DATA INTERPRETATION

DATA INTERPRETATION

The interpretation of data assigns a meaning to the information analysed and determines its signification and implications. It refers to the implementation of processes through which data is reviewed for the purpose of arriving at an informed conclusion.

Keeping in view its importance, data mapping should be done properly. Data is obtained from multiple sources, so it needs to enter the analysis process with haphazard ordering. Data analysis is usually subjective and thus, goals of interpretation may vary from one business to another. Basically, there are two main types of analysis, quantitative and qualitative.

A good decision is to be made regarding scales of measurement. The varying scales include the following:

  • Nominal Scale: It is non-numeric categories that cannot be ranked or compared quantitatively. Variables are exclusive and exhaustive.
  • Ordinal Scale: It consists of categories that are exclusive and exhaustive but with a logical order. Quality ratings and agreement ratings are examples of ordinal scales (i.e., good, very good, fair, etc., or agree, strongly agree, disagree, etc.).
  • Interval: It is a measurement scale where data is grouped into categories with orderly and equal distances between the categories with orderly and equal distances between the categories. There is always an arbitrary zero point.
  • Ratio: It contains features of all three.

When interpreting data, an analyst must try to discern the differences between correlation, causation and coincidences, etc., in addition to some other factors.

QUALITATIVE DATA INTERPRETATION

Narrative data is mostly collected by employing a wide variety of person-to-person techniques. It is basically described as ‘categorical’. The description is not through numerical values or patterns but through descriptive context or text. These techniques include the following parameters.

  • Observations: Here, behaviour patterns may be the amount of type and time spent in an activity and communication used.
  • Documents: Here, different types of documentation resources can be coded and divided based on the type of material they contain.
  • Interviews: It is described as the best collection method for narrative data. Inquiry responses can be grouped by theme, topic, or category. The interview approach helps in highly-focused data segmentation.

A person-to-person data collection technique can lead to three basic principles, notice things, collect things and think about things. Qualitative data much open to interpretation must be ‘cooled’ so as to facilitate the grouping and labeling of data into identifiable themes.

QUANTITATIVE DATA INTERPRETATION

The keyword in quantitative is ‘numerical’. It is a set of processes by which numerical data is analyzed. It involves the use of statistical modelings, such as standard deviation, mean and median. Let’s quickly review the most common statistical terms as follows:

  • Mean: A mean represents a numerical average for a set of responses.
  • Standard deviation: It reveals the distribution of the responses around the mean, the degree of consistency within the responses, and then insight into data sets.
  • Frequency distribution: This is a measurement of gauging the rate of response appearance within a data set. It is extremely keen on determining the degree of consensus among data points.

It entails correlation tests between two or more variables. The different processes can be used together or separately, and comparisons can be made to ultimately arrive at a conclusion. Other signature interpretation processes of quantitative data include the following:

  • Regression analysis
  • Cohort analysis
  • Predictive and prescriptive analysis

Importance of Data Interpretation

The purpose of collection and interpretation is to acquire useful and usable information and to make the most informed decisions possible. Data interpretation includes the following characteristics:

  • Data identification and explanation
  • Comparing and contrasting data
  • Identification of data outliers
  • Future predictions

There are some common issues with data interpretation:

  1. Informed decision-making: Data analysis should include identification, thesis development, and data collection followed by data communication.
  2. Anticipating needs with trends identification: Data insights provide knowledge, and knowledge is power.
  3. Cost efficiency: Proper implementation of data analysis processes can provide businesses with profound cost advantages within their industries.
  4. Clear foresight: Companies that collect and analyze their data gain better knowledge about themselves, their processes, and their performance.

In addition, there are certain problems with data interpretation.

It is usually said that ‘big data equals big trouble’ where some ‘pitfalls’ do exist and can occur when analyzing data, especially at the speed of thought. Let’s identify three of the most common data misinterpretation risks and shed some light on how they can be avoided.

  1. Correlation mistaken for causation: It is the tendency of data analysts to mix the cause of a phenomenon with its correlation. When two actions occurred together, one caused the other. The remedy is to attempt to eliminate the variable you believe to be causing the phenomenon.
  2. Confirmation bias: It occurs when we have a theory or hypothesis in mind but are intent on only discovering data patterns that provide support while rejecting those that do not. This pitfall is often based on subjective desires. Thus it always remembers to try to disprove a hypothesis rather than try to prove it.
  3. Irrelevant data: As large data is no longer centrally stored and as it continues to be analyzed at the speed of thought, it is inevitable that analysts will focus on data that is irrelevant to the problem they are trying to correct. The remedy is to proactively and clearly frame any data analysis variables and key performance indicators prior to engaging in a data review.

Keeping in view all these aspects, we need to be careful about the following factors:

  1. Collect your data and make it as clean as possible.
  2. We need to be careful about the type of analysis to perform be it qualitative or quantitative and apply the methods respectively to each. We have already discussed qualitative and quantitative aspects.
  3. We may need to take a step back and think about data from various perspectives and what it means for various participants or actors of the project.
  4. We need to reflect on our own thinking and reasoning, such as correlation versus causation, subjective bias, false information, and inaccurate data, etc.

Leave a Reply