How to Data Interpretation Well?

Data interpretation is an essential part of daily life for all of us  and more so for Decision makers and managers.

Every day we receive lots of data-visual, audio, textual  and, numeric. We derive information and  insights from this data by interpreting it as per our thinking and belief patterns. We interpret data when we read poll results for political parties, we interpret when we compare and buy any product and we interpret when we check traffic conditions to decide which route to take to office.

In businesses also, being able to conduct accurate and meaningful data interpretation is one of the most invaluable skill that often separates high growth success stories from failures.

In today’s world of  big data with high volume of different types of data being received by business organization, the need for staff skilled and experienced in  Data Interpretation and analysis is growing at an alarming rate.

 

What is Data Interpretation?

Interpretation of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making.

The most important role of data interpretation is for making inferences and predicting future business needs or trends. How accurate these predictions are is based on how well the data was interpreted and inferred for future predictions.

Of course how the data has been gathered is also of equal importance, however even if the data collection process is sound, data can be misinterpreted.

Data are often interpreted and reported with hidden agenda, and the results can therefore be misleading or incomplete. For example, an advertiser or a media channel may try to get viewers’ attention by presenting the data in a skewed or selective manner by only highlighting what they want viewers to remember.

How the data is analyzed and interpreted depends on  several factors like the purpose of assessment, how the data was collected, its volume, and scope as well as any constraints faced while collecting it.

Quantitative Vs. Qualitative Data Interpretation

Data can be Numeric i.e. quantitative based on numbers or it can be Qualitative based on text, pictures and other visuals.

The analysis of numerical (quantitative) data is represented in mathematical terms. It can range from the simple assessment of frequency, proportions and averages to the test of correlation and causal hypothesis using various statistical tests.

The qualitative data analysis is conducted by categorizing or organizing the data into common themes or categories. Interpreting qualitative data is more complex and time consuming since it lacks the structure which is inherent  in numerical data.

However using various techniques and use of  now available software’s for Text analytics, it is now possible to interpret valuable information on  customer sentiments, satisfaction and expectations from rampantly available comments, Likes and similar data on social media etc. Leading brands and companies have already started to make use of such qualitative data for interpreting consumers’ needs and designing their products, services and marketing around that.

Key considerations for successful Data Interpretation

To conduct a well-structured data analysis and interpretation, it is imperative to

• Understand the data from different dimensions and what it implies to its various users.
• Select the most suitable analysis technique based on the objective, type and volume of data as well as any data collection constraints.
• Ensure that there is adequate volume of data to make meaningful and sustainable interpretation and inferences.
• List out any assumptions made while analysis and ensure no biases or beliefs have affected the interpretation.
• Revalidate the interpretation and predictive data models using suitable validation techniques.
• Re-examine any outliers or exceptional data as often times data that is non-conforming or contradictory leads to valuable innovative insights which can result in a successful new product or marketing initiative.

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