Over the last few years most enterprises have implemented several (if
not more) large ERP and CRM suites. Although these applications were
meant to have self-contained data models, it turns out that many
enterprises still need to manage “master data” between the various
applications. So the traditional IT role of hardware administration and
custom programming has evolved to packaged application implementation
and large scale data management. According to Wikipedia:
“MDM has the objective of providing processes for collecting,
aggregating, matching, consolidating, quality-assuring, persisting and
distributing such data throughout an organization to ensure consistency
and control in the ongoing maintenance and application use of this
information.” Instead of designing large data warehouses to maintain the
master data, many organizations turn to packaged Master Data Management
(MDM) packages (such as Informatica MDM).
With these tools at hand, IT shops can then build true Customer Master,
Product Master (Product Information Management – PIM), Employee, or
Supplier Master solutions.
MDM solutions vary by industry in terms of tactical approaches taken – e.g., pharmaceutical/life sciences will adopt semi-batch, database-centric approaches for master physician data to be deployed to sales forces, while financial services providers and online retailers will require near real-time, business process-centric solutions to compete in the business-to-consumer (B2C) online world. These different types of implementations require technical IT expertise in delivering an end-to-end solution. Based on quarterly surveys of the MDM Institute Business Council™ (8,000+ subscribers to the MDM Alert newsletter engaged in MDM projects), the perennial top four business drivers for MDM initiatives are summarized as:
(1) compliance and regulatory reporting;
(2) economies of scale for mergers and acquisitions (M&A);
(3) synergies for cross-sell and up-sell;
(4) legacy system integration and augmentation; and
Note that this list represents business drivers, not technical initiatives. Ideally, the business analyst “owns” the data and is responsible for the initial definition of what the master data looks like (whether this is from a custom application or a packaged solution). In addition, they are responsible for the processes (not actual data entry) of inputting the data into the source systems. IT acts as “data stewards” – coordinators between various business groups. IT’s role should be project managers that phase in updates to the primary MDM. These data stewards must be equally savvy in data modeling as well as business processes. IT must also be the technical gurus to glue applications and databases together. This also involves data quality processes, such as standardization, cleansing, validation, enrichment and matching.
Traditional MDM solutions have been implemented on premise, primarily as data hubs to various applications spokes such as Human Resources, PLM, ERP, and CRM applications. With the huge uptick of software as a service (SaaS) CRM providers such as salesforce.com, this requires MDM solutions to integrate data from the cloud.
While an on-premise model works well when most of the data is updated within the “four walls” of the enterprise, a hybrid cloud + on premise model may be better suited to a B2C environment when massive customer updates happen on a seasonal basis. In this case, a hybrid model will allow for extra cloud resources to be tapped in order to increase performance. In addition, with a hybrid model, sensitive data that may be legally prohibited from residing in the cloud can be kept on premise.
Should MDM be completely implemented in the cloud?
In this case, the master data model engine will reside in the cloud and will act as a hub between multiple SaaS applications and potentially on premise applications. A common scenario might be managing customer data between Salesforce CRM, Order fulfillment with UPS services, and on-premise ERP Receivables. Or replace the on-premise ERP solution with a cloud-based ERP such as NetSuite. In these cases, having MDM in the cloud might be the right approach. A cloud-based solution also makes sense for piloting a longer term MDM project. So look for a vendor that provides both on-premise and cloud-based MDM solutions for maximum deployment flexibility.
The Hybrid IT organization continues to evolve with new responsibilities. Cloud-based solutions tend to free up the IT staff from the more routine data center operations to get more involved with business activities such as Master Data Management. IT will play an important role in managing MDM solutions. Although, they don’t “own” the data, the technical requirements for implementing a solution remain in the IT domain. And acting as a data steward to capture the business requirements of what data needs to be managed and formulate the detailed rules and processes will become a key role. IT will also need to decide between on-premise and cloud-based architectures for the enterprise.
—-
Mercury Consulting is a trusted technology advisor with deep expertise in cloud applications. We offer strategic guidance to senior executives to select the right cloud solution and services assistance to help enterprises accelerate their adoption of cloud solutions.
