Data visualization is a photographic or graphic presentation of the data. It enables stakeholders to see data analytics visually presented, so that complicated concepts can be grasped or new patterns identified. By using technology to dig in charts and graphs for greater detail and dynamically alter what data you display and how it was treated, you can take the idea a step further with interactive visual representation.

Over several decades, from maps and drawing to the inventing of the pie chart in the early 1800s the idea of using images to interpret the data has been around. Many decades later, when Charles Minard illustrated Napoleon’s conquest of Russia, it was one of the most cited examples of statistical graphics. The map showed the military’s size and way to Napoleon’s departure from Moscow, and related the details to a more detailed understanding of the case on a temperature and time scale.

However, technology actually lit the fire under the visualization of data. Computers allowed vast quantities of data to be processed at fast lightning speeds. Data visualization has now become a rapidly evolving mix of science and art which will transform the business landscape in the coming years. Graphs and other visualizations of data should tell a story, in order to be successful. The relayed knowledge would be useful for the client in making business choices. The viewed data is the interpretation of the results. Better than that. More rapidly. More realistic. It brings harmony to the world!

You should to following things to make data visualization more effective:

Make it Look Good : Aesthetic is important in the visualization of data, but this does not mean that a lot of colours and effects is needed for graphs and charts. There may be fewer, but that doesn’t mean that we should ignore any consequences absolutely. Play one or two effects around to see what is best for you or the viewer to understand the data.

Have Someone Else Take a Look : Even if you are very clearly aware of what you see, look at your graphs and diagrams with another set of eyes; no one can always see it. There may be interdepartmental correspondence needed here. You do not know that event targets are set correctly in your analytics. If the data is not right and patched, contact the IT or DEV team to make sure things are correctly configured. The details you have on your data will be clean and transparent.

Double-Check Your Data : With your results, be cautious. Ask what you see and look at it in as many ways as possible to ensure that you grasp it correctly and how everyone else can see it. You don’t want to confuse someone accidentally and you definitely don’t want to deceive deliberately.

Data alone is intangible, and consists of bits and bytes contained in a file on a hard drive of computer. We need to imagine it in order to see and make sense of details. Data analysis projects involves visualization. It offers a front line of attack that exposes complex data structures that can not otherwise be absorbed. We consider previously unimaginable results and question them.

The representation of data does not mean insight, but the exchange of insight itself.

Quantitative observations based on vast volumes of data are also subtle, surprising and scientific. It makes communicating these perspectives to any audience and in particular to an industry audience who might be interested / tired to go into the technical specifics more difficult because of this. Nevertheless, there are people who tend to view outcomes of more visually pleasing presentations in boring black and white numerical tables and equations, but this number is much smaller.

Common Types of Data Visualizations

Time-series

Line charts : These visualizations are one of the most common and widely used. Over time, they represent changes in one or more variables.

When to use: You need to show how a variable changes over time.

Area charts : A line diagram variation, the area diagrams display many values in a time sequence.

When to use: You need to show cumulative changes in multiple variables over time.

Ranking

Bar charts : These charts are like line charts, but they use bars to represent each data point.

When to use: Bar charts are best used when you need to compare multiple variables in a single timeframe or a single variable in a time series.

Population pyramids : Population pyramids are stacked bar graphs that depict the complex social narrative of a population.

When to use: You need to show the distribution of a population.

Part to Whole

Pie charts : These show the parts of a whole in the form of a pie.

When to use: You want a percentage of sections of a whole. Nonetheless, several experts prefer alternative formats because it is easier for people to interpret the data in this format as the processing time is increased. A bar chart or a line graph is argued more sensibly by others.

Tree maps : Tree maps display hierarchical data in an in-line format. The scale of the rectangles corresponds to the percentage of the entire group.

When to use: These are most useful when you want to compare parts of a whole and have many categories.

Deviation

Bar chart (actual vs. expected) : These compare an expected value vs. the actual value for a given variable.

