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Data Processing
Data processing involves tasks to refine raw data for analysis or application, including cleaning, organising,
transforming, and summarising information. It ensures data accuracy, relevance, and accessibility for effective
decision-making and analysis. It is crucial across various sectors like business, science, and technology, facilitating
better utilisation of data assets. Data processing helps computers understand raw data. Use of computers to
perform different operations on data is included under data processing.
Data Interpretation
Data interpretation is the process of making sense of data by analysing it to uncover patterns, trends, and
insights. It involves examining the data to understand its meaning, implications, and significance, helping to
inform decision-making and draw conclusions. It is the process of making sense out of data that has been
processed. The interpretation of data helps us answer critical questions.
Process of Data Interpretation
The steps in the process of data interpretation are as follows:
Acquire Process Analyse Interpret Present
1. Acquire: This initial step involves gathering raw data from diverse sources such as surveys, databases, or
sensors. It ensures that all relevant information is collected to provide a comprehensive dataset for analysis.
2. Process: Once the data is collected, it undergoes cleaning and organisation to remove errors, inconsistencies,
or irrelevant information. This step ensures that the data is in a standardised format and ready for further
analysis.
3. Analyse: In this phase, the cleaned and organised data is scrutinised to identify patterns, correlations,
or trends. Statistical methods, algorithms, or data visualisation techniques may be employed to extract
meaningful insights from the data.
4. Interpret: After analysing the data, the results are interpreted to derive actionable insights or conclusions.
This involves understanding the implications of the analysis findings in the context of the problem or question
at hand.
5. Present: The final step involves presenting the interpreted findings in a clear and engaging manner. This
could include visualisations such as tables, graphs or charts, along with concise summaries, to effectively
communicate the insights derived from the data analysis.
These steps make sure that working with data is organised, complete, and useful, so that organisations can make
smart choices based on the data.
Methods of Data Interpretation
Data interpretation is the process of making sense out of a collection of data that has been processed. This
collection may be present in various forms like bar graphs, line charts and tabular forms and other similar forms.
There are two ways to interpret data-
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