Daily Archives: September 5, 2017

BIQ: Alive and Well in the Opera House! Part II

Yesterday we noted that BIQ, from the sleepy little town of Southborough, that was acquired by Opera Solutions in 2012, is not only alive and well in the Opera House, but has been continually improved since its acquisition and the new version, 5(.05), even has a capability no other spend analytics product on the market has.

So what is this new capabilities? We’ll get to that. First of all, we want to note that since we last covered BIQ, a number of improvements have been made, and we’ll cover those.

Secondly, we want to note that the core engine is as powerful as ever. Since it runs entirely in memory, on data entirely in memory, it can process 1M transactions per second. Need to add a dimension? Change a measure? Recalculate a report? It’s instantaneous on data sets of 1M transactions or less. And essentially real-time on data sets of 10M transactions. Try getting that performance from your database or OLAP engine. Just try it.

One of the first big changes they made was complete separation of the engine from the viewer. This allowed them to do two things. One, create a minimal engine footprint (for in-memory execution) with a fully exposed API that allowed them to create a full web-based SaaS version as well as an improved desktop application and expose the full power of the BIQ engine to either instance.

They used QlikView for the web interface and through this interface have created a collection of CIQ (category intelligence) and PIQ (performance intelligence) dashboards for just about every indirect category and standard performance category (supplier, operations, finance, etc.) in addition to a standard spend dashboard with reports and insights that rivals any competitor dashboard. In addition, they have exposed all of the dimensions in the underlying data and measures that have been programmed and a user can not only create ad-hoc reports, but ad-hoc cross-tabs and pivot tables on the fly.

And they re-did the desktop interface to look like a modern analytics front-end that was built this decade. As those who saw it know, the old BIQ looked like a Windows 98 application, even though Microsoft never built anything with that amount of power. The new interface is streamlined, slick, and quick. It has all of the functionality of the old interface, plus modern widget that are easy to rearrange, expand, minimize, and deploy.

One of the best improvements is the new data loader. It’s still file based, but supports a plethora of file formats, can be used to transform data from one format to another, merge files into a single file or cube, picking some or all of the data. It’s quick, easy, user friendly, and can process massive amounts of data quickly, letting users know if there are errors or issues that need to be identified almost immediately.

Another great feature is the new anomaly detection engine that can be run in parallel with BIQ, built on the best of BIQ and Signal Hub technology. Right now, they only have an instance fine tuned to T&E spend in the procurement space, but you can bet more instances will be coming soon. But this is a great start. T&E spend is plentiful, a lot of small transactions, and hard to find those needles that represent off policy spend, off contract spend, and, more importantly, fraudulent spend. Using the new anomaly detection feature you can quickly identify when an employee is flying business instead of coach, using an off-contract airline, or, and this is key, charging pet kennels as lodging or strip club bills as executive dinners.

But this isn’t the best new feature. The best new feature is the new Open Extract capability that provides true open access to Python-based analytics in BIQ. The new version of BIQ engine, which runs 100% in memory, includes the python runtime and a fully integrated IDE. Any analyst or data scientist that can script python can access and manipulate the data in the BIQ engine in real time, using constructs built specifically for this purpose. And these custom built scripts run just as fast as the built in scripts as they run native in the engine. For example, you can run a Benford’s Law analysis on 1M transactions in less than a second. And building it in python, and the Anaconda distribution in particular, means that any of the open source analytics packages for Continuum Analytics can be used. There’s nothing else like it on the market. It takes spend analysis to a whole new level.