Restaurant Mobile App with Analytics

Analytic POC On Pension Data

SLA Drilldown - Infographic

Document Volume - Infographic

Left Arc - Document Categories with less volumes

Right Arc - Document Categories with high volumes

Customer Complaints - Infographic

Total Number of customer Complaints:



First level of drill down  (To be expanded as you click on respective blocks)








Second level of drill down (To be expanded as you click on respective blocks)






You can add multiple levels of drill down depending on the categories drill down and the extent of analysis ./ visualization required.

Weekly Report Infographics

Heat Map - Visual Representation

Heat map is one of the useful and powerful data-analysis tools available in business intelligence. Heat Map is visual representation of data using colors instead of numbers only. This tool is used to analyze the complex data sets for a quick and easy way of understanding.

Popular heat maps being referred/used in the industry includes

• Election results by geography
• Visitors interaction with a webpage
• Usability or consumer experience

There are many ways to create heat maps but the common understanding in all of these representation is usage of colors to communicate the numbers and their relationships. Heat Maps are mostly used for two dimensional representation. But the advanced heat maps can be drawn for more than two dimensions. For example cell size and color both can be used to represent a different relationships. One can add sliders to filter/zoom the data and its relationships as required by the user. If you have to represent the same situation using a bar chart, the visual would be cluttered and difficult to understand.

Example:

Below heat map used to understand the customer feedback from a service operations. Customer provided his feedback with the services offered by the vendor. Feedback can be positive (Happy with the service provided) or negative (Not happy with the services). Purpose of this heat map is to understand the customer view of the services provided with appropriate filters and drill-downs.

Analytics – Lead Time Prediction

Below approach can be used to predict the lead time of completing a project considering the variation and cycle time distribution from the past data. The results are interpreted considering the probability of interest.

Steps to predict the Lead Time:

• Collect the data from past related to cycle time and data may include the sub divisions i.e., cycle time may include the actual cycle time, hold time, cycle time of multiple steps etc.,

o   E.g., Project of setting up a new server may include purchase, installation steps. While gathering the data we can collect the data at the desired level to capture the uncertainty in each steps while predicting.

  

•        Understand the distribution of the data

•     Calculate the Lead cycle time for multiple probabilities considering the distribution data is following. For this example we are considering that the data is following Normal Distribution. We can use Excel Function NORM.INV (probability, mean, standard_dev) to calculate the Lead time (Predicted) for the project considering different probabilities

• Another way of predicting the lead cycle time is through simulation. Assuming normal distribution generate the normal variables

o   Cycle Time : Mean (31) and Standard deviation (17)

o   Hold Time : Mean (23) and Standard deviation (24)

o   Add up to get the Total Cycle time = Cycle Time + Hold Time

• From this you can interpret that 90% probability cycle time = 103 days
• Probability to complete the project in 60 days = 55%