20 years hence, we stand in the middle of high technology machines. There are machines everywhere, even in the meeting rooms. The attendees carry their ideas in their latest gadgets, present them on everybody’s screen with a single click, the discussions are almost non-verbal with fingers tapping quickly, meetings are held across the globe, all through the connected intelligent systems, and the decisions are taken.
There is a lot which has changed. But there is something that remains same in its fundamental functionality too- the charts. Earlier, the charts were shared on papers and slides, now those are drawn with different tools on intelligent machines- laptops, mini gadgets, and smart phones. But there still hasn’t been a replacement to the chart system when it comes to conveying the thoughts, especially the numbers, comparisons and checklists.
So, the charts have their own effectiveness for businesses. The decisions are taken based on the facts shown on charts which are tool designed, even automated sometimes. And many a times, the business decisions taken prove to be terribly wrong! So, when the business decisions go wrong, do the representations on chart owe the discredit?
Actually, they do… Here are the most common misrepresentations that deal to fatal decisions, owing to the lack of clarity into business standing.
1. Incorrect Choice Of Colors:
When designing tools are used for creating a chart, there is a choice of at least million colors on the screen. Usually, the designer tends to pick the most attractive ones which create a visual impact. These colors however are critical in creating a distinction between the parameters. Rather than the most attractive, focus should be on enhancing the distinction which can bring clarity to the subject. Also, the color blind users should be considered. This takes one back to the era when the selection of colors used to be mild, distinct and limited.
2. Wrong Usage Of Pie Charts:
A pie chart is a very efficient way to clearly bring out the comparison between many factors. Now, imagine a pie chart where the pie is represented by a thin line and the chart as a whole consists of many thin lines one after the other! There is nothing more confusing and dissatisfying than such a piece of pie. This signifies one major flaw- over flow of information, which shrinks down the pie to a single line.
Whenever drawn, the pie chart must consider the limited data sets. Instead of mixing two data sets, one must be represented to compare parts of it. In fact, the choice of a pie chart is good whenever the data sets to be handled are fewer. A good way of comparison is to order the slices of pie from largest to smallest.
3. Information Clutter:
Are you clear what you want to convey as a single precise point? Every day, across the world, millions of businesses feel that the meetings they organize regularly deviate from the track, and the purpose of the entire formal get-together gets defeated. Be careful that the topics being covered in your representation are completely relevant to the subject. Any unnecessary detail may confuse the audience, or worse, force the entire flow of the meeting in an unwanted direction. The limited information with the needed structural representation is an extremely efficient tool in conveying the message.
4. Inefficient Design:
In Steve Jobs words, “Design is not what it looks like and feels like. Design is how it works.” A beautiful design that is incapable of conveying the idea in clarity and entirety is ineffective. Business demands effective design, not the beautiful design. Another key factor to designing is the communication skill. A developer who knows what s to be conveyed will be able to prepare the visualization that is effective. Behind unattractive colors, there would be substantial information which is vital to the business. And hence, the design passes the test.
5. Data Issues:
After all, the complete scenario ends at the most important parameter that the business is interested in- Data. So, what if the data presented in the visualization is wrong? The Bad Data is not endurable. Any visualization must be thoroughly checked for the data issues before its presentation. Wrong information is no way related to data visualization, but can force all the designing efforts to drain if the data is incorrect.