Tag Archives: ravi shankar

Thoughts On Proactive Data Governance From Dan Power

Through his many speaking engagements and regular contributions to publications such as Information Management, Dan Power has built a solid reputation as one of the most knowledgeable commentators on all things in master data management. He’s got a new white paper titled “When Data Governance Turns Bureaucratic: How Data Governance Police Can Constrain the Value of Your Master Data Management Initiative” that we’re featuring on the Siperian website.


Dan observes that while many early adopters of MDM expected to quickly establish a “single source of truth” across their information systems, many have encountered problems. The culprit? Reactive data governance.


As many as 80% – 90% of companies implementing MDM start with a “coexistence” architecture whereby front office applications (CRM) and back office applications (ERP) are still used to author master data (customer and product data, suppliers, employees, etc.). Because these applications remain the “Systems of Entry”, while the MDM hub’s role is limited to being the “System of Record,” some of the biggest promises of MDM remain unrealized.


Dan shows that firms embracing a proactive data governance approach can overcome these limitations. By authoring data directly in the master data management hub itself, firms can decouple data entry from the traditional CRM and ERP systems. When the System of Entry and the System of Record are one and the same, the application architecture is simplified quite a bit. The CRM and ERP systems become consumers of master data only – they no longer originate it.


Read Dan’s new white paper to learn about the shortfalls of reactive data governance and how proactive data governance can benefit you. You can download the white paper here.

Posted in Data Governance | Tagged , , , , , , , , , | Leave a comment

Technology as an Enabler for Master Data Governance – Five Things To Look For In An MDM Solution

No two companies approach master data governance the same way, because master data governance requires a unique approach within every organization, even among companies within the same industry. A customized approach is necessary because master data governance is closely tied to a company’s business processes, culture, and IT landscape. For the same reason, every company thinking of embarking on a master data governance initiative needs to go through a well-planned design phase in order to create a program that fits the distinct requirements of the organization.

Moreover, when it comes to execution, you need master data management (MDM) technology to bring that design vision to fruition. And not just any MDM technology, but one that’s flexible enough to support your custom design. If the MDM platform is rigid in its functionality—for instance, perhaps it supports only a fixed data model—you might have to compromise your data governance design to adapt to the limitations of the technology. So, what capabilities does your technology platform need in order to support your data governance design?

There are five broad requirements that any MDM platform should support out-of-the-box. To wit, your MDM platform needs to be able to:
1. Define your unique business data using a flexible data model.
2. Infer relationships across your data, such as between customer and products.
3. Support creation of master data directly on the MDM platform (“system of entry”).
4. Support centralized and federated MDM architecture.
5. Configure (rather than customize) data quality, data consolidation, and data governance rules.

Fundamental to these five requirements is the ability to capture rules and metadata. Since your policies cover your business processes, you want a rules-based system that can be easily used by a business person to capture the policies. In the absence of a rules-based system, you are going to have your programmers writing code, which is not an efficient way to implement data governance.

Similarly, you want all changes to your data captured as metadata. In the absence of metadata, you cannot prove the reliability or the origin of the data. When this happens, your users will not use the data because they can’t trust it, ultimately causing your data governance initiative to fail.

Posted in Data Governance | Tagged , , , , , , , | Leave a comment

Device Recall Regulatory Compliance – A Challenge for Medical Device Manufacturers

Medical device manufacturers may be facing new and emerging regulation on the state and federal levels, but FDA recall rules remains the industry’s most immediate day-to-day compliance challenges. With device recalls, the FDA gives manufacturers some leeway in alerting customers. Companies can use the means of their choice in alerting device users, whether via email or snail mail or mass media. The rule is, however, that at least 80% of the users must be contacted. Fail to reach that threshold, and you have to contact users directly over the phone—meaning your recall gets far more expensive than it already was.

To address the FDA recall rules, most device manufacturers use customer relationship management (CRM) systems. Problems arise, though, when companies use one system for phone interactions, another for online customer contact, and another for snail-mail communication. When customer data is scattered across several systems—and it almost invariably is—accurately tracking contact data across all of the systems becomes difficult-to-impossible. For instance, a customer might have changed her telephone number via a mail-in registration card, but that information never gets reflected in the telephone database. Similarly, a customer might alert a device manufacturer to an address change via the website, but it doesn’t get updated in the postal database. When the company tries to reach their customers of a recall via snail-mail, they might get 20-30% returns due to incorrect addresses, prompting an expensive calling campaign.

No wonder device manufacturers are turning to master data management (MDM) to bolster their compliance efforts. A quality MDM solution can provide manufacturers with consistent, complete and accurate consumer, contact, and communication preference information—even when it is captured and stored in different systems. Companies can use this information to reach customers rapidly and avoid costly and ineffective device recall campaigns. With MDM, if a contact information is changed in the CRM system, that new information will quickly be reflected in the web content management system and other customer-facing systems. This means that any campaign that is using data from the central MDM system would always use the correct preference setting, regardless of the system of origin.