When to use: For a single variable, you will compare real and expected values. The example above indicates the amount of products sold in each category vs. the number predicted. In comparison to the other categories, sweaters quickly see low standards while skirts and shorts are overperformed.

Correlation

Scatter plots : Scatter plots show the correlation between two variables in the form of an X and Y axis and dots that represent data points.

When to use: You want to see the correlation between two variables.

Frequency Distribution

Histograms : Histograms plot the number of times an event occurs within a given data set and presents in a bar graph format.

When to use: You want to find the frequency distribution of a given dataset. For example, you wish to see the relative likelihood of selling 300 items in a day given historical performance.

Box plots : These are non-parametric visualizations that display a measure of dispersion. The box represents the second and third quartile (50%) of data points and the line within the box represents the median. The two lines extending outside the box are called whiskers and represent the first and fourth quartile, along with the minimum and maximum value.

When to use: You want to see the distribution of one or more datasets. These are used instead of histograms when space needs to be minimized.

Nominal Comparison

Bubble charts : Bubble charts are like scatter plots but add more functionality because the size and/or color of each bubble represents additional data.

When to use: When you have three variables to compare.

Heat maps : A heat map is a graphical representation of data in which each individual value is contained within a matrix. The shades represent a quantity as defined by the legend.

When to use: These are useful when you want to analyze a variable across a matrix of data, such as a timeframe of days and hours. The different shades allow you to quickly discern the extremes. The above example shows users of a website by hour and time of day during a week.

Geographical Comparison

Choropleth : Choropleth visualizations are a variation of heat maps where the shading is applied to a geographic map.

When to use: You need to compare a dataset by geographic region.

Flow

Sankey diagram : The Sankey diagram is a type of flow diagram in which the width of the arrows is displayed proportionally to the quantity of the flow.

When to use: You need to visualize the flow of a quantity. The example above is a famous example of Napoleon’s army as it invaded Russia during a cold winter. The army begins as a large mass but dwindles as it moves towards Moscow and retreats.

Relationships

Network diagram : These display complex relationships between entities. It shows how each entity is connected to the others to form a network.

When to use: You need to compare the relationships within a network. These are especially useful for large networks. The above shows the network of flight paths for Southwest airlines.

Gathering insights from data Visualization is a task which is generally done with inputs from the business decision makers and normally these tasks differ from project to project as each business domain has its own set of requirements for data analysis and insights.

We can set some rules which can trace and put data in earlier specified string format which can be used to give insights as per the requirement of the client. It does not only include recording statistics and answering people, but also reviewing data in the right context so that useful insights can be made. It requires a good management experience and a clear knowledge of our customers. Clear observations made the organization and its problems precise. We configure contact e.g. by using individuals, archetypes, e-books, playbooks, etc., depending on the different players who gain from the insights.

Whatever the case is , insights should be :

Actionable: The relationship or relationships will lead to an action plan being produced.

Measurable: The Action Plan will include a collection of primary performance metrics which can be assessed when used.

Stable: An interpretation must not alter in accordance with the relationship identification / model development algorithm used. For example, if a predictive model of the same model-creation approach is created from two different samples of the same data set, then the resulting models will produce the same result in the same new data input.

Reproducible: Whatever the amount of times you use an insight generation method to feed a specific data set, the same insight is generated.

Robust: The level of insight does not decrease due to any noise from input data. In the Big Data world this is especially important. Insight generation systems must be able, based on a specific context, to organize noisy data and concentrate on data, which makes “sense.”

Enduring: The insight shall be true for a period equivalent to the “half life” of the underlying data.

Concerns on the output of information extractors, including systems that construct analytical models and data used to produce the output, are discovered. The role of informative systems is twofold: the identification by information extractors of interesting relations (intelligences), and the creation of one or more action plans, linked to a relationship or a set. This is not mechanical in its entirety. Instead, domain knowledge (or domain ontology) must be used and domain experts directed.