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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 

  1. Provide the interpretation of the data
  2. Bring in relevance and context to the data
  3. Reveal elusive insights to spark deeper analysis
  4. Drive  management  by exception
  5. 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 [email protected]

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Comment by Wayne G. Fischer, PhD on September 20, 2013 at 6:55am


But I think it is more clear (and more useful in thinking about and doing analyses) to say effective graphical analysis of data *reveals* the patterns, structures, etc. (the information) rather than "interprets" or "gives insights."  Interpretation is the next step in the discovery process.

In my experience, only the SME can interpret..."see" and understand the insights in the context of the process from which the data came.  Graphical displays can not interpret, can not point out insights - since they are inanimate and have no understanding of that process upon which to base any interpretation or insight.

[Interpret: to give or provide the meaning of; explain; explicate; elucidate.]   :-)

Comment by Somjit Amrit on September 19, 2013 at 8:53pm

Wayne ,

Let me clarify the difference between "interpretation of data" and "interpretation of results". While "interpretation of results", means understanding the results of visualization/analysis in the specific context of the domain/vertical, "interpretation of data" means identifying patterns in data from the visualization.

For example, a simple histogram can provide understanding the distribution of the underlying the data. Interpretation of data is a necessary step even before using algorithms to analyze data, and much before interpreting results in the context of the specific domain.

Yes, you are right . The interpretation of results cannot be effective without the domain context provided by the SME. However, SME alone cannot interpret results without being given the insights extracted from a multidimensional complex data. Insights obtained from the interpretation of data from analysis are further interpreted by SME in the context of the specific domain to transform insights to actionable insights.

Thanks for your feedback , it is definitely enriching and sharpens our way of approaching the business problems.




Comment by Wayne G. Fischer, PhD on September 18, 2013 at 11:38am

I must politely, but firmly, disagree on your first two points.  Neither data visualization, nor numerical analyses of data, can ever provide *interpretation* of the data (and I think you mean "interpretation of the results"...from whatever visualization is used).

Only the subject-matter expert can provide that, and that (in part) is because the SME knows the *context* of the data - the visualization does not.

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