Posted in Master Data Management | Tagged , , , , , , , , , , , , | Leave a comment

MDM Enables Basel II Compliance

In the wake of the financial sector meltdown of late 2008 and the ensuing broader economic downturn, the banking industry is sure to face a far more stringent regulatory environment in the years ahead. On that score, no compliance challenge is more important right now than Basel II.

In 2005 the Federal Reserve Bank along with other U.S. banking regulators outlined an implementation transition period running from 2008 to 2011. To comply with Basel II requirements in the coming years, institutions need to develop a series of capabilities around data visibility and reporting. Namely, banks must be able to:

• Aggregate credit exposure at multiple levels, including legal entity, counterparty (or party to a contract) and business unit.
• Uniquely identify counterparties at the legal entity level and rate them using the banks’ internally developed rating models.
• Identify and link all credit exposures to individual counterparties, and then aggregate this information based on legal entity hierarchy.

Master data management (MDM) can set banks up to nimbly cope both with the Basel II issues they face today. For instance, to effectively comply with Basel II requirements, banks need to accurately aggregate counterparties so that all credit exposures can be linked to already identified counterparties. MDM makes it easy to create these linkages. Good risk analysis depends on good quality data, and on the availability of relevant counterparty data for effective analysis and modeling. Again, advanced MDM solutions provide a framework to capture and maintain data quality business rules, so counterparties can be uniquely identified at the legal entity levels, and hierarchies/families of legal entities can be identified so that risk exposures can be rolled up to the parent. Read how one financial services company complies with Basel II.

Posted in Master Data Management | Tagged , , , , , , , , | Leave a comment

Seven Ways to Reduce IT Costs with Master Data Management

Information Technology managers face a dilemma given the current economic climate: budgets are being cut, yet there’s no tolerance for decreases in IT service levels. Under such circumstances, how do you maintain or improve service levels, and continue to run the business efficiently? Smart IT decision-makers are seeking out technology investments that can help to accelerate cost reductions while streamlining business processes. Master Data Management (MDM) is exactly this kind of investment.

MDM ensures that critical enterprise data is validated as correct, consistent and complete when it is circulated for consumption by business processes, applications or users. But not all MDM technologies can provide these benefits. Only an integrated, model-driven, and flexible MDM platform with easy configurability can provide rapid time-to-value and lower total cost of ownership. Consider the ways a flexible MDM platform can reduce the following costs:

#1: Interface costs
Simplify expensive business processes that rely on point-to-point integrations by centralizing common information.

#2: Redundant third-party data costs
Eliminate duplicate data feeds from external data providers (Dunn & Bradstreet, Reuters, etc.).

#3: Data cleanup costs
Integrate data from disparate applications into a central MDM system, making it possible to cleanse all data across the enterprise in a single system.

#4: Outsourced cleansing costs
Eliminate the need for outsourced manual cleansing by automatically cleansing, enriching and deduplicating data on an ongoing basis, then centrally storing it for future use.

#5: License, support and hardware costs
Centralize data across the enterprise to reduce the amount of, or even eliminate, redundant data stores and systems.

#6: Custom solution development and maintenance costs
Replace antiquated custom masters with a configurable off-the-shelf MDM platform to save significant development and maintenance costs associated with band-aiding custom solutions.

#7: Information delivery costs
Eliminate reporting errors, expedite audits and improve compliance by a) managing a single version of the truth along with a history of all changes, and b) delivering this information to any reporting, business intelligence or data warehouse.

Posted in Master Data Management | Tagged , , , , , , , | 1 Comment

10 Questions you need to ask when writing an RFP for MDM

Critical master data management (MDM) functionality can be easily overlooked when request for proposals (RFP) are narrowly focused on a single business data type—such as customer (Customer Data Integration) or product (Product Information Management) — or on near-term requirements within a single business function.  Consequently, IT teams and systems integrators alike run the risk of selecting and investing in technologies that may be difficult to extend to other data types or difficult to scale across the organization. Worse, such solutions will likely require costly custom coding down the road in order to add additional business data entities or to extend the system to other lines of business. These mistakes are easily avoided, but to do so it is important to keep the following ten capabilities in mind as your prepare your RFP. If a prospective vendor can’t answer in the affirmative to all ten questions, keep looking.

Ten Costly RFP Mistakes to Avoid 

1. Will we be able to manage multiple business data entities within a single MDM platform?
2. Can the solution support my organization’s unique data governance needs?
3. Will it work with our standard workflow tool?
4. Is the MDM solution capable of supporting complex relationships and hierarchies?
5. Does the system rely on a flexible Service Oriented Architecture (SOA) model that automatically generates changes to the SOA services whenever the data model is updated?
6. Can we cleanse data inside the MDM platform?
7. Does the system allow for probabilistic matching using an assortment of matching techniques, including deterministic, probabilistic, heuristic, phonetic, linguistic, empirical, etc.?
8. Can the system create and maintain a golden record encompassing master data from different sources, which can be reconciled and centrally stored within a master data hub?
9. Is the solution designed to closely track history and lineage in order to support regulatory compliance?
10. Can the solution be implemented for several modes of operation, including analytical and operational?

