of maturity in Data Analysis, there are accessible and effective solutions for everyone. Here is an overview of the recommended tools and methods:
Simple, free tools:
Excel and Google Sheets allow you to perform simple descriptive and diagnostic analysis, while Google Data Studio enables advanced visualizations.
→ Ease of use, free or low cost.
Intermediate tools:
Power BI, Tableau, or Looker.
These oftenpaid solutions are ideal for growing companies that need complex analytics. They offer advanced visualization capabilities, increased interactivity and extensive connectivity with various systems (ERP, CRM, etc.).
→ Advanced visualizations and interactivity.
Specialized tools:
For companies with quick analysis needs, lacking time or IT teams, solutions like Dvore, which I implemented at Vapiano, can be an excellent alternative. They allow for quick analysis with an easy learning curve.
→ Powerful and affordable for SMEs.
Advanced solutions:
For larger organizations, tools like Alteryx or SAS handle large data and model complex scenarios. These tools often require dedicated resources but can be essential for advanced analysis.
→ Ability to process large volumes and automate complex analyses.
Complementary solutions:
Panels, consumer studies and customer reviews help you gather direct feedback from your customers to enrich your internal data, allowing you to better understand their expectations and anticipate necessary adjustments.
Again, there are simple solutions at no or low cost, such as Microsoft Form or Google Form for satisfaction surveys. You can also work with on companies like Myli with whom I work.
→ Additional analysis through external feedback.
Get support: don't hesitate to get help from external consultants both to help you deploy the right tools and to carry out the analyses adapted to your needs.
The choice of tools and methods depends on your needs (the questions you are looking to answer), your resources (budget, internal skills, dedicated time) and your maturity in Data.
What are the pitfalls to avoid in Data Analysis
To maximize your analyses and avoid false leads, beware of the following pitfalls:
Underestimating the importance of data quality: As I have already said in my previous articles, garbage in, garbage out. The best analysis in the world is worthless if the data is of poor quality.
Drowning in data: Too much data kills data. It is important to have the right data, not too much or too little. You must define your scope, what are you looking for?
Not contextualizing: A drop in sales might seem alarming, but if it corresponds to a period of low annual footfall, it may be normal.
Ignoring the human aspect in analytics: Data is powerful, but it needs to be interpreted by experts who understand the context. Powerful tools, complex models, and AI assist the analyst but cannot fully replace human insight.
By avoiding these pitfalls, you will transform your data into effective strategic assets.
For many, Data Analysis is just about producing numbers. However, it is much more than that. Its objective is to translate available information into informed decisions based on the facts. In an increasingly demanding environment where the volume of data is exploding, it is essential to remain competitive.
Don't miss the final step of our journey, Data Telling or the art of turning your analytics into compelling stories. It is not enough to make excellent analyses, you also need to communicate them simply and clearly.
Need tailored support?
At TippsMe, we are here to help you turn data into tangible, measurable performance drivers.
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