What is data presentation and analysis?
Data presentation and analysis forms an integral part of all academic studies, commercial, industrial and marketing activities as well as professional practices. Presentation of data requires skills and understanding of data. It is necessary to make use of collected data which is considered to be raw data. This raw data must be processed to put for any use or application. Data analysis helps in the interpretation of data and take a decision or answer the research question. This can be done by using various data processing tools and softwares. Data analysis starts with the collection of data, followed by data processing. This processing of data can be done by various data processing methods and sorting it. Processed data helps in obtaining information from it as the raw data is non-comprehensive in nature. Presenting the data includes the pictorial representation of the data by using graphs, charts, maps and other methods. These methods help in adding the visual aspect to data which makes it much more comfortable and easy to understand. Various methods of data presentation can be used to present data and facts. Widely used format and data presentation techniques are mentioned below:
- As text – Raw data with proper formatting, categorisation, indentation is most extensively used and very effective way of presenting data. Text format is widely found in books, reports, research papers and in this article itself.
- In tabular form – Tabular form is generally used to differentiate, categorise, relate different datasets. It can be a simple pros & cons table, or a data with corresponding value such as annual GDP, a bank statement, monthly expenditure etc.
- In graphical Form – Data can further be presented in a simpler and even easier form by means of using graphics. The input for such graphical data can be another type of data itself or some raw data. For example, a bar graph & pie chart takes tabular data as input. The tabular data in such case is processed data itself but provides limited use. Converting such data or raw data into graphical form directly makes it quick and easier to interpret.
The significance and importance of data presentation
Data presentation and analysis plays an essential role in every field. An excellent presentation can be a deal maker or deal breaker. Some people make an incredibly useful presentation with the same set of facts and figures which are available with others. At times people who did all the hard work, but failed to present it present it properly have lost essential deals, the work which they did is unable to impress the decision makers. So to get the job done, especially while dealing with clients or higher authorities, presentation matters! No one is willing to spend hours in understanding what you have to show and this is precisely why presentation matters! It is thus essential to have clarity on what is data presentation.
Data analysis helps people in understanding the results of surveys conducted, makes use of already existing studies to obtain new results. Helps to validate the existing research or to add/expand the current research.
Data presentation and analysis or data analysis and presentation?
These two go hand in hand, and it will be difficult to provide a complete differentiation between the two. Adding visual aspect to data or sorting it using grouping and presenting it in the form of table is a part of the presentation. Doing this further helps in analyzing data. During a study with an aim and multiple objectives, data analysis will be required to complete the required objectives. Compiling or presenting the analyzed data will help in overall analysis and concluding the study.
You can have a variety of data which can be used in presentations. Some of these types include :
- Time Series Data
- Bar Charts
- Combo Charts
- Pie Charts
- Geo Map
- Scatter Charts
- Bullet Charts
- Area Chart
- Text & Images
Some of these have been described in brief with an example at the end of this article.
Steps for presenting and analyzing data:
- Frame the objectives of the study and make a list of data to be collected and its format.
- Collect/obtain data from primary or secondary sources.
- Change the format of data, i.e., table, maps, graphs, etc. in the desired format
- Sort data through grouping, discarding the extra data and deciding the required form to make data comprehensible
- Make charts and graphs to help to add visual part and analyze trends.
- Analyse trends and relate the information to fulfill the objectives.
Presentation of data:
- A presentation should have a predefined sequence of arguments being made to support the study. Start with stating the Aim of study and the objectives required to reach the aim.
- Break the objectives in multiple parts and make a list of data to be collected. Noting down the sources of data, form in which data exist and needs to be obtained. Also conducting a primary survey for information which does not exist.
- Form and explain the methodology adapted to carry out a study.
- Data collection through primary survey needs to have well thought of sampling methods. This will help in reducing the efforts and increasing efficiency. Sample size should be given importance and correct sampling technique should be applied.
- Present only the required information and skip the background research to make your point more clear.
- Do not forget to give credits and references in the end and where ever required.
The presentation can be done using software such as Microsoft Powerpoint, Prezi, Google Analytics and other analytic software. It can also be done by making models, presenting on paper or sheets, on maps or by use of boards. The methods selected depends on the requirement and the resources available.
How to present the different type of data – which format to choose
Since there are number of options available while presenting data, careful consideration should be given to the method being used. A basic understanding of the desired result/ form is helpful to choose the correct form of representation. One cannot expect to get liner data from a pie chart, thus basic knowledge and application of different type of presentation methods saves time. Additionally, there should be enough sample available so as to get some meaningful analysis and result. Some of the popular ways of presenting the data includes Line graph, column chart, box pot, vertical bar, scatter plot. These and other types are explain below with brief information about their application.
Secondary surveys form a significant part of data research and primary means of data collection by conducting various studies and making use of existing data from multiple sources. The data thus obtained from multiple sources like Census department, Economics and Statistics Department, Election Commission, Water Board, Municipal Bodies, Economic surveys, Website feedbacks, scientific research, etc. is compiled and analyzed. Data is also required to forecast and estimate the change in the requirement of various resources and thus provide them accordingly. Phasing and prioritization form another important part for the effective implementation of the proposals.
Such presentation of data and information can be either by means of manual hand drawings/ graphs & tables, Whereas much effective and accurate way for such presentation is by means of specialised computer softwares. Different types of charts which can be used for data presentation and analysis.
Bar Charts/Bar Graphs: These are one of the most widely used charts for showing the grown of a company over a period. There are multiple options available like stacked bar graphs and the option of displaying a change in numerous entities. These look as shown in the image below:
Line Chart: These are best for showing the change in population, i.e., for showing the trends. These also work well for explaining the growth of multiple areas at the same time.
Pie Charts: These work best for representing the share of different components from a total 100%. For, eg. contribution of different sectors to GDP, the population of different states in a country, etc.
Combo Chart: As the name suggests it is a combination of more than one chart type. The one shown in the figure below is a combination of line and bar graph. These save space and are at times more effective than using two different charts. There can even be 3 or more charts depending on the requirement.
Most popular and commonly used charts in everyday life:
- Area Chart – It is one of the most popular charts which is used to show continuity across a data set or variable. It is very similar to the line chart and is often used for plotting time series. The area chart is also useful for plotting continuous variables.
- Correlogram – It is mostly used for testing the level of correlation between the given variable of a particular data set. The matrix cells can be colored or shaded for showing the correlation value. The cells which are darker as compared to others have a high correlation value. For example, let’s examine the correlation between weight, cost, sales outlet, established year and others.
- Scatter Plot – Scatter Plot is most commonly used for establishing the relationship between two or more than two variables. In the above dataset, we can create visualizations of items as per their given cost by using a scatter plot with the help of two variables MRP and visibility.
- Stacked Bar Chart – Stacked Bar chart is also a type of bar chart which is used by combining several categorical variables. From our given database, if we want to get the number of outlets on the basis of different variables such as outlet location type, the stacked bar chart will visualize the data in the most appropriate format.
- Bar Chart – This type of charts is used you want to use a categorical and continuous variable together. In our given dataset, if we want to know how many stores were developed in a particular year, then a bar chart is the most preferred option.
- Heat Map– Heatmap is used to find the relationship between two or more variables by using different shades of color. In a heatmap, the first two dimensions are represented as axis and the other dimension by different shades of color. If you want to find the cost of each item on every store, you can plot a heatmap using three variable such as the type of item, price of item and outlet identifier.
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