A leading academic publishing company wanted to improve internal efficiency, enhance their e-commerce business channels, and future-proof their systems to support sustained growth.
Elsevier is one of the world’s largest academic and research publishing companies. They focus primarily on medical and scientific literature, and their catalog includes over 30,000 book titles and more than 2,500 regularly published journals. The company does business with institutional clients like universities, and they sell directly to consumers via their e-commerce website.
MDM for E-commerce
Elsevier recently decided to enhance their e-commerce offering in order to meet the demands of an expanding product base and a rapidly changing marketplace. As part of this initiative, they wanted to introduce a Master Data Management (MDM) tool that would streamline their improvement process and make their internal systems faster and more flexible.
James Carne, Head of Global Product Data at Elsevier, is responsible for the data quality for all products at Elsevier. He describes why Master Data Management became a priority for the organization. “The e-commerce channel is an important part of our business,” he explains. “We are always focused on improving efficiency, and we saw an excellent opportunity to introduce MDM to help achieve that goal.”
Carne and his team began by prioritizing MDM for their product data. They identified two key challenges that the MDM solution would address: multiple sources of product data and improved data consistency.
Multiple Sources of Product Data
The data related to Elsevier’s products were stored in multiple places throughout the organization. Richard Hague is the Lead Data Architect on the MDM initiative, and he describes how the data was organized. “Books and journals are the main products we sell,” he says, “and they were controlled by two separate systems. Our main traditional products had masters in one of our legacy systems, but the newer products in our catalog were mainly controlled by spreadsheets.”
Improved Data Consistency
James Carne explains, “Data was spread in multiple systems. So you had data from two different places for the same product, and those sources complemented each other but they sometimes used different vocabularies.” For example, one data source at Elsevier might identify a product as a “book” while another source might identify it as a “hardback book.” They needed a central location to define rules to make sure the e-commerce platform could present all of the data consistently to their customers.
“We also had timing issues,” Carne says, “where one data source might be getting updated daily, another was updated weekly, and a third was being updated yearly. So which data source do you choose? And how are you supposed to know which one to use for a given application?”
When it came time for the team to choose an MDM solution, Carne says they first considered another PIM platform. “We had already used a different solution from a very large vendor for an MDM initiative around our customer data. But we ultimately decided that we wanted a smaller, more agile vendor for this program.”
Carne and the team also realized that building their own solution would be time-consuming and costly in its own right. They opened up their vendor search to include other MDM-specific vendors like Informatica and Semarchy. “We knew that we wanted an MDM solution to act as a convergence hub for all our product data so that the e-commerce platform could have a single data source,” Carne says. “So we were looking for a system that would work for our specific scenarios.”
After considering their options, Elsevier chose Semarchy as their platform. “When I explained what we were trying to do,” Carne says, “Semarchy got it immediately and they didn’t try and tell us how to build our solution. Elsevier is a very complicated business, and it’s difficult to fit us into someone else’s box. Semarchy didn’t try to do that. They were quite open to being flexible, and they seemed very enthusiastic about the project.”
“With some of the other bigger vendors,” he continues, “their solutions would have required compromises from us in the way we work. Semarchy takes a fair amount of customization out of the box, but we saw that as a good thing since it would work for us exactly how we wanted it to.”
Another important requirement for Carne and his team was that the MDM project did not impact the timeline for the e-commerce initiative. The agile approach from Semarchy to MDM deployment was a strong point of differentiation from the other vendors they considered, so Elsevier decided to move forward with Semarchy and start a Proof of Value (POV) phase.
The Agile Approach to MDM
The POV implementation used both Semarchy Convergence for MDM and the Semarchy Convergence for Data Integration. Working alongside Semarchy resources to learn the tool and build the implementation, Elsevier’s team built a data model that achieved their goals for the initiative.
“The POV cemented our view that the Semarchy team was creative in their solutions,” Carne says. “The answer was always ‘Yes, we can do that,’ and they managed to turn things around and come up with creative solutions to our particular problems quite quickly.”
Richard Hague also says the POV increased the team’s comfort level with Semarchy. “For me,” he explains, “the POV gave us confidence that Semarchy could do a great job of modeling our products and providing the functionality we needed. The fact that we never hit any ‘gotchas’ was a big selling point going forward.”
Elsevier was able to use the model they built during the POV phase as the first iteration of their production data model. They improved on the first iteration based on lessons learned during the POV. This example of the Semarchy agile approach to MDM helped Elsevier get to their first production iteration in only a third of the time required for traditional MDM solutions.
The Elsevier team says they are very happy with the results they have seen with the Semarchy MDM. “Our one data recipient at this point is the e-commerce solution,” says Richard Hague. “I think they are now starting to realize the benefit of us providing them a single feed of product catalog data. They can clearly see value in terms of the lack of integration work and data issues they will have to solve as part of their project. Thanks to Semarchy, they have been able to rely on us to solve those issues for them.”
Hague also talks about the ease of working with Semarchy. “On the convergence platform, it’s very straightforward, and we have been able to build the tool with relatively little assistance. The Data Integration tool is a bit more complicated, and there’s a higher level of skill needed, but Semarchy has been very helpful and responsive as we’ve gone along.”
“The support has been very good,” he continues. “It has been easier dealing with Semarchy than it would have been with a larger organization. For instance, we’ve had some patches created for us in the DI tool, and that would not have happened so quickly if we were dealing with a much bigger company. We would have been in the queue for a six-month wait!”
Real Value for the Business
James Carne talks about how his team has been able to demonstrate the value of MDM on the Semarchy platform. “There was a lot of scepticism at first,” he says. “People wondered out loud if we really needed an MDM system for our product catalog. But today everyone has accepted that it was a good decision.”
Elsevier is now using the Semarchy MDM and DI tools to feed the e-commerce platform a single stream of cleansed and normalized product data. The immediate benefit is that users of the e-commerce website can find what they want more easily, and they see more accurate product details. Carne says the effects they are seeing at this point are only the beginning. “There’s a long tail of benefits to come even after this first e-commerce piece is done,” he says. “As the rest of the business adopts the feed coming from Semarchy, it means better data quality and data consistency across the company.”
Carne is happy with the solution Semarchy has provided. “Because the Semarchy system is so open and transparent,” he says, “we have learned very quickly what it can do. There have been very few occasions where I haven’t been able to provide what an internal customer needs. We’ve replaced all of the custom data sources with a single feed from Semarchy, and this has reduced the overall cost of providing this data to the organization. Our end result is an extensible product master with better data quality capabilities that is faster and more flexible than before. It meets today’s data needs, and it’s perfectly positioned for tomorrow’s requirements as well.”