Data interpretation and analysis have taken a center stage in the digital stage and small amounts of data are frightening.
According to a Digital university study, the data supply in 2020 was 64.2 zettabytes!
Based on the amount of data, it has been very clear that the calling card of any successful organisation in today’s global world will have the capability to analyse multiple data, produce actionable insights and adapt to new market needs, all this at the speed of thought.
- Meaning of Data Interpretation
- Examples of Data Interpretation
- Importance of Data Interpretation
What is Data Interpretation?
Data Interpretation refers to the process of utilising miscellaneous analytical steps to preview the data and arrive at required conclusions.
Interpretation of data helps researchers to manipulate, categorise and summarise the data in order to answer critical questions.
The significance of data interpretation is obvious, which is why it must be done correctly. Data is likely to come from a variety of sources and to enter the analysis process in an unorganised state.
Data analysis is notoriously subjective. That is to say, the nature and objective of interpretation will differ from one company to the next, and will most likely be related to the sort of data being evaluated.
While there are a variety of techniques that are used depending on the nature of the data, the two most prevalent categories are “quantitative analysis” and “qualitative analysis.”
However, before any meaningful data interpretation investigation can begin, it is important to understand that visual representations of data findings are meaningless unless a clear judgment about measurement scales is made.
The scale of measurement for the data must be decided before any significant data analysis can begin, as this will have a protracted impact on the data interpretation ROI.
The varying scales include
- Nominal Scale – These are non-numeric categories that cannot be ranked or compared quantitatively.
- Ordinal Scale – In this scale, it includes exclusive categories that are exhaustive and exclusive when in a logical order. Examples of ordinal scales are Good, Very Good Fair, Strongly Agree, Disagree, and many more.
- Interval – It is a measurement scale where data is grouped into sections in order.
- Ratio – Contains all features of all three scales.
Now, I hope that you got a small idea of what data interpretation is, and let me explain to you the examples and their importance.
Examples of Data Interpretation
Below are the three examples of data interpretation with answers. Just to make you understand the process.
1. A factory buys three machines M1, M2, and M3 to manufacture 3 products x, y, and z. Each Machine runs for 12 hours a day. The following table gives the time taken by each machine to manufacture 1 unit of each product.
What is the maximum number of products manufactured in a day?
The maximum number is requested, and the product’s machine time must be kept to a bare minimum. Products X, Y, and Z require the least amount of time on machines M1, M2, and M3.
Because each machine has a 720-minute manufacturing period, the three machines can produce 72 pieces of Z, 80 units of Y, and 60 units of X, respectively.
As a result, the maximum number of units that may be manufactured in a single day is 212.
2. The requirement for 40 sets of X and 50 sets of Y on a certain day must be met. What is the highest number of sets of Z that can be created on that day if the remaining assembly is exclusive of product Z?
Allow X and Y to be created on equipment that requires the least amount of time.
As a result, machines M1 and M2 produce X and Y, respectively. Manufacturing 40 sets of X on M1 takes 720 minutes – 480 minutes = 240 minutes.
As a result, this machine can produce 24 units of Z. Manufacturing 50 sets of Y on M2 takes 720 – 450 minutes or 270 minutes total.
As a result, this machine can produce 15 sets of Z. Because M3 has a manufacturing period of 720 minutes, 60 sets of Z can be produced on this machine. As a result, the total number of Z sets that can be made is 99.
3. After 3 units of X and 4 units of Z have been made, a unit of Y can be manufactured. What is the shortest time it takes to make 15 units of Y?
Before producing 15 units of Y, 45 units of X, and 60 units of Z, 45 units of X and 60 units of Z must be produced.
X-M1 (540 minutes), Z-M1 (180 minutes), Z-M3 (42 units) 504 minutes, and Y-M2 (15 units) 135 minutes. As a result, the total time is 1359 minutes.
Now let me tell you the importance of Data Interpretation to understand the value of this topic.
Importance of Data Interpretation
1) Informed Decision Making – In data interpretation the data is collected from the thesis and in communication a proper decision is made.
2) The process will invariably be again with new data and sights if the data findings are monitored.
3) Knowledge is power, and data insights bring knowledge. The information gleaned from the market and consumer data analytics can be used to forecast trends for peers in similar market categories.
I hope you understood the basics of data interpretation and its importance through examples.
Stay on this track to learn more about methods, differences, and how to interpret data?
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Learn more about what is Data Interpretation? in part 2 blog
Click Here: What Is Data Interpretation? – Part 2
Frequently Asked Questions
1. What are data interpretation types?
Tabular DI, Pie Charts, Bar Graphs, Line Graphs, Ceselet DI, and Miscellaneous are some of the several types of data interpretation.
2. What are data interpretation skills?
The ability of a person to correctly extract and analyse significant information or data from various sources of data such as charts, tables, graphs, and so on is referred to as data interpretation. It entails a number of phases, including data collecting, data processing, and data analysis.
3. What is a data interpretation test?
Data interpretation exams are a type of screening examination that is used to examine data analysis abilities. An inferential test is a which was before aptitude test used by businesses to assess candidates’ ability to read data.