Data Analytics
Data Analytics explained
Start with a clear question. Why, for example, does delivery time appear longer than last month? Before building a model, establish what the data mean and where they come from. Are these calendar or working days, completed or still-open orders, and has the order mix changed?
Preparation is part of the analysis. Duplicate records, incompatible units, missing values or incorrectly joined tables can distort results. Corrections and exclusions must be justified and traceable. Inconvenient observations should not be removed simply because they do not fit a preferred story.
Exploratory analysis initially examines distributions, differences and anomalies. Charts and simple summaries may be sufficient. A hypothesis discovered during exploration should subsequently be tested deliberately. Searching many groups can also uncover chance differences; selected findings should therefore not be presented as certain rules.
Communication should combine the conclusion with its limits. Explain which cases the result applies to, the size of the data foundation and remaining uncertainty. Forecasts also need assumptions and evaluation on suitable new data. AI can support steps in the work but cannot replace understanding the data or checking calculations and conclusions.
Examples
Hypothetical application
A dispatch team sees an increase in average delivery time. Separating small parcels from bulky goods reveals that the figures contain more bulky orders. The team therefore checks the individual groups and their case counts before claiming that the dispatch process has deteriorated. The overall figure alone would have hidden this distinction.
Key Points
- Keep data origins, definitions and cleaning traceable.
- Examine distributions and relevant groups alongside totals.
- Treat discovered patterns as questions to test, rather than finished explanations.
Practical application
Define the question and data foundation. Check units, duplicates, missing values and joins. Examine meaningful groups and distributions, document decisions and state the scope of the conclusion.
Useful measures
Traceability
Sources, processing and calculations can be checked.
Appropriate conclusion
The statement fits the data foundation and method used.
Decision usefulness
The analysis makes a next step or remaining question concrete.
Common mistakes
- Using the overall mean as a complete description.
- Excluding inconvenient cases without a documented reason.
- Turning a correlation or prediction into a proven cause.
Sources and context
- NIST: What is Exploratory Data Analysis?
Explanation of exploratory data analysis and examining structures, anomalies and assumptions.
Frequently Asked Questions about Data Analytics
Data collection records information. Data analytics investigates a specific question using that information. More stored records do not automatically produce a better answer.
No. Tables, visualisations and suitable statistical methods are enough for many questions. The method should fit the task and available data, rather than a desire for maximum technical complexity.
Clear definitions, traceable processing, appropriate comparisons and openly stated limitations. Important calculations should be reproducible; surprising results deserve an additional check.
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