Data interpretation and analysis are fast becoming more valuable with the prominence of digital communication, which is responsible for a large amount of data being churned out daily. According to the WEF’s *“A Day in Data”* Report, the accumulated digital universe of data is set to reach 44 ZB (Zettabyte) in 2020.

Based on this report, it is clear that for any business to be successful in today’s digital world, the founders need to know or employ people who know how to analyze complex data, produce actionable insights and adapt to new market trends. Also, all these need to be done in milliseconds.

So, what is data interpretation and analysis, and how do you leverage this knowledge to help your business or research? All this and more will be revealed in this article.

Data interpretation is the process of reviewing data through some predefined processes which will help assign some meaning to the data and arrive at a relevant conclusion. It involves taking the result of data analysis, making inferences on the relations studied, and using them to conclude.

Therefore, before one can talk about interpreting data, they need to be analyzed first. What then, is data analysis?

Data analysis is the process of ordering, categorizing, manipulating, and summarizing data to obtain answers to research questions. It is usually the first step taken towards data interpretation.

It is evident that the interpretation of data is very important, and as such needs to be done properly. Therefore, researchers have identified some data interpretation methods to aid this process.

Data interpretation methods are how analysts help people make sense of numerical data that has been collected, analyzed and presented. Data, when collected in raw form, may be difficult for the layman to understand, which is why analysts need to break down the information gathered so that others can make sense of it.

For example, when founders are pitching to potential investors, they must interpret data (e.g. market size, growth rate, etc.) for better understanding. There are 2 main methods in which this can be done, namely; quantitative methods and qualitative methods.

The qualitative data interpretation method is used to analyze qualitative data, which is also known as categorical data. This method uses texts, rather than numbers or patterns to describe data.

Qualitative data is usually gathered using a wide variety of person-to-person techniques, which may be difficult to analyze compared to the quantitative research method.

Unlike the quantitative data which can be analyzed directly after it has been collected and sorted, qualitative data needs to first be coded into numbers before it can be analyzed. This is because texts are usually cumbersome, and will take more time and result in a lot of errors if analyzed in its original state. Coding done by the analyst should also be documented so that it can be reused by others and also analyzed.

There are 2 main types of qualitative data, namely; nominal and ordinal data. These 2 data types are both interpreted using the same method, but ordinal data interpretation is quite easier than that of nominal data.

In most cases, ordinal data is usually labelled with numbers during the process of data collection, and coding may not be required. This is different from nominal data that still needs to be coded for proper interpretation.

The quantitative data interpretation method is used to analyze quantitative data, which is also known as numerical data. This data type contains numbers and is therefore analyzed with the use of numbers and not texts.

Quantitative data are of 2 main types, namely; discrete and continuous data. Continuous data is further divided into interval data and ratio data, with all the data types being numeric.

Due to its natural existence as a number, analysts do not need to employ the coding technique on quantitative data before it is analyzed. The process of analyzing quantitative data involves statistical modelling techniques such as standard deviation, mean and median.

Some of the statistical methods used in analyzing quantitative data are highlighted below:

**Mean**

The mean is a numerical average for a set of data and is calculated by dividing the sum of the values by the number of values in a dataset. It is used to get an estimate of a large population from the dataset obtained from a sample of the population.

For example, online job boards in the US use the data collected from a group of registered users to estimate the salary paid to people of a particular profession. The estimate is usually made using the average salary submitted on their platform for each profession.

**Standard deviation**

This technique is used to measure how well the responses align with or deviates from the mean. It describes the degree of consistency within the responses; together with the mean, it provides insight into data sets.

In the job board example highlighted above, if the average salary of writers in the US is $20,000 per annum, and the standard deviation is 5.0, we can easily deduce that the salaries for the professionals are far away from each other. This will birth other questions like why the salaries deviate from each other that much.

With this question, we may conclude that the sample contains people with few years of experience, which translates to a lower salary and people with many years of experience, translating to a higher salary. However, it does not contain people with mid-level experience.

**Frequency distribution**

This technique is used to assess the demography of the respondents or the number of times a particular response appears in research. It is extremely keen on determining the degree of intersection between data points.

