Comment analysez-vous et interprétez-vous des ensembles de données complexes et à grande échelle?

Sometimes the analyses you produce are confirmatory, that is, hypothesis-driven explorations of the data set. In other cases, the analysis is exploratory, in which the hypotheses may include, "who are my best customers?", "how do I compare to my competitors?", or "why have sales declined?". In the latter case, hypotheses are useful, but they also require a degree of pre-knowledge about the findings of the analysis. This is called conference room thinking, and does not always yield insights, but simply work to confirm what is already known. Both approaches can be valuable, but if you haven't already established the connections between your data, exploratory might be more useful.

Getting buy in and alignment with key stakeholders at the appropriate levels at the start of the analysis will not only make the insights actionable but also create commitment and bias for action when results are in.

One option is to start with clearly defined hypotheses so the quantification and measurement is correctly designed and executed. This would require sufficient business context about the data to develop clear hypotheses to begin with. Another option is to do an EDA(Exploratory Data Analysis) which requires looking at all columns in the data, analysing their distributions and finding key insights which then aid in hypotheses development.

Ensure that your objectives and questions are well-defined and unambiguous. Avoid vague or broad statements that may lead to confusion or misinterpretation. Define your objectives and questions in a way that allows you to measure and quantify the results. This will help achieve the desired target of constraint analysis.

The specific goals of the analysis influences the choice of tools and methods. For example: Descriptive Analysis- Can be performed using spreadsheets (e.g., Excel) or statistical software (e.g., SPSS, R). Predictive Modeling- Often requires machine learning libraries (e.g., scikit-learn, TensorFlow) & programming languages (e.g., Python, R). Data Visualization- Can be achieved using libraries like matplotlib, seaborn, or specialized visualization tools (e.g., Tableau, Power BI).

With the complexity and variability of data quality, granularity and sources, triangulation, power of the methods and understanding of the level of accuracy needed for the analysis should guide these decisions.

In many cases, being tool-agnostic is the right approach. It is the rigor of the analysis and what is already known about the data, that should drive the choice of methods and, therefore, tools. For example, basic correlations and cross-tabulations provide foundational knowledge about each data set and the variables within. Sometimes using simple tree-based approaches are helpful for understanding your data, particularly if you already have a hypothesis about a particular outcome you wish to explore. Data reduction techniques, such as factor analysis, are very useful to summarize large sets of correlated data, and of course, regression and modeling provides the hypothesis testing for your outcomes.

If the analysis will be repeated it is beneficial to put in place ongoing processes and documentation in place to continuously and systematically ensure that this is done well and improved continuously.

This entails reviewing and repairing any mistakes, anomalies, missing values, contradictions, or duplication in your data. In order to make your data compatible and accessible across many sources and formats, it also entails standardizing and transforming your data. To improve your analysis, you might also need to add characteristics or other information to the data, which is known as enrichment or augmentation.

Hey no judgement for me, but gotta ask... you good, LinkedIn? feeling like you may have taken more GPT than planned.... Blink twice for help.

Calculate churn rate, analyze customer demographics, & visualize purchase patterns. Build a logistic regression model to predict churn based on customer features. Evaluate the model's performance using metrics like accuracy and AUC. Identify the most important factors influencing churn (e.g., purchase frequency, customer tenure).

In addition to findings, include actionable next steps based on alignments and commitments with stakeholders at the onset of the analysis.

It is advisable to employ suitable and visually appealing visual aids, such tables, charts, graphs, or dashboards, to emphasize and demonstrate your principal conclusions and suggestions. It is imperative that you furnish corroborating facts and state any constraints or uncertainties in your research.

Data analysis is an ongoing process of learning and development rather than a one-time task. It's important to constantly ask your audience for comments, which you can then utilize to update and improve your analysis and interpretation. Along with tracking & evaluating the results of your study, you should also look for any gaps or areas that could want more R&D.

This is commonly known as Continuos Improvement. As the name says as soon as you finish the process it all starts again to make sure your implementations stay in place and continue refining the business.

Actually, there's 2 levels of analysis that seem to work. First is to throw it all in something like Google Data Studio and see if you can logially find correlations and perhaps causations as well. The second is to throw it all in Google Big Data a similar tool and see if there are trends that AI can spot that your eye may have missed.

Some specifics around type of models and actual examples will enrich this article and make it relevant to a larger audience. Right now, it is appropriate primarily for early career analysts.

A clear scope based on achievable objectives is the best way to start. Then, based on your needs, take in consideration your tool limitations and knowledge gaps to select the best combination for your project. Finally, create, review and shine your business questions and / or problem statement, The clearest business questions or problem statements are, the easiest will collecting the right data and selecting the right approach become.