“An operating system for a self-aware quantifiable predictive enterprise” definitely gets the prize for the most intriguing presentation subtitle, for an afternoon session that I went to with Surendra Reddy and David Chaney from Quantiply (a stealth startup that has just publicly launched), and their customer, a discount brokerage service whose name I have been requested to remove from this post.
Said customer has some significant event data challenges, with a million customers and 100,000 customer interactions per day across a variety of channels, and five billion log messages generated every day across all of their product systems and platforms. Having this data exist in silos with no good aggregation tools means fragmented and poor customer support, and also significant challenges in system and internal support.
To address these types of heterogenous data analysis problems, Quantiply has a two-layer tool: Edge Cloud for the actual data analysis, which can then be exposed to different roles based on access control (business users, operational users, data scientists, etc.); and Pulse for connecting to various data sources including data warehouses, transactional databases, BPM systems and more. It appears that they’re using some sort of dimensional fact models, which is fairly standard data warehouse analytical tools, but their Pulse connectors is allowing them to pour in data on a near-real-time basis, then make the connections between capabilities and services to be able to do fast problem resolution on their critical trading platforms. Because of the nature of the graph connectivity that they’re deriving from the data sources, they’re able to not only resolve the problem by drilling down, but also determine what customers were impacted by the problem in order to follow up. In response to a question, the customer said that they had used Splunk and other log analytics tools, but that this was “not Splunk”, in terms of both the real-time nature, and the front-end user experience, plus deeper analytical capabilities such as long-term interaction trending. In some cases, the Quantiply representation is sufficient analysis; in other cases, it’s a starting point for a data scientist to dig in and figure out some of the more complex correlations in the data.
There was a lot of detail in the presentation about the capabilities of the platform and what the customer is doing with it, and the benefits that they’re seeing; there’s not a lot of information on the Quantiply website since they’re just publicly launching.
Update: The original version of this post included the name of the customer and their representative. Since this was a presentation at a public conference with no NDA or confidentiality agreements in place, not even a verbal request at any time during the session, I live-blogged as usual. A day later, the vendor, under pressure from the customer’s PR group, admitted that they did not have clearance to have this customer speak publicly, which is a pretty rookie mistake on their part, although it lines up with my general opinion on their social media skills. As a favor to the conference organizers, who put a lot of effort into making a great experience for all of us, I’ve decided to remove the customer’s name from this post. I’m sure that those of you who really want to know it won’t have any trouble finding it, because of this thing called “the internet”.