Some other interpretation processes of quantitative data include:

- Regression analysis
- Cohort analysis
- Predictive and prescriptive analysis

**Identify the Required Data Type**

Researchers need to identify the type of data required for particular research. Is it nominal, ordinal, interval, or ratio data?

The key to collecting the required data to conduct research is to properly understand the research question. If the researcher can understand the research question, then he can identify the kind of data that is required to carry out the research.

For example, when collecting customer feedback, the best data type to use is the ordinal data type. Ordinal data can be used to access a customer's feelings about a brand and is also easy to interpret.

**Avoid Biases**

There are different kinds of biases a researcher might encounter when collecting data for analysis. Although biases sometimes come from the researcher, most of the biases encountered during the data collection process is caused by the respondent.

There are 2 main biases, that can be caused by the President, namely; response bias and non-response bias. Researchers may not be able to eliminate these biases, but there are ways in which they can be avoided and reduced to a minimum.

Response biases are biases that are caused by respondents intentionally giving wrong answers to responses, while non-response bias occurs when the respondents don't give answers to questions at all. Biases are capable of affecting the process of data interpretation.

**Use Close Ended Surveys**

Although open-ended surveys are capable of giving detailed information about the questions and allow respondents to fully express themselves, it is not the best kind of survey for data interpretation. It requires a lot of coding before the data can be analyzed.

Close-ended surveys, on the other hand, restrict the respondents' answer to some predefined options, while simultaneously eliminating irrelevant data. This way, researchers can easily analyze and interpret data.

However, close-ended surveys may not be applicable in some cases, like when collecting respondent's personal information like name, credit card details, phone number, etc.

One of the best practices of data interpretation is the visualization of the dataset. Visualization makes it easy for a layman to understand the data, and also encourages people to view the data, as it provides a visually appealing summary of the data.

There are different techniques of data visualization, some of which are highlighted below.

Bar graphs are graphs that interpret the relationship between 2 or more variables using rectangular bars. These rectangular bars can be drawn either vertically or horizontally, but they are mostly drawn vertically.

The graph contains the horizontal axis (x) and the vertical axis (y), with the former representing the independent variable while the latter is the dependent variable. Bar graphs can be grouped into different types, depending on how the rectangular bars are placed on the graph.

Some types of bar graphs are highlighted below:

**Grouped Bar Graph**

The grouped bar graph is used to show more information about variables that are subgroups of the same group with each subgroup bar placed side-by-side like in a histogram. For example, let us consider the subgroups of the average CGPA of students in the mathematics and statistics department of an institution.

**Stacked Bar Graph**

A stacked bar graph is a grouped bar graph with its rectangular bars stacked on top of each other rather than placed side by side. By representing the CGPA example highlighted above using a stacked bar graph, we get the graph below.

In this graph, the rectangular bars representing the mathematics and statistics department are stacked on top of each other with each group representing, first year, second year, and third-year students.

**Segmented Bar Graph**

Segmented bar graphs are stacked bar graphs where each rectangular bar shows 100% of the dependent variable. It is mostly used when there is an intersection between the variable categories.

The segmented bar graph shows the percentage of the average CGPA accumulated by the mathematics and statistics department of the institution.

- It helps to summarize a large data
- Estimations of key values c.an be made at a glance
- Can be easily understood

- It may require additional explanation.
- It can be easily manipulated.
- It doesn't properly describe the dataset.

A pie chart is a circular graph used to represent the percentage of occurrence of a variable using sectors. The size of each sector is dependent on the frequency or percentage of the corresponding variables.

There are different variants of the pie charts, but for the sake of this article, we will be restricting ourselves to only 3. For better illustration of these types, let us consider the following examples.

**Pie Chart Example**: There are a total of 50 students in a class, and out of them, 10 students like Football, 25 students like snooker, and 15 students like Badminton.

**Simple Pie Chart**

The simple pie chart is the most basic type of pie chart, which is used to depict the general representation of a bar chart.

**Doughnut Pie Chart**

Doughnut pie is a variant of the pie chart, with a blank centre allowing for additional information about the data as a whole to be included.

