Data analysis and Data Presentation – 6 Differences between data analysis vs data presentation

Data analysis and Data Presentation – 6 Differences between data analysis vs data presentation

Data analysis and data presentation are two words that are related but not the same. The capacity of the researcher to describe, delineate similarities and contrasts, emphasize noteworthy discoveries or data, and extract information or messages from the provided data is referred to as analysis.

In communication studies, data presentation is critical because the aims are made obvious with appropriate evidence. The excellent data presentation allows the researchers to persuade the reader of their findings. The reader of the study document can acquire the trust from effective data presentation.

The difference between data analysis and data presentation is given under the following heading:

The Audience – who is the data for?

The primary audience for data analysis is the data analyst. He or she is the one who manipulates the data as well as sees the consequences. The analyst must operate in tight feedback loops of hypothesis creation, data analysis, and visualization of outcomes.

The audience for data presentation is a different set of end-users, not the analysis’ author. These end-users are frequently non-analytical, work on the front lines of corporate decision-making, and may struggle to draw the dots between analysis and its consequences for their jobs.

The Message – what do you want to say?

The journey to identify meaning in your data is what data analysis is all about. The analyst is attempting to put the jigsaw pieces together. The fundamental goal of data analysis is to assist the audience to comprehend the data and get usable information so that the knowledge gained may be used to make better decisions. A data analyst will, in general, obtain and gather data, arrange it, and utilize it to draw useful conclusions.

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Sharing the solved problem with individuals who can act on the insights is what data presentation is all about. Authors of data presentations must use a goal and point of view to guide an audience through the material.

The Explanation – what does the data mean?

The significance of analysis might be self-evident for analysts that use data analysis tools. A 1% increase in your conversion indicator might signal a significant shift in your research strategy. The analysts’ main problem is to figure out why this is happening.

The task of describing the analytical results falls more heavily on data displays. When the audience is unfamiliar with the data, the author of the data presentation should begin with more basic explanations and context. What is the conversion metric and how do we calculate it? Is a one-percentage-point difference significant? What effect will this modification have on research?

Visualization – how do I show the data?

The two goals of data visualization are discovery and communication. Various sorts of graphs must be used throughout the discovery phase to comprehend the data’s rough and overall information. The communication phase focuses on summarizing the information that has been discovered.

Data analysis visualizations must be simple to build and frequently include numerous dimensions to reveal complicated patterns. Data analysis is the process of visualizing data by converting, modelling, and altering it with the objective of uncovering usable information, informing conclusions, and assisting decision-making.

It is critical that visuals be basic and intuitive when presenting data. The visualization of data using standard visuals such as graphs, charts, infographics, and even animations is known as data presentation visualization. These convenient visual representations of data communicate complicated data linkages and data-driven insights. The audience does not have the patience to figure out what a chart means.

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It should be noted that while data analysis is geared towards discovery, data presentation is geared towards communication.

Goal – what should I do about the insight?

Data analysis is frequently used to propose a better question. Finding better questions leads to fresh insights and greater knowledge of how your company operates. Inspection, cleaning, converting, and modelling data with the objective of identifying relevant information and making conclusions are all part of data analysis insight.

The goal of data presentations is to help decision-makers make better decisions. The ultimate purpose of data presentation is to make facts, patterns, and linkages visible to the reader, not to confuse them. Using the same piece of data several times. Most of the learning should be done through data exploration, reserving the equally challenging chore of communicating the insights and actions that should follow to the next step.

Interaction – how are data insights created and shared?

Data analysis may be a solitary endeavour: analysts must obtain data on their own, integrate data from disparate sources, and dive into the data to uncover insights. Data analysis may be a solitary job that only requires collaboration with others when new insights are discovered and need to be shared.

The presentation of data is a participatory activity. When the insights discovered are shared with others who understand the company environment, the values emerge. The purpose, not the failure of the analysis, is the debate that arises.

How does data analysis relate with data presentation?

Data analysis helps to inform data presentation. It is only when data has been analyzed and a conclusion is derived that data presentation will be possible. On the other direction, the presentation of data helps our audience to have an easy understanding and interpretation of the research outcomes.

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Presentation skills and knowledge of facts are required. It is important to make use of data that has been obtained but is still considered raw data. It must be treated before it can be utilized for any purpose. Data analysis aids in data interpretation and decision-making, as well as in answering the research question.


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