Monday, 16 September 2013

Do not stifle the questions visualized data raises!

Do not stifle the questions visualized data raises!
Extracting meaningful insights from data to address business needs has benefited immensely from the availability of data visualization  tools that have  data more approachable. Today the proliferation of off-the-shelf tools, which are easy to learn and are web enabled, have democratized the way data is presented and consumed. Tools like Spotfire, Tableau, Qikview have helped breathe life into data. They provide a professional look and feel and give an inherent feel of fidelity of the data that is being visualized, more than when data is simply presented as text .

Well designed and deployed Data visualization many a time could lead the user to a question which could spark the need for deeper insights and which possibly cannot be answered by the visualization software. But before we delve into this problem let us understand the primary goals of data visualization
Essentially good Data Visualization is expected to 

  • Provide the interpretation of the data  
  • Bring in relevance and context to the data  
  • Reveal elusive insights to spark deeper analysis
  • Drive management  by exception  
  • Embed intelligence in the reports 

Recently we had an opportunity to work on a project with the objective to understand and analyze the failures in a telecom network, and the related quality issues which could have direct bearing on customer churn apart from the repair and service costs. The goal was to enhance the quality of service (QoS) of the telecom network provider using predictive analytics. The added expectation was to enable the service manager to take proactive decisions on repairs using machine learning models. 

We developed dashboards for the equipment maintenance manager, using a well regarded  commercial off-the-shelf visualization tool. We also developed the advanced failure prediction model using the input data as the standard telecom equipment log files and used techniques such as  pattern mining and event sequence analysis to predict equipment failure.   R open source programming language was used to create this model.

We had  two options to present this data. In one scenario the maintenance manager used the visualization tool to drill into the failures to locate the regions or equipment models with high failure. This information was then shared with the engineering team for root cause analysis who used our R based model to predict failure. 

The visualized data provided information to act on, but was it intelligent enough to bring in preventive maintenance?

The manager had a number of questions  during such slice and dice analysis, namely - Why is a region doing better than others? Why are some failures more common in one model and a particular region and not the others? Unfortunately, simple data visualization cannot provide answers to these questions and they get stifled. Consequently the service manager hopes that his engineering team will come up with the right answers. Many a time, the well represented and slickly visualised data is “counter-productive” by making the user numb to the questions which could get triggered.

Our approach was to integrate the relevant prebuilt sequence mining models in R and integrate it with the off-the shelf visualization tool.  This approach immediately gave the manager the freedom to ask even deeper questions about the state of equipment he managed.

In the new approach the manager did not send the information to his engineering team for analysis but felt empowered to do the same .  

Once the region associated in the problem got identified, next , running the advanced sequence mining algorithms, identification of the frequently occurring patterns in the repair history were  carried  out.. The patterns in data showed that two components were failing in tandem. While the short term measures would be generally to replace the defective parts, but more importantly the findings were passed on to product engineering team to redesign the part which would be more fault tolerant.

Here is an example of using the power of R aided by the inherent strengths of good visualization tool to build intelligent and  actionable data visualization.The service manager did not have to leave his data visualization  environment nor wait for the engineering team to do background analysis. 

I would like to conclude that in data visualization, there is more to the choice of representation of data only. Data visualization should not make the user numb with its slick representation, but should help  in revealing those elusive insights by goading the user to ask questions which he would have  never asked.   

by Somjit Amrit
Somjit is the Chief Business Officer of Technosoft Corporation, an IT Outsourcing Services Provider
He can be reached at

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