Data analysis refers to the identification of patterns in data to extract conclusions and insights.
Data analysis is usually the role of a data analyst who will perform the activities needed to provide with actionable intelligence. Frequent activities done by a data analyst are data cleaning, data mining, statistical analysis, diagnostic analysis, regression analysis, data visualization, text analysis, cluster analysis.
Some of these are specialities of Data science and some others are more traditional data analytics techniques. Regardless, the objective of this step is to provide with insights that can be turned into actions (next step of GIDAR).
Elements of an actionable Data Analysis
Clear Goals
As stated in the first step of the method, Goals are intrinsic to businesses and they need to be clearly defined. Everyone involved must clearly understand what is that we are trying to achieve.

Lack of clear goals is a frequent issue with data analysis and one of the main reasons of futile efforts.
Analysis Scope
Also, a frequent source of trouble is the scope. At the beginning of the data analysis, we need to objectively define what are the boundaries. What are the areas we are going to cover? What is excluded? What are the metrics and KPIs that you will use? Which countries are included?
Analysis Methodology
The data analysis methodology refers to the set of techniques and conditions that you will apply on top of the dataset. You need to declare the data analysis process that you followed. A good methodology includes:
- Category of the data analysis: Descriptive Analytics, Predictive Analytics or Prescriptive analytics. You can have more than one.
- Analysis technique: data mining, machine learning, statistical analysis.
- Name of the data analyst.
- Date ranges for the analysis and data.
- Sources of data: Data Warehouse, Salesforce, Google Analytics, Shopify, etc.
- Collected data: Ecommerce sales, customer purchases, lead report, campaign X.
- Caveats or known issues: Excluded Data, missing countries, seasonality, etc.
- Data quality: Is the data complete, accurate and unique?
Data Quality
Data quality is a common problem at the time of believing in the conclusions of a Data Analytics project. It is frequent that someone will raise the flag of non-reliable data along with the project (especially if they don’t like the outcome).
TO avoid this common pitfall is important to invest time on data quality. If you are planning to recommend changes you have to make sure that your conclusion is reliable.
Partnering with data engineers is always a good way, they can analyze the quality of the collected data.
Data Analysis Technique
Depending on the category of the analysis, descriptive, predictive or prescriptive analytics you will need to pick one or another analysis method as well as the analysis tools.

For advanced analytics cases (prescriptive and predictive analytics) is common for a data scientist to use data analysis methods like data mining, machine learning and tools like Python or R.
There are a lot of good articles, discussions and even job opportunities around data science at the website Analytics Vidhya.

For descriptive analytics, the data analysis technique is more exploratory data analysis, descriptive statistics with tools like Microsoft Excel, Tableau or Power BI.
Analysis Output
Now that you have collected and analyzed quantitative and qualitative data is time to put it into a format that can be digested by everyone. I say digested because is common to see reports that are only understandable by the creator and that is a huge mistake. The report should be self-explanatory and contain clear insights.
The template below is from
How to produce a strong Data Analysis with Actionable Insights
Go back to your Goals
It is easy to get lost in the sea of DATA. Have a copy of your goals always in front of you. It is your north star and will put you back on track when you get lost.
Look at the Key questions
At the end of the step information (useful information), you should have collected a set of key questions like Why customers buy this or that? How can we measure the quality of the leads? What is the impact of a change in price?. These are good pathfinders for your data analysis.
Blend information with Data
Take the questions above and answer them with data or use the information to filter your data. For example, if you know that your sales at Christmas are always excellent you might want to exclude that period from the analysis.
Choose the right Analysis Technique
Chose the right data analysis tool, not all are born equal. If you are going for statistic, make sure that your dataset has statistical significance. Your data analysis skills could worth nothing if you choose the wrong methods.
When using visuals avoid complex visualisations like a scatter plot. They are very powerful but some get confused.
Dashboards are very good to monitor but for analysis, it is better to have single charts with captions. Less is more!
Master the Output
A great data analysis contains a scope, methodology, executive summary, visuals, captions, and conclusions.
Once you have all inside give it a beautiful final touch. People are more likely to pay attention if your output is nice. Make your conclusions and key charts with big and bold letters. If your output is PowerPoint use the 10, 20, 30 technique. It never fails.
Conclusion
There are many more things to say about data analysis, it can get dense, complex and long but it can be much lighter and easier if you rely on the previous 2 steps of the framework. Focus on your goals and use the information.
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