Our story begins with Farmer Joe who has a beautiful daughter Janet. Farmer Joe decides to bequeath the family decorative pin to Janet on her 20th birthday. Now Janet, in a fit of unbridled joy decides to run around a haystack holding the pin towards the heavens. Suddenly,in a scene far too clichéd to be coincidence, she trips and the pin falls into the haystack…
Now begins the daunting task of “finding the pin in the haystack”. Janet is faced with a dilemma which would be quite familiar to RA analysts the world over – how do we find the pattern which highlights the root cause (or the pin, if you are a farmer’s daughter who goes by the name of Janet) within a world of millions of CDRs.
Of course, the solution is to cut the haystack into smaller cubes and search smaller segments for the pin. Does this sound familiar to you, my RA analyst friend? It should – because this is the way we attempt to find the root cause today. When your system presents you with millions of CDRs (or, God forbid, meaningless summaries), we tend to break them into smaller sets which have seemingly similar patterns. Then begins the back-breaking task of finding the elusive pattern that indicates the root cause – an endeavor that involves quite a few cups of strong coffee, pointless mails and shattered dreams regarding deadlines and analyst efficiencies.
But hey, this is how we do Root Cause Analysis the world over right? We would reduce our effort by managing the problem size right? Well, it gives me great pleasure to say that the winds of change are blowing. Today, I would like to introduce to you to a fundamental paradigm shift in Root Cause analysis which would effectively transform the way we do RA.
Let’s imagine for a second that Janet decides to find the pin by placing a powerful magnet over the haystack. Consider how much time and effort she saves, as compared to breaking the large haystack into smaller stacks. Consider how sure this solution is, as compared to the possibility of not finding the pin even after breaking the haystack into smaller segments.
Now imagine such a magnet for RA. A magnet that presents the analyst with all the hidden patterns in a problem set (discrepant CDRs). Imagine how this would change your day in terms of boosting analyst efficiency, achieving cost efficiencies as a department, being prepared to handle new and upcoming technologies and always staying one step ahead of the curve.
That magnet has a name, and its name is “Zen”. Subex recently launched ROC Revenue Assurance 5, and along-with RevenuePad (which my colleague Moinak would write about), Zen is one of the fundamental pillars of this ground-breaking solution.
Zen is an automated Root Cause Advisory engine which provides, for the first time ever, machine intelligence for pattern identification and presentment. What makes it revolutionary is that the engine is programmed to sniff out patterns with minimal involvement from the analyst. Give Zen two data sets, and it will tell you exactly why some CDRs in data set 1 are not present in data set 2. This also involves telling the analyst what percentage of the total data set can be linked to any particular pattern. Since pictures speak louder than words, here is a sample illustration:
Zen is essentially a data analytics engine for ROC Revenue Assurance 5. Based on the discrepant sets identified as the result of an audit, Zen automatically fires up the pattern analytics engine. As Zen works on identifying the patterns, it also works on linking the patterns to specific CDRs (to ensure that an audit trail would be maintained). Finally, Zen presents the analyst with a comprehensive view of:
- All identified patterns in the discrepant data set
- Distribution of how many CDRs are linked to which pattern
- Historic event indicators to further guide the analyst towards the root cause
Zen is keyed towards two “Intelligent” actions:
- Pattern Analytics
- Analyst Feedback integration
We refer to Patterns as “Areas” and the learning from past investigations as “Reasons”. Why do we need both, you ask? The answer is fairly simple – the same pattern (or Area) might have presented itself for very different “Reasons”. A simple example of Subscriber profile between HLR and Billing might clarify this point.
An analyst, on performing the HLR vs Billing subscriber reconciliation, finds that 20 subscribers on the HLR are not present on the Billing platform. Now, in the absence of provisioning logs, he/she might surmise that this is a simple case of provisioning error and forward the information to the relevant teams.
However, if the same discrepancy is seen next week for the same set of subscribers, it might be prudent to address the possibility of internal fraud as well. Here we see an example where the same pattern (20 subscribers are missing repeatedly in billing but are provisioned on the network) might be due to two distinct “Reasons” – Provisioning Error or Internal Fraud.
Zen helps you tie it together. Reasons are incorporated into the Zen engine based on “Acknowledgments” received from various teams. This helps to ensure that “False Reasons” are minimized. In this manner, Zen becomes a repository of Analyst intelligence to address the world-over issue of Knowledge Management in RA.
Zen is a virtual analyst who never sleeps, eats or goes on vacations. For sure he will never leave the team (taking his accumulated knowledge with him).
In conclusion, I want all of us to take a moment to step into Janet’s shoes. The pin is in the haystack, and the stack is getting bigger and bigger all the time (due to burgeoning volumes and technology/product complexity). The timelines to find that pin are ever-shrinking, and cost reduction is the call of the hour globally.
How is your team planning on finding that pin?