**3D Pie Chart**

3D pie chart is used to give the chart a 3D look and is often used for aesthetic purposes. It is usually difficult to reach because of the distortion of perspective due to the third dimension.

- It is visually appealing.
- Best for comparing small data samples.

- It can only compare small sample sizes.
- Unhelpful with observing trends over time.

Tables are used to represent statistical data by placing them in rows and columns. They are one of the most common statistical visualization techniques and are of 2 main types, namely; simple and complex tables.

**Simple Tables**

Simple tables summarize information on a single characteristic and may also be called a univariate table. An example of a simple table showing the number of employed people in a community concerning their age group.

**Complex Tables**

As its name suggests, complex tables summarize complex information and present them in two or more intersecting categories. A complex table example is a table showing the number of employed people in a population concerning their age group and sex as shown in the table below.

- Can contain large data sets
- Helpful in comparing 2 or more similar things

- They do not give detailed information.
- Maybe time-consuming.

Line graphs or charts are a type of graph that displays information as a series of points, usually connected by a straight line. Some of the types of line graphs are highlighted below.

**Simple Line Graphs**

Simple line graphs show the trend of data over time, and may also be used to compare categories. Let us assume we got the sales data of a firm for each quarter and are to visualize it using a line graph to estimate sales for the next year.

**Line Graphs with Markers**

These are similar to line graphs but have visible markers illustrating the data points.

**Stacked Line Graphs**

Stacked line graphs are line graphs where the points do not overlap, and the graphs are therefore placed on top of each other. Consider that we got the quarterly sales data for each product sold by the company and are to visualize it to predict company sales for the next year.

- Great for visualizing trends and changes over time.
- It is simple to construct and read.

- It can not compare different variables at a single place or time.

As a business owner who wants to regularly track the number of sales made in your business, you need to know how to collect data. Follow these 4 easy steps to collect real-time sales data for your business using Formplus.

- Visit Formplus on your PC or mobile device.
- Click on the
**Start for Free**button to start collecting data for your business. - Register with your email address or with
**Google**in 2 seconds

- Go to the
**Forms**tab beside your Dashboard in the Formplus menu. - Click on
**Create Form**to start creating your survey

- Take advantage of the dynamic form fields to add questions to your survey.
- You can also add payment options that allow you to receive payments using Paypal, Flutterwave, and Stripe.

- Go to the
**Customise**tab to beautify your survey by adding colours, background images, fonts, or even a custom CSS. - You can also add your brand logo, colour and other things to define your brand identity.
- Preview your form, share, and start collecting data.

- Track your sales data real-time in the
**Analytics**section.

**Analytics**

The responses to each form can be accessed through the analytics section, which automatically analyzes the responses collected through Formplus forms. This section visualizes the collected data using tables and graphs, allowing analysts to easily arrive at an actionable insight without going through the rigorous process of analyzing the data.

**30+ Form Fields**

There is no restriction on the kind of data that can be collected by researchers through the available form fields. Researchers can collect both quantitative and qualitative data types simultaneously through a single questionnaire.

**Data Storage**

The data collected through Formplus are safely stored and secured in the Formplus database. You can also choose to store this data in an external storage device.

**Real-time access**

Formplus gives real-time access to information, making sure researchers are always informed of the current trends and changes in data. That way, researchers can easily measure a shift in market trends that inform important decisions.** **

**WordPress Integration**

Users can now embed Formplus forms into their WordPress posts and pages using a shortcode. This can be done by installing the Formplus plugin into your Wordpress websites.

- It helps to make informed decisions and not just through guessing or predictions.
- It is cost-efficient
- The insights obtained can be used to set and identify trends in data.

Data interpretation and analysis is an important aspect of working with data sets in any field or research and statistics. They both go hand in hand, as the process of data interpretation involves the analysis of data.

The process of data interpretation is usually cumbersome, and should naturally become more difficult with the best amount of data that is being churned out daily. However, with the accessibility of data analysis tools and machine learning techniques, analysts are gradually finding it easier to interpret data.

Data interpretation is very important, as it helps to acquire useful information from a pool of irrelevant ones while making informed decisions. It is found useful for individuals, businesses, and researchers.

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