If you haven't already gotten the power of Endeca Latitude as a sort of uber-BI platform that can sit on top of diverse structured and unstructured datasets, we hope the example we present below will make you a believer. Consider how Latitude can complement a PLM system such as PTC Windchill by sitting on top of design information and enabling new types of data interrogation and discovery by different stakeholders. In a sense, Latitude can become the analytics layer that both sandwiches in and rides on top of PLM and in between other systems that design engineering (and procurement) never had access to before. In terms of business value, this can enable users to optimize their part selection (based on cost, performance, business risk, etc.) and even tweak part specifications to engineer out cost by enabling greater supplier participation.
The dataset combinations that can make sense in this environment will often start with component level design and engineering attribute information in the context of detailed spend detail (e.g., GL extracts, invoice/line level supplier detail), logistics information (e.g., restricted substances from air cargo, DOT violation data) and third-party datasets such as supply risk details from a D&B or BvD or supplier diversity data from a CVM Solutions/Kroll or Supplier Gateway. Users may also opt to bring in detailed supplier performance information based on both systems data (e.g., PPM, escapes, on-time delivery) and qualitative data (e.g., stakeholder scorecards) and combine this and other information alongside part attribute information and possibly even unstructured data sources from the web or behind the firewall.
It's much easier to appreciate the ease of Latitude's dashboard in practice. Imagine logging onto your Endeca desktop and being greeted by reports showing parts with highest spend (trending) along with third-party feeds on pricing data for the highest used raw materials from MetalMiner's new IndX (coming soon). From here, you can quickly drill into information on the homepage (e.g., total metals spend by part family) or begin to search both structured and unstructured information by part family (e.g., bearings, bearing type, by supplier). Perhaps then you might want to refine search results based on the actual PLM-provided part attribute information such as part number, part process controls, part sources, part weight, overall part classification, etc. Or maybe you want to interrogate against technical attributes at the same time, such as pivoting on a dataset and querying total (or selected) spend by interior or exterior diameters, general dimensionality, load characteristics, shapes, thicknesses or lifecycle details like status code (e.g., concept, pre-release, released, obsolete).
At this point, let's just say you want to go back to querying or interrogating the integrated dataset based on procurement and financial metrics such as searching (or sorting) by supplier name, supplier number, supplier region, supplier payment terms, incoterms, PO order currency, D&B SER or Paydex rating, diversity status, etc. With Endeca, such a pivot or change in the middle of a search is not only possible -- it becomes natural to users. It's important to realize in this analysis that in picturing how all of this is possible with Latitude, that my description of various scenarios or even a static screenshot showing it does not do it justice. Seeing the drillability across the integrated datasets is what is so powerful -- in both a demonstration and in practice. This is where Endeca has really nailed it. Anyone who has ever criticized dashboards before would do well to look at what Endeca has before they mutter another word about their limitations.
Endeca realizes that the capabilities of their solutions alone are worthless if users base queries on garbage data. They can apply many of the same cleansing and classification rules and approaches of spend analysis providers from Ariba to Zycus (and every name in between) across a typical 6-8 week initial roll-out, clean-up and classification process. And they apply these rules and clean-up efforts not just to spend data but broader datasets as well (even a rule integrating dynamic third-party feeds).
In short, Oracle now has a completely unique spend analysis capability, one that vastly transcends procurement-led efforts. It's a tool as much for design engineering, supply chain and line of business users as it is for those wearing a strategic sourcing and buying hat. Combined with capabilities such as Oracle Supplier Hub and Oracle Supplier Lifecycle Management, there's no question Oracle will have a huge winner on its hands -- and likely one that stands in a class of its own. Given the head start Oracle has in the market with these multiple solutions which may be combined at a customer site, both best of breed vendors such as Emptoris going down their own MDM, spend analysis and supplier management path as well as larger vendors like SAP, which has a range of capabilities in the area, should begin to rapidly investigate alternatives to bring their products onto the same playing field -- let alone in the same league from the ability to integrate financial, operational, design and other supplier and spend data together in one place.