Mike Canniff is a faculty member of Management Information Systems at the University of Pacific – Eberhardt School of Business. He has worked in the Information Technology field for over 20 years beginning with IBM as a software engineer and as Vice President, Development for Acuitrek Software. Mike has specialized his career research in the areas of Enterprise Application Integration and Electronic Commerce systems. He has published several papers on Electronic Commerce and Business Process Management best practices.
MDM solutions vary by industry in terms of tactical approaches taken – e.g., pharmaceutical/life sciences will adopt semi-batch, database-centric approaches for master physician data to be deployed to sales forces, while financial services providers and online retailers will require near real-time, business process-centric solutions to compete in the business-to-consumer (B2C) online world. These different types of implementations require technical IT expertise in delivering an end-to-end solution. Based on quarterly surveys of the MDM Institute Business Council™ (8,000+ subscribers to the MDM Alert newsletter engaged in MDM projects), the perennial top four business drivers for MDM initiatives are summarized as:
(1) compliance and regulatory reporting;
(2) economies of scale for mergers and acquisitions (M&A);
(3) synergies for cross-sell and up-sell;
(4) legacy system integration and augmentation; and
Note that this list represents business drivers, not technical initiatives. Ideally, the business analyst “owns” the data and is responsible for the initial definition of what the master data looks like (whether this is from a custom application or a packaged solution). In addition, they are responsible for the processes (not actual data entry) of inputting the data into the source systems. IT acts as “data stewards” – coordinators between various business groups. IT’s role should be project managers that phase in updates to the primary MDM. These data stewards must be equally savvy in data modeling as well as business processes. IT must also be the technical gurus to glue applications and databases together. This also involves data quality processes, such as standardization, cleansing, validation, enrichment and matching.
Traditional MDM solutions have been implemented on premise, primarily as data hubs to various applications spokes such as Human Resources, PLM, ERP, and CRM applications. With the huge uptick of software as a service (SaaS) CRM providers such as salesforce.com, this requires MDM solutions to integrate data from the cloud.
While an on-premise model works well when most of the data is updated within the “four walls” of the enterprise, a hybrid cloud + on premise model may be better suited to a B2C environment when massive customer updates happen on a seasonal basis. In this case, a hybrid model will allow for extra cloud resources to be tapped in order to increase performance. In addition, with a hybrid model, sensitive data that may be legally prohibited from residing in the cloud can be kept on premise.
Should MDM be completely implemented in the cloud?
In this case, the master data model engine will reside in the cloud and will act as a hub between multiple SaaS applications and potentially on premise applications. A common scenario might be managing customer data between Salesforce CRM, Order fulfillment with UPS services, and on-premise ERP Receivables. Or replace the on-premise ERP solution with a cloud-based ERP such as NetSuite. In these cases, having MDM in the cloud might be the right approach. A cloud-based solution also makes sense for piloting a longer term MDM project. So look for a vendor that provides both on-premise and cloud-based MDM solutions for maximum deployment flexibility.
The Hybrid IT organization continues to evolve with new responsibilities. Cloud-based solutions tend to free up the IT staff from the more routine data center operations to get more involved with business activities such as Master Data Management. IT will play an important role in managing MDM solutions. Although, they don’t “own” the data, the technical requirements for implementing a solution remain in the IT domain. And acting as a data steward to capture the business requirements of what data needs to be managed and formulate the detailed rules and processes will become a key role. IT will also need to decide between on-premise and cloud-based architectures for the enterprise.
—-
Mercury Consulting is a trusted technology advisor with deep expertise in cloud applications. We offer strategic guidance to senior executives to select the right cloud solution and services assistance to help enterprises accelerate their adoption of cloud solutions.
Mike Canniff is a faculty member of Management Information Systems at the University of Pacific – Eberhardt School of Business. He has worked in the Information Technology field for over 20 years beginning with IBM as a software engineer and as Vice President, Development for Acuitrek Software. Mike has specialized his career research in the areas of Enterprise Application Integration and Electronic Commerce systems. He has published several papers on Electronic Commerce and Business Process Management best practices.
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