TAMR may be a relatively new entrant in the stand-alone best-of-breed spend analysis space, having been incorporated back in 2013, but — and this is largely due to the pedigree and experience of its founders and senior team — it’s AI-backed probabilistic machine learning engine is on par with any player out there and it’s spend analytics success at some of the Fortune 500 players that have adopted it is on par with companies that have been doing spend analysis for over a decade.
And while, at first glance, TAMR appears to play in a large spend analytics space, when you zero in, you find that the vast majority of players offering spend analytics are (sourcing) suite providers and ERPs, with few companies focussed only on spend analysis or broader analytics. In fact, upon review of 25 major players, only Analytics 8 Spend View, Rosslyn Analytics, Sieveo, Spend 360, and SpendHQ remain in the stand-alone best-of-breed spend analytics space. Moreover, when you look at larger analytics focussed enterprises with spend analysis offerings, only Opera Solutions and PRGX stand out as most of the suite providers are still offering last generation or acquired solutions.
And even though there are only a few standalone providers and a few suite providers that stand out, TAMR, whose customers are primarily large Fortune 500 / Global 3000 customers, is in a class almost its own. Many of the standalone providers left are focussed on the mid-market and many of the leading analytics companies, like PRGX that focusses on audit recovery, specialize in other areas of analysis. At the end of the day, only Opera addresses the full range of analytics that TAMR does.
TAMR is relatively unique because, with TAMR, you can start with spend analysis and then deploy the same platform throughout the enterprise and marry marketing and social media impact analytics with spend to analyze results and outcomes per dollar of spend on a campaign basis, collect NPD and innovation challenge data and measure the outcomes from that spend on a fine-grained level, and compare investment opportunities against spend reduction opportunities and see which has the better outcome for the enterprise long term.
Like Opera, TAMR is built around advanced probabilistic machine learning algorithms that can work on any kind of data and can identify probably related and duplicate data in any domain. When human experts label a small subset of data elements, related data elements, and duplicate data elements, the algorithms can quickly adapt to the data sets and classification can occur quickly and accurately.
With regards to spend analysis, TAMR has built out a complete interface to their platform in Tableau that allows an analyst to see what has been classified, drill down, and see confidences in addition to standard supplier groupings, spend by category, spend by supplier, etc. The demo drill-down reporting suite is already more extensive than the standard offering from most of the pure-play spend analysis players and the alternate view into mappings and confidences will be more familiar to Tableau users than TAMR‘s built-in UI.
For a deeper dive into the strengths and weaknesses of this new analytics platform, check out the deep dive by the doctor, the prophet, and the maverick over on Spend Matters Pro (Part I).