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Tips in Getting Started in Data Visualization

Overview

The options and possibilities can seem to be overwhelming for those who are unversed in the art of charting and visualizing data. Organizations with data visualization solutions deployed to help derive value from Big Data find out that there are readily available behind-the-scenes benefits in using these tools. Data visualization can be accessed at multiple points and there are several very intuitive applications and tip sheets that can make the execution of visualizations a lot easier especially for starters. 

Starting Data Visualization

Visualizing data is crucial in making sense out of the huge amounts of it that can now be tapped. In a recent midmarket (those with 2,000-5,000 employees) survey, 80% of the respondents concurred that placing data to good use could tremendously help improve product quality, discover new business opportunities and accelerate decision making. Around 96 percent of those surveyed already have big data projects which are either operational or just starting up. However, with reduced budgets, constrained IT resources and above all, not well-trained data analysts in their rank, many midsize organizations are not so sure where to start.

To begin, we offer the following tips on how to get started and what should the organization do to succeed. These very practical tips include many specific business functions where visualization and analysis data can deliver the best results.

1. Business Case Building

Data visualization is not a one-man show. The survey already cited and identified much successful collaboration between business units and IT as one of the most important success factors in data analytics projects and the absence of this vital cooperation between the two as the most important cause that led to failure.

Ambiguous promises with the improvement of product quality or a change for better customer service are not a sufficient measuring stick to justify an investment in a data visualization solution project. Wanting of moving to data-driven decision making, one should need to think about precisely what are the business advantages of better data analysis would be and how much are the costs. This is not as complicated as it sounds. For example, a data visualization is extremely successful at growing the size of a shopping basket by analyzing former customers’ behavior plus of course other factors and suggesting the up-and-cross sell items that a particular customer is likely to choose. The same kinds of questions could be presented for any facet of a business, namely; engineering, finance, operations, human resources, and even IT. These what-if scenarios are not problematic to compute and they place the need for a data visualization solution on a rock-hard business footing.

2. Data Democratization

Fig. 2: An example of Data Democratization

In most cases, organizations that have found success with Big Data are using data visualization to help make the significance of the data. According to the 
IDG Research survey, respondents say that their organizations are very or rather successful at Big Data analysis, 58 percent have already implemented, or are in the process of implementing a data visualization solution project, and a further 40 percent looking forward to implementing one soon. 

Data visualization solutions are at first designed and developed as a business tool exclusively for enterprise-scale organizations that can pay to employ statisticians and other data scientists qualified of working with sophisticated data analytics. These specialists often worked, and still, do, as in-house consulting groups which make it too expensive, clumsy, and slow for midsize organizations, and therefore must be shunned at all costs. Making serious about data-driven decisions the rule and regulation in an organization, make the data upon which decisions are based available without the interference of any intermediaries, and in a functional configuration. This is an area wherein having the right technology plays a huge role in the organization.

Combining strong analytics and data visualization enable users to quickly and easily explore data and also facilitates the convergence of different disciplines within an enterprise to help in solving a business problem. This suggests that employees don’t have to be knowledgeable in analytics to work with Big Data. The power to make the right business decisions are an integral part of running a business, and adding data visualization solutions to a strong, successful core of analytics makes that easy and quick. Using visual analytics, users can deeply penetrate data to verify a gut feeling, spot patterns, understand trends, or figure out where in the process went wrong. Since these tools deliver results visually, they are considerably simpler to work with and obtain value from than the traditional analysis tools. Using all the data collected, data visualization gives users new outlooks for data analysis, letting them explore more options and makeup more accurate decisions.

3. Do Not Ever Disregard The Need For Speed 

The speed at which a data visualization solution performs is not something that only concerns any IT department. 

Do not ever let a perceived lack of technical skills stop you. Having a well-defined business purpose, consultants can be engaged on a limited basis just to obtain the necessary technical skill needed in getting an IDG Research survey up and running, in addition to a customized training regimen for the user base. This is an economical and much more practical approach than seeking to employ the talent needed, which at times might be hard to attract if the organization is not a large one, much more when the system speed of a midsize organization is wanting.

A system’s speed has had two very realistic business after-effects. 

First, administrators who are attempting to figure out a problem need a system that works in real-time and these administrators tend to be men and women of action. Problem-solving in a business setting is an iterative process where each answer leads to the next question, to the next answer and the next question, and so on. If each answer necessitates an hour of calculation, it is then extremely tough for users to maintain continuousness of thought. They are more likely to abandon a system that requires hours or days of patiently waiting to deliver a useful result.

