As part of my previous post, I had explained the benefits that Utilities & Telcos can experience if they leverage the power of Big Data towards making their Business Processes & the participating systems more efficient. As part of this blog, I will be concentrating on the first of the 2 variables (i.e.) the Business Processes and how Big Data can help make them efficient.
To justify my stance, I will be concentrating on the following 4 components of a GIS driven Business Process:
- Business Process Flow: To identify phases of the Business Process that are potential bottlenecks.
- Data Quality: To identify common data errors which might be resulting in lengthy digitization cycles.
- Back-end Tasks: To identify any back-end processes which might be delaying the Business Process cycles & lastly
- System Integrations: Time consuming integrations, Web-services (for example) , which invariably elongates the time taken for Business Process to be completed.
With that in mind, I would like to propose that we investigate the title of this blog by using a use case, an imaginary one. Here is how the story goes:
Client A (an Electric Utility) has a few GIS driven Business Processes and they have invested in the Big Data based monitoring tool.
Now, before going into the “how” of it, I would like the reader to know that the Monitoring tools that I have mentioned in this blog are capable of tracking everything that is happening within the GIS system. So they are capable of tracking things like:
- How much time a particular Business Process has spent on a particular phase (Ex: how long has a given New Connection business process been in the In Design phase?)?
- What are the errors that are being reported frequently and for which objects and in which Business Process?
- How much time does the execution of a given batch process/web services takes and how many times it has failed? and so on.
Lastly, just a FYI, the dashboards that I have used in the following sections were created using Diagnostics running on top of GE Smallworld. Also, I have tried to keep the explanations as minimal as possible. This is because I have this slogan which I go by:
“If you have to explain a dashboard, it is not much of a dashboard”
So the assumption here is that these dashboards are self-explanatory and the reader, regardless of his system know-how, should be able to make sense out of them without assistance.
Enough chit-chat, lets look at how Client A is able to make their Business Process efficient using these monitoring tools.
Step 1: Knowing where to look
The first step towards making their Business Processes more efficient is to know the following 2 things:
- Which are the most used Business Processes?
- Which of the most used Business Processes have long completion cycles?
To do this Client A opens up Diagnostics and fires up a queries to create dashboards to identify the most active and the most time-consuming Business process. They then use these dashboards to identify the Business Process that they want to Focus on. In this case it is the New Connection Process (refer slides below).
Insights gained from above two dashboards helps Client A to get an understanding on the Business Process that they need to focus on. Since the analysis phase consists of cumulative insights, I am going to keep a track of the insights obtained in a notepad as seen in the adjoining image.
Step 2: Where are the bottlenecks in the New Connection Business Process?
Once Client A understands where to look, the next step is for them to understand which of these. Here is how it looks:
The above dashboard provides details on the average time spent on each phase of the New Connection Business Process. From the above dashboard Client A can conclude that the bottlenecks are possibly present in either the “In Design” (or) “Post Pending” phase of the business Process. Lets update this insight to our notepad and move on.
Step 3: Identify Bottleneck Causes
Having identified that the In Design & the Post Pending phases of the Business process are where most of the time is being spent, narrows down the scope of potential bottleneck causing candidates. We can sum them up as follows:
In summary, there are 3 prime candidates to investigate for Client A to understand what might possibly be contributing to the performance bottleneck in their New Connection Business Process. Lets make a note of this in our notepad and move on to investigate these candidates one by one.
Candidate 1: Data Validations Checks
Data errors are one of the leading contributors to lengthy digitization cycles. Ensuring that the users make minimal data errors during digitization not only helps shorten the digitization periods but also contributes directly to the confidence that a Utility might have one their GIS data.
So, to identify get insights on the QAQC errors related to the Business Processes, Client A fires up the Data Errors & Validation dashboards & focuses on the ones with a significant contribution to the performance pie. This is how it would look like:
Dashboards such as the one provided above, helps the client to identify what are the most common type of errors that are being reported on the digitized data. They also help provide the first actionable insight for the clients. In this case the action item can be for Client A to provide training to their network engineers focused specifically around reducing the Phase & Voltage Check related errors.
We add this to our notepad and move on to the next candidate.
Candidate 2: Back-end Processes (or) Jobs
Next up Client A turns their attention towards the scheduled jobs (or) batch processes which gets executed as part of the transition of the design. To get insights into the possible bottlenecks they create the following 3 dashboards using the monitored data from Diagnostics:
Again focusing on the ones that occupy the top slots in the dashboard they understand that the New Connection process has a lot of errors being reported while triggering the Generation of PDF & Submission of BOQ (Bill of Quantity). They also understand that most of these errors are being reported while Posting the data and while requesting the Approval of the designed network.
We make a note of these insights and move on to the last candidate.
Candidate 3: Integration related Web-Services
Lastly, Client A looks at the following integration related dashboards available in Diagnostics. Narrowing their focus on the top contributors they arrive at the following insights:
- Link assets, one of the most frequently used web-services, takes a lot of time to execute.
- The load balancing among web-servers is not optimal.
They make a note and wrap up their analysis mode (final notepad provided in the next section).
Analysis Outcome
Looking at their “Analytical Notepad” Client A now knows exactly what they need to do in order to make their New Connection Business Process efficient. Here are the action items that they can focus on:
- Reduce Digitization cycles by educating the users on how to avoid the Phase & Voltage related errors.
- Reduce Backend Process timelines by focusing on making the jobs associated with PDF Generation & BOQ Submission efficient.
- Diminish Integration Bottlenecks by making the Link Asset web-service efficient and by optimizing the load balancing configuration on their Web-servers.
Summary
Big Data driven monitoring tools can help deliver actionable insights using easy-to-understand, data driven dashboards. Utilities & Telcos can leverage these insights to pin-point possible performance bottlenecks to focus on in an effort to make their Business Processes efficient.
Simplifying Where 