Procurement Information Architecture Part 3: Analytics

Breaking up the analytics solution for greater design flexibility

Using a multi-part analytics architecture helps break the IT/Procurement logjam and provide more flexibility in vendor selections

In the first installment ofProcurement Information Architecture Part 3: Analytics，我们讨论了分析在整体采购信息架构中的作用。

在第二期分期上，我们将讨论将分析技术堆栈分解为不同的构建块的价值，以便采购，并且可以更好地满足于个人目标。虽然花费分析甚至不是整体供应分析足迹的10％，但这是这个概念的良好插图。这是我的“令人讨厌的采购分析架构”中的“试验分析架构”：

Part 1: The supply data warehouse。Or, a place for your stuff.

这首先是定义提供分析所需的“分析数据模型”（包括主数据，交易数据，外部内容等）来支持需要进行的具体决策。它需要多种形式：

• In basic ‘spend analysis’, it is the ‘spend cubes’ holding transaction history from AP data, PO data, G/L data, P-card feeds, supplier master data, etc., but can also include budget status, external supplier IDs (e.g., DUNS number), parent-child data, diversity status, etc.
• In supply base analysis, the added dimensions of supplier types, risk types, spend types, regulations, supplier KPI types, etc. (pulled from internal systems and external content stores) would be added.
• In working capital analysis, payment terms (i.e., discount rate, net payment date, INCOTERM), external supplier data (e.g., average DSO), etc. would come into play.
• In strategic sourcing analyses beyond spend analysis, the data starts broadening to cost types, material types, commodity codes, market indices, item codes, and a variety of user-defined (or system derived) codes for market complexity, category impact, project-specific attributes, etc.
• Dozens more

*请注意，分析数据模型不应根据各种供应商到位的内容，也不应派生，而且也许，也许是主要的，也许是需要进行的有效和有效的业务决策。That said, procurement information architects should use the data models from packaged applications to help paint the “art of the possible” in terms of broader analysis (e.g., starting to bring in cross reference files such as item-category-supplier to properly the ‘many-to-many’ data relationships that exist within procurement). For companies runningOracle业务应用程序，使用日常商业智能通过拍摄生产应用程序并将其放入一个分析数据商店，将此概念带入生命，并将其坐在顶部的包装报告中。

一个数据仓库不应该是一个巨大的IT驱动的金钱坑的代名词，该坑在电力用户手中笨拙和更随意的用户。But, like the comedian George Carlin said, “you need a place for your stuff”, and IT plays a role in building ‘the plumbing’ from the numerous source systems into the warehouse even if it is essentially a staging area for other applications to do something more useful with the data than just ‘slice and dice’ it. So, after the data has been aggregated, it has to be transformed so that it can be analyzed properly to derive value.

Part 2: Data Transformation…with a big “T”

从使用的许多源系统中汇总数据后，您需要将其呈现成适当分析的形状。当然，这transformationis way easier said than done.Note that when I say “transformation” it is not the low-level data conversion step that is the “T” in “ETL” (Extract, Transform, Load) where source data is pre-processed from operational data stores before going into the data warehouse.

The ‘transformation’ term is the post-aggregation work where the data is cleansed, de-duplicated, enriched with external content, and “harmonized” (e.g., cross-referenced and properly structured/related such as in the case of parent-child relations) before it can be适当地analyzed. You can certainlytryto analyze the databeforeit is transformed, but we don't recommend it other than to highlight the level of source data sparseness, toxicity, etc. that has to be remedied.

*Note, analytics and MDM (Master Data Management) are obviously terrific bedfellows. Analytic projects will quickly highlight your MDM ‘opportunities’ and many analytic applications can even do a fair amount of MDM nativelywithintheir applications to set up a virtual system of reference for the master data and to create more complex data relationships without having to change the simpler/inadequate data model of the source systems.