Secondly, there is another, technically more sound reason why system speed makes a difference. Simply put, a slow system cannot digest the huge volumes of data currently available to midsize organizations. To circumvent this problem is getting to analyze samples rather than the whole data volumes, but unfortunately, selecting samples to accurately represent a larger group of data requires an echelon of expertise that midsize organizations do not always have. It should be always remembered that the value of data visualization is proportional to the number of employees in an organization who can directly work with data, without help from experts. The bottom line here is that a fast system is needed since the slow system will often make visual analytics impossible in midsize organizations.

4. Leveraging The Cloud and Looking Past Bedazzling Visuals 


Fig. 3: Leveraging Cloud for Disaster Recovery – Data Protection Group Discussion | Source: https://thinksis.com/

It should not be necessary for organizations to invest in an on-site system to just gain the benefits of data visualization. Many on-demand solutions are offering quick time to value without causing any burden to the IT department in installing and maintaining yet another system. These will also have a positive financial impact, with no CAPEX costs and at least in most cases lower total costs to the business.

On the other hand, good looks and dazzling visuals can only take so far. There are a variety of report generators that are available that can build graphs, generate impressive charts, and even exceptional dashboards. Though these products do a good job of communicating well what already has been known more effectively, they cannot simply tell what is not known unless these are backed by robust analytical capabilities. At the very least, consider for any ability to drill down into the data easily, to generate charts automatically, and to deliver geo-mapping capabilities.

3 Steps to getting started with data visualization

1. Start with a purpose and end in mind

All data visualization solutions suppose that you have a story to tell and the precise numbers to back it up. Just like any strategic communication, data visualization is a procedure that necessitates starting with a well-defined and clear-cut idea of what is the goal to accomplish.

2. Selection of presentation vehicles

Once a story and purpose have been decided, choose an appropriate tool to showcase the data. There are several options to choose from - some are more complicated than others. Depending upon a selection, any organization can probably produce it in-house or hire an expert graphic or data designer for help. In all of these cases, the bottom line is simplicity.

- Here are a few options;

● Tabling individual values

When the goal is for people to look up individual values or when those values need to be expressed exactly so, tables work the best. 

● Bar Charts

Bar charting aids in comparing two sets of data and are a favored, simple method of telling a story. One of the best in this method is Cole Nussbaumer’s No More Excuses for Bad Simple Charts: A Template.

● Charts 

Fig. 4:  Example of Delightful JavaScript Charts with FusionCharts


Chartings help a lot in processing huge amounts of data quickly. These methods take various forms and are all dependent on the objectives. More about charts types and their uses at SAP Design Guild 

● Graphs 

Graphing works best when the information is contained in the shape of the data, such as patterns, trends, and any exceptions to the standard, or the entire sets of values that must be compared. Data professionals have advised moving away from pie charts as they complicate the mind when it is looking for relationships between data points. 

● Sparklines 

The sparklines are a well-designed method to express a vast number of data points. These are small graphics designed to provide a speedy depiction of numerical or statistical data in a piece of text, taking the form of a graph minus the axis. 

● Infographics 

Infographics are a system gaining popularity as eye-catching and compelling presentations found online, in annual reports, magazines, and marketing campaigns. These are often developed with help of professional designers. Learn more about “Ten Fun Tools to Easily Make Your Infographics”.

3. Telling the story-

From the examples above, it is noticeable that it doesn’t matter which type of visualization is selected and that in effect, visualizations go along a pecking order and format that help target an audience adhere to reasoning and understanding of the purpose.

The presentation must have a;

1. Headline – that explains what the visualization is all about. A direct-to-the-point yet clear statement that sets the stage and draws them in.

2. Background – simply set the context and give a short but concise clarification of why the data is wealth worth looking at.

3. The chart – once a format is appropriately chosen, keep it as simple as it can be and must be clear of “chart junk”. Keep the labels clear, spare data points, and draw the people’s eyes to where they need to go. Learn the gestalt principles of visual perception.

4. Conclusion – Tell the viewer what is the point must be understood.

5. Attribution – Cite the source of the data. It is vital for people who want to know its origin, aptness, and when relevant, the scale. 

Conclusion

Data visualization is increasingly becoming an important element of analytics in the age of big data. Organizations should take full advantage of visual analytics to address several challenges related to visualization and big data. 

Data visualization helps enable an organization’s decision-makers to look at analytics presented visually so that they can comprehend problematic concepts or recognize and pinpoint new patterns. Take the concept of interactive visualization a little further by using technology to drill down into charts and graphs for more detail.

Data visualization is a wise investment for the future of big data.

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About the Author-Writer

The Author is a Non-voice BPO/DAS Tech Services project contractor and a Media Tech writer. Studied engineering in the fields of Mechanical, Electrical and Electronics & Communications Engineering with a Master’s in Business Administration. Experiences encompass Travel, Industrial, and Telecommunications writing, media publishing, and analytics. The Author has been in this field for over 15 years.

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