Data Analytics is considered one of the most prominent fields for careers and businesses in India. Data analysis plays an important role in helping professionals understand information and make meaningful decisions.
Students learn Python and SQL, while firms build dashboards, and data-driven professionals increasingly face challenges in managing big data across finance, retail, healthcare, marketing, manufacturing, and e-commerce.
There is a serious mistake on both sides; beginners tend to jump straight into data analysis without first understanding what the data means, and firms often make the same mistake.
A highly sophisticated model built on misunderstood data can appear to provide the right solution without actually solving the problem.
Thus, simple questions are a good starting point for any good analysis.
Start by Understanding What Each Variable Represents
The first step in data analysis is to understand what each important column represents before performing any calculations.
Imagine a retail dataset containing:
- customer age;
- order value;
- city;
- product category;
- payment method;
- number of items purchased;
- order date.
These variables are not all the same.
Order value and age are numeric. City and payment type are categorical variables. Dates contain time-related information, while product categories represent groups rather than quantities.
Knowing these differences enables calculations and visualisations to make sense.
For instance, calculating an “average city” would not make sense, but calculating the “average order value” would.
Do Not Rush Into Relationships
Students may find it interesting to study relationships between variables.
Can advertising be considered a good sales technique? Are older consumers purchasing more products? Is there any connection between the time of delivery and subsequent repurchase?
While these questions may be interesting, analysts should first understand each variable separately.
Consider a company where transaction values range from ₹100 to ₹1,00,000. Knowing that the average is ₹500, ₹5,000, or ₹50,000 is useful, but understanding why is equally important.
Simple Statistics Can Reveal a Lot
Averages can prove useful but are sometimes insufficient.
Consider, for example, two sets of people who both have an average spending per month of ₹5,000.
In the first set, each individual spends between ₹4,000 and ₹6,000.
In the second set, some individuals spend ₹1,000 per month, while others spend more than ₹30,000.
The averages are quite similar, but the spending patterns of the customers are not.
That is why analysts also examine:
- median;
- minimum and maximum;
- percentiles;
- range;
- variance;
- standard deviation;
- frequency distributions.
These measures help reveal how values are spread rather than reducing everything to a single number, making data analysis more informative.
Visualisation Makes Patterns Easier to Notice
Patterns are much easier to identify when data is presented visually.
Sometimes graphs could show problems before tables do.
Histograms can show whether a numeric variable has a uniform or clustered distribution. Bar graphs can show which category dominates a dataset, while box plots can help identify unusually large or small values. This type of preliminary investigation is often called univariate analysis, meaning the analysis of one variable without considering other variables.
These simple questions can provide valuable insights. Are most of your customers in a certain age category? Do some of your products dominate your sales? Are there any outliers that could distort your average? Understanding these questions provides a strong foundation for further data analysis.
Outliers Need Investigation, Not Automatic Removal
During early-stage data exploration, there is a high chance of spotting unusual numbers.
For instance, the mean of e-commerce transactions may be less than ₹10,000, but there could be one transaction valued at ₹3,50,000.
It could either be a mistake or a legitimate bulk purchase.
Useful data could be lost if it were deleted without investigation.
Instead, ask: Why is this observation there?
This is also true in the case of salaries, transaction amounts, delivery times, web sessions, and production figures.
An outlier can indicate many things, such as a data error, fraud, an unusual customer, or an operational problem.
Data Quality Comes Before Advanced Techniques
This is because simple exploration can point out problems regarding data quality.
Datasets frequently contain:
- missing values;
- duplicate records;
- inconsistent spelling;
- impossible values;
- incorrect data types;
- inconsistent units.
For example, in a certain dataset, Delhi may be denoted as “Delhi”, “New Delhi,” and “NCR Delhi”. If not properly cleaned, the software may consider them as different locations.
Similarly, if salaries are partly represented in rupees and partly in lakhs, the analysis can produce incorrect results if the inconsistency is not identified.
An ill-defined field cannot be corrected using any machine learning technique.
Move From One Variable to Relationships Gradually
Once these variables are understood, the next question is: How do they relate to one another?
One example is the relationship between age and consumer expenditure. It is possible to compare cities when looking at different product groups. Conversion rates can be considered in relation to advertising expenditures.
Why is this important?
Knowing the variables first makes understanding the relationships easier.
Second, this reduces the likelihood of identifying a false relationship between two variables caused by how the data was recorded.
This gradual approach makes data analysis more reliable and easier to interpret.
Business Context Still Matters
Numbers never say everything.
Let us consider the possibility of extremely high sales in October and November. It may very well seem to be a weird deviation from the pattern to an outside observer who has no idea about India.
Business analysts in India would readily understand the impact of increased demand during Durga Puja, Dussehra, and Diwali.
In the same way, datasets in India are affected by regional purchasing patterns, salary payments, and climatic conditions.
It is better to combine technical analysis with domain knowledge.
Conclusion
Complex algorithms do not come before a proper data analysis procedure.
It begins with understanding the nature of the data, the variation within it, and what can and cannot be included in the analysis.
Despite the fact that basic procedures such as summary statistics, visualization, and univariate analysis may look simplistic as compared to advanced techniques used in machine learning, they can save you from falling into traps that could render your data analysis ineffective.
The process of learning this foundation should not be rushed if you are an aspiring data analyst in India.
This will make everything that follows more precise, relevant, and easier to interpret.
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