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Bootstrap Market Research: Master Data Management (Results)

As previously noted, I’ve been doing a lot of discussion and data crunching around “Master Data Management” lately – so I’ve “bootstrapped” a little market research project. It’s still a work in process – responses are trickling in – but I thought I might take some time to summarize what I am hearing to date. A document is available for download here … the super summary follows below.

Survey Methodology

Please note: I am obviously not a professional market research firm, so this is is an understandably limited sample. Still, I am hearing some interesting things that may put your own Master Data work in a bit more context.

  • I’ve put together a little survey (download from here) which is intended to take about 15 minutes to complete – that should give you an indication into the amount of rigor and depth I am looking for.
  • Please fill it out and email the result to

I’ve received input from ten companies so far – large and small, with all sorts of ERP systems. If you care to add some information, I’ll thank you in advance, and add it (sufficiently anonymized) to the summary results document (download from here).

Here are some of the findings / observations from the summary …

Master Data Domains

The types of Master Data called out included the usual suspects – Customers, Vendors, Finished Goods, Employees. Others mentioned include Metadata, Packaging / Tooling (components), and Indirect customers (like Payors in managed care, or Buying Groups in B2B). The primary systems in scope included SAP, Oracle, JDEdwards, and QAD, joined by an eclectic mix of legacy systems and point solutions. Secondary systems called out included Siebel, JDA/Manugistics, and ADP (payroll) – plus more legacy / home grown / departmental apps.

Master Data initiatives varied, based on where the “current pain” is – R&D / engineering, CRM / Customers / Contracts / Pricing, and Finished Goods / Logistics were named by different companies as their particular focus areas. Other important considerations were things like geography (North America vs. ROW), and business structure (Enterprise vs. business unit vs. local plant).

A significant determinant of how folks thought about this problem was how their ERP is implemented – in a fully integrated “enterprise” (Finance, Order Management, Supply Chain, etc.) – and/or how the instances are divided (all enterprise, by location (geography) or by business unit).

Note, however, that relatively few respondents are concerned with synchronizing data across multiple instances – a popular callout / feature of some MDM solutions. they will speak of “integration”, but a focus of the conversations were all around quality and process, not synchronization.

An interesting frustration from some of the respondees; the ERP system(s) do not capture all of the required attributes for an item, so these additional details are kept in a separate, siloed system. Easy examples would be specific attributes (like shipping material specifications), but there were multiple instances where [so-called] Master Data is calculated with complex formulas / rationale, so an Excel component is required (typically in the area of pricing / quoting details).

Note: I believe we should consider computation of pricing as a (potentially) complex process that occurs in it’s own transactional / analytical system (aka “the magic gonkulator”). The output is master data – but the calculations don’t belong in an MD system.

Size & Scope of Master Data

Predictably, there was a great variation in the responses – 100s to 1000s of customer, vendors, finished goods. However, the interesting trend was the notation that 10s of people (relatively large numbers, based on size of the company), were “responsible” (i.e. “did some of the data entry”). Could this be why there is interest in MDM and an MDM organization? Apparently, Master Data is often managed like a wiki – everybody is an editor.

Note This is not always “out of control” – companies that have reasonably sized groups are the same ones that speak of metrics and controls. However, few report the existence of a centralized data governance organization (see below).

Most organizations have no metrics in place; a few can speak to “data police”, folks that actively monitor the data looking for issues. Best examples cited included “Health Check measures” (does data fit set of established guidelines / tolerances); vendor audits, and [results of] scrubbing (ex. Name And Address data against external sources).

When asked about the business benefits of a Master Data Management effort, most companies left this blank or said “none”. I generally got the sense that hard benefits are difficult to quantify; notable exceptions seem to come from past pain. Some organizations spoke to inventory reductions and transportation savings – both derived from more accurate supply chain data, which is facilitated by clean, consistent, complete Master Data.

Master Data in the Organization

Many companies keep control / accountability at the functional area. However, companies with “enterprise ERP” implementations (full integration of Finance, Order Management, Supply Chain) typically call out ownership at the Enterprise level. It’s not about the size of the company or the recency of their implementation – it’s the degree of integration within the primary ERP.

Organizational specifics were tougher to get at – depending on how the company managed their master data. Generally speaking, companies that manage Master Data at a functional level (Customer Service, Purchasing, Finance) have organizational clarity. However, folks that say they manage at the Enterprise level had the wispier definitions for Title and Accountability

Of note: centralized MDM teams rarely manage the bigger projects (implementations, acquisitions, or special projects with large MD components) – but they will (out of necessity) participate. None of the respondents look to these organizations / people for project management skills. However, there were some good callouts for the communication / change management skills required for the role, especially where the group has to review implications of adds / updates [of Master Data items] with multiple groups that will/may be impacted.

Scope of Responsibilities

An interesting, consistent set of answers in this area; “Yes, we take ownership and accountability – but no, we can’t measure it”. To be fair, not all companies had that clarity of ownership, but the lack of sharp, clear quality metrics is noticeable. Content, Quality, and Governance are consistent in all of these companies … consistently not-well defined, not well measured.

Positives & Challenges

Funny how best practices in one company are challenges in another. There are two recurring themes throughout the responses; Quality and Complexity. The latter is interesting; this was the first point in the survey where the difficulties of Finished Goods Master Data were raised. Many companies call it out as a large challenge; all of them cite the complexity, the multiple facets (manufacturing, packaging, warehousing, transportation, pricing, costing) and the cross-functional nature

Full Results

The summary results document is available for download from here; I will add a version date on the page and keep it up to date as additional surveys come in.

Questions? Comments? Suggestions? Let me know …

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