Tag Archives: Analysts
The Benefits of Product Information Management, by Andy Hayler, CEO of “The Information Difference”
A recent survey by The Information Difference of well over 100 large organisations found that, on average, they had nine separate systems providing competing sources of product data (13% of respondents had over 100 sources). As can be imagined, that diversity of product data creates headaches for anyone trying to measure business performance, e.g. “what are our most profitable products?” is an easy question to ask but a tough one to answer if no one can agree what a product is, or into which category it is placed.
It also presents operational problems: if you are a retailer who has high street stores, a print catalogue operation, and also an eCommerce web site, then how are you to ensure a consistent process for onboarding and updating product information if different parts of the business have different systems and definitions? Customers that see a special offer online will expect that offer to be available in a store or vice versa, and will not be happy if it is not. There are further issues with eCommerce compared to a retail store: in a store customers can touch and see a product, so online they need more detailed information in order to have the confidence to purchase, such as detailed images of the product and its specifications.
Various phases of application consolidation, including ERP, have failed to improve this situation. Master data management has evolved as a discipline and technology to provide dedicated hubs of high quality data in an enterprise that can serve other systems as needed. It may be impractical to switch off all those legacy systems, but you can put in place a new hub for your product data where the data is trustworthy. This can then be linked directly back to other systems, either in batch or in real time via a web service, so that new product data, when updated in the master data hub, can be immediately used in other systems such as an inventory system or eCommerce web site.
There are various approaches to master data management: some technologies are designed to deal with all kinds of different master data (customer, product, asset, location etc.) while others specialise in a particular data domain, such as product or customer. There are reasons why specialising can make sense. Product data is much more complex than customer name and address data, with materials master files often appearing in unstructured files. Such data needs to be parsed and structured and then validated, requiring different approaches to those used to handle address data. Moreover the classification of products can be complex, with something like a camera having a large number of components and options, so systems to handle product data must be strong at handling complex classification hierarchies.
One example of a company confronting this issue is Kramp, Europe’s largest wholesaler of spare parts for motorized equipment. With 2,000 suppliers they used to take weeks to transfer new product data from suppliers into their internal systems and its eCommerce hub. By implementing a product data hub they were able to radically streamline this process, allowing suppliers to interact directly with the product data hub, and for this data to be consistently updated in the systems that need it, without need for time-consuming interactions with the suppliers to discuss particular data formats. This has led to higher margins due to being able to take advantage of “Long Tail’ niche items, lower process costs and quicker reaction time, important in new markets.
Improved multichannel processes, such as in this example, are why more and more companies are evaluating master data management solutions in order to finally tackle the issue of inconsistent product data. The evident benefits that such improvements bring means that businesses see real, quantified benefits, and why master data management is arguably the fastest growing enterprise software segment right now.
The Siperian team is back in the office after attending the Gartner MDM Summit in Los Angeles last week. As usual, it was a great event for picking up on the latest developments in the field, meeting the analysts who cover MDM, and hearing from the organizations and endusers in the field who are using MDM to resolve their business challenges.
The topic getting the most buzz at the Summit was the shift towards “multidomain MDM.” We heard this from both analysts and vendors, and we were pleased to hear Siperian mentioned frequently in presentations as a vendor that “knows how to do multidomain MDM.”
Of the delegates I interacted with, and there were 330 in attendance total, the consensus was that the analyst presentations and advanced use cases from firms with MDM implementations were real highlights. Among these were case studies presented by Siperian customers Cephalon and Johnson & Johnson. Johnson & Johnson was also highlighted as a past Gartner MDM Excellence Award winner.
Some general notes from the conference:
• Gartner expects the market for MDM to grow from $1.1 billion (in license revenue) in 2008 to $3 billion in 2013.
• North America is still the largest market for MDM (over 50%) followed by Europe (35%).
• IBM, SAP and Oracle control 40% of the MDM market, while specialists like Siperian control 30%.
• Analysts are saying that there’s a “land grab” on for multidomain MDM, but most vendors have yet to span master data province.
Siperian news from the conference:
• John Radcliffe presented the CDI magic quadrant and mentioned Siperian as a “good best-of-breed company.”
• Siperian was recognized as a “multidomain MDM” player in the analyst charts, and was also mentioned as one of the top 5 vendors that can handle customer data.
• Siperian was named a “company to watch” for product data.
• Siperian receives the 4th most inquiries from prospects behind SAP, Oracle, and IBM.
• As a platinum sponsor, Siperian had 4 customer sessions and an evening cocktail session as well. For everyone who attended our events: Thanks!
Many of our customers express frustration that even though it is quite obvious how their business suffers from poor data quality, they find it difficult to convince their associates to invest in initiatives that correct the problems.
Earlier this year, we participated in Rob Karel’s Forrester research that addresses this issue. The resulting research paper is titled “A Truism for Trusted Data: Think Big, Start Small” and its getting a lot of interest. Recently, there was a nice writeup in Intelligent Enterprise where they interviewed Rob and also made mention of the Data Quality ROI calculator that we’ve developed by working alongside our customers.
The article states
While Forrester is often suspect of vendor-supplied calculators, the research firm lists Informatica as an example of a vendor that has taken an approach that matches Forrester’s bottom-up strategy. The Informatica Data Quality ROI Calculator enables customers “to capture and visualize the benefits of a data quality investment ” before that investment is made,” Forrester said.
The report is available from Forrester’s web site. It contains some nice examples of how customers have built a business case for justifying investment in Data Quality. If you would like to share some previous successes in building a Data Quality ROI, feel free to post a comment!