Enhancing poorly designed dashboard. Displays key performance measures of a fictitious company and improved Visualization of Various sales and profit measures in Tableau.

In my view primary objective of a data visualization tool is to effectively envisage the ability of the dashboard to communicate a story effectively

However, wrong plots have been used to share desired details, Color combination used are not correct, dashboard appears to be cluttered with too many information and dashboard doesn’t seem to have an logical flow.

Let’s assume if a dashboard was created to share insights on Sales and profit using different variables and future sales forecasting.
Dashboard could have category as filter to give a dynamic and user interactive behavior to the story.

Choice of Charts

The choice of charts isn’t particularly exciting as most of these fail to put across an information or insight that would be helpful for whoever is looking at the dashboard. Also, not having followed a thought through approach to making the dashboard, most charts seem to be placed un-tactfully.

What can be done

Problem: Taking cue my assumption in this problem is, I am too busy/lazy to sift through data and what I need is a dashboard to convey what I need to know.

How to improve the design of the dashboard and Comment on choice of charts

Starting point, is seriously what a in-depth analysis done by everyone.

Then we can move to further analysis through graphs. Let’s take some examples.

1. Graph: Let’s assume Quantity shipped by Ship Mode

This data can be better utilized if its to be compared with previous year data if data is available.

If not, quantity shipped by ship mode should be compared with sales or profit showing the final impact. Quantity doesn’t represent actual figure in currency. Effective way should be answering the question of how Sales/Profit is impacted by shipping mode.

2. Tree map of Profit and Sales Category wise

Problem:

· We can see the color selection is not proper as the data in the red field is showing profit and classification in the red color signifies loss.

· Similarly, legends showing data is wrongly identifying data as mentioned above. Legend is particularly not necessary for this graph if it is color coded.

· Simply showing data of sales doesn’t help us understand the relationship of sales and profit.

Improvement suggestion:

· Color selection should be changed to simple Green-gold color or simply to automatic.

· Legend should be removed.

3. Graph: Profitability by Region

Problem:

· Pie chart is not able to distinguish the profitability as we are not able to assess the area in effective way.

· East and West data in the pie chart is not telling which has got the most profitability. Similarly, we are not able to get which region has got least profitability among the central and south region.

Improvement Suggestion:

· Best visualization can be done through Bar graph.

4. Graph: Profit and Discount over time

Problem:

· From the overlapped line graph, it is difficult to understand which line is representing Discount and which one is representing Profit.

· Fluctuation is high in the graph, so in-depth analysis is required. Graph should be analyzed in the quarter basis.

· We are not able to find any trend from data, so it very difficult to conclude anything.

Improvement Suggestion:

· Quarterly bifurcation of data and accordingly comparing the data with profitability.

· More segmentation by type of products can be done so as to find a trend.

5. Graph: Sales forecast by Region

Problem:

· As the name suggest sales forecast by region, we are not able get by line graph showing data by sales across different Category i.e. Misc., office and wood.

· Graph should tell sales for each category for different region and individually.

Improvement Suggestion:

· Graph should be made for each category across different region.

6. Graph: Sales per customer by sub category.

Problem:

· Graph contains too much of overlapped data.

· Line area graph is not the best choice for data visualization.

· Legend field is overlapping the graph.

· Variation overtime can be analyzed for Sales per customer by sub category should be based on order date rather than ship date.

Eg.

Improvement Suggestion:

Bar graph segmented by category and data consisting of Avg sales data.

The Dashboard leaves one clueless about the “Performance”.

Conclusion

Overall the Story this dashboard is trying to convey is not coming out properly because of Wrong use of charts in some areas, with missing legend and information or overuse of legends and visual clutter, In some charts Wrong use of color palette is used which could have avoided, also when we are trying to show compassion between Sales VS forecast vs sub-category vs region etc., these things are not coming out visually hence it is not telling a story on single glance which is the real motive of any Dashboard.