For example, spend analysis vendorZycus（now a full suite strategic spend management application) created a sister company calledZynapse将许多核心技术从其支出分析解决方案（即分类管理，自动分类）到其MDM产品中的许多核心技术。

更广泛的例子是Informatica– a vendor that went from an analytics-focused Business Intelligence focus to data integration and MDM. Conversely, master data centric vendors in SIM, CLM, catalog management, etc. will find salvation not in low-level data synchronization and workflow, but the analytics that provide much higher business impact, including areas such as next generation search/discovery tools which are essentially a form of a data mining analytic application with a flexible user-friendly front-end.

Within this transformation layer sits many vendors across a spectrum of content players (e.g.,D B那Lexis-Nexis那Cortera那Bureau van Dijk那CVM Solutions那Panjiva和许多其他)和分析供应商(或larger vendors that have analytic modules/capabilities) such as the spend analysis vendors who provide the auto-classification capabilities (whether rules-based or pattern-matching statistically based) needed to properly categorize line-item transaction data into a category/commodity taxonomy. Such vendors include Zycus,Ariba那exporis.（IBM.),Bravo Solutions那SAP,Spend Radar（SciQuest),Rosslyn Analytics， 和别的。

SAP is interesting in that it is looking to tap the collective power of its procurement installed base and find opportunities in supplier risk mitigation and other areas via its Supplier InfoNet solution that aggregates supplier performance data across multiple buyers and marries it up to external content and to its data enrichment and predictive performance algorithms to provide insight not available just within a single company. This was the vision ofOpenRatings（在十年前回到了D＆B），它并不奇怪，其中一些顶级产品现在在SAP的这个地区。

Part 3: Data Analysis...除了低音系统之外

The third part of analytics is essentially真理的那一刻- where the various users determine whether the application is providing insights needed for reporting, discovery, simulation, etc. The idea is to provide mass democratization of the analyticsto all the potential users谁能产生影响andto give them the horsepower they need to truly uncover the opportunities latent within the data. “Big Ass SpreadSheets” (the BASS system) and simple databases (e.g., MS Access) are certainly democratized, but not as powerful as purpose-built analytic applications with strong OLAP capabilities. In spend analysis, the best example of such an application isBIQwhich is now part ofOpera Solutions。

And even ERP vendors are getting into the game.Oracleis a great example with its acquisition of increasing integration ofEndeca,which provides the multi-dimensional taxonomy management, search/discovery, and ‘drill around’ capability that is key at this highest-level portion of the analytics technology stack.

Putting it all together– apply sourcing principles to create your optimal market basket for supply analytics

The whole point in defining the supply analytics area in the three buckets above is to provideflexibility在如何混合和匹配解决方案的方法和我ndividual vendors. Don’t create one giant ‘market basket’ of vendors and choose a ‘winner take all’ approach. Put all the applicable vendors in the hopper and then see which ones optimize the total end user priority (Procurement, IT, and other stakeholders) for time-to-benefit, strategic priority, adherence to standards, cost, etc.

For companies with many back office systems and minimal IT standardization who are early in their procurement journey, cloud-based vendors running the entire stack at a low price point makes a lot of sense. For others who have big ERP and BI infrastructures, they might want to have one of those big vendors run the data warehouse, but then layer on the next two levels with a best of breed provider. Also influencing the mix are other best of breed systems, the complexity and importance of the strategic analytics needed, the budget/time-to-value tradeoff, and other factors. For a discussion of your particular needs, please联系作者directly.

拉回一点，分析是一个有趣的命题。它们是应用最战略性的，需要强大的技术能力（例如，MDM，集成）和领导力，因为它们没有明确的硬盘（即，这是所有'选项值'）。但是，他们是一个完美的地方，因为它们是最简单的集成形式，是一种“复合应用”。他们支持“松散地耦合您的应用程序的原则，而是紧密地整合数据”。换句话说，您可以快速获得基本的分析和运行，然后使用它来突出显示您的数据（以及您的流程和KPI）的罪，然后可以自基础自基础改进和更广泛的分析。

So, analytics are obviously key components of a broader procurement architecture discussion, and as mentioned before, are highly dependent onstrong MDM capability。在本系列的第4部分中,我们将研究the MDM aspect of the procurement architecture and different ways to improve MDM capabilities in order to justify the investment.