Posted in Master Data Management | Tagged , , , , , , , , , | Leave a comment

How To Avoid Common MDM False Starts

Companies wishing to start a Master Data Management (MDM) project may be unsure where and how to begin.  MDM is a journey and success or failure at the first step either defines or dooms the further evolution of the project.  Recently, industry analysts have been recommending a cautious approach to starting with MDM – suggesting that companies start with a single data type (such as customer), implement MDM using a small footprint (such as registry style) or deploy MDM solely with a data warehouse to improve reporting.  Inherently these technology focused approaches reduce project risk and relieve the data governance burden.  Companies may readily adopt these approaches as perfectly reasonable starting points and lean to a more risk-averse approach to their initial MDM implementation in hopes of mitigating risks.  However, these same approaches may limit the scope and potential return on investment (ROI) from MDM since they do not attempt to solve the most pressing and difficult business problems. 

Some MDM vendor solutions only support a single data set (customer, product, etc), architecture style such as registry or can only be deployed for a single usage – either operational or analytical.  These solutions simply cannot be extended to other architectural styles or another usage mode which can severely limit their usefulness in addressing the most challenging of business problems.  In addition, a technology-centric start will not fulfill the most important needs around enterprise master data governance.

MDM is more precisely about solving business problems by efficiently managing master data that is critical to a company’s business operations.  How to get started? A pragmatic place to begin is to answer these three questions:

1. Which business problems need to be tackled? 
2. What is the business use? 
3. What are the business requirements for master data governance? 

What becomes obvious from answering these questions is that MDM will almost always require a multi-entity deployment (such as customer and product) and an architectural style that is not restricted to registry alone.  In most instances, synchronization with both operational and analytical systems may also be essential to effectively address the specific business needs of your organization.

Posted in Master Data Management | Tagged , , , , , , , , | Leave a comment

Seven Critical Questions to Ensure Master Data Governance Success

Typically, in large organizations data governance and its practice germinates either from a top down approach led by a key executive– say a Chief Financial Officer, or from a bottom up approach driven by business unit stakeholders who understand the importance of data ownership and data management to their success.  In a report entitled, A Data Governance Manifesto: Designing and Deploying Sustainable Data Governance, Jill Dyché co-founder of Baseline Consulting and author of the report warned, “Without a sound description of the problems being solved, as well as clear communications around key decisions and the authority to make them, data governance can fail before it really begins.”

While data is certainly ubiquitous across an organization, the practice of data governance is commonly limited to the most important types of data– the data necessary for efficiently managing business operations and regulatory compliance.  Today, this important class of data– which is called master data– is emerging as a critical and central component to a company’s data governance efforts.  However, to ensure master data governance success, organizations should be able to answer the following questions:

1. What data should constitute master data?
2. Who will own the various aspects of master data?
3. How many and what data sources exist for each type of master data?
4. What level of validation and/or verification of consistency, correctness and completeness is sufficient?
5. What, if any, industry or regulatory standards must be supported?
6. Who is allowed access rights to which data type and what actions can they perform?
7. What controls need to be put in place for master data, and what level of change needs to be recorded over what timeframe?

Taking the time to answer these seven critical questions in advance of designing your data governance process will allow you to better plan and implement a successful enterprise-wide master data governance effort.  Better yet, you just might find your data governance efforts will be rewarded by simply defining, determining and communicating key data decisions along with who has the authority to make and maintain them– up front.

Posted in Data Governance, Master Data Management | Tagged , , , , , , , , | 1 Comment

Are You Feeling Left Behind At The MDM Station?

Master Data Management (MDM) adoption is like a long freight train – forward-thinking companies (like the first car) have moved long-past their initial starting point into enterprise-level MDM solutions, while lagging companies (like the last car still at the station) are figuring out how to get started on their MDM journey. The companies that are in between (the middle car) have seen their success with their initial MDM solution and are now looking to evolve that success into other solution areas for different parts of the organization.

Leading MDM practitioners are those trendsetting companies who constantly push the envelope with state-of-the-art MDM functionality. These companies have already deployed multiple MDM solutions to improve the efficiency within their sales and marketing operations, for regulatory compliance, credit risk management, etc. They are now working diligently to evolve the MDM solutions to an enterprise-level to keep all the critical and core data truly synchronized across their entire enterprise. These customers demand superior solutions from their MDM vendor in order to support their company’s specific technology and data governance requirements.

Companies in the middle of their MDM journey have deployed a single MDM solution, perhaps for new account openings within their brokerage division, and now are maybe looking to expand their MDM footprint for Basel II compliance for their investment banking division. Organizations at this stage need their existing MDM technology to help get the second MDM solution up and running quickly and at the same time, they must continue to manage cross-functional data governance across various divisions. 

The companies at the tail-end of the train are looking to begin their MDM journey in order to address a specific business problem such as real-time order-to-cash management or high-fidelity management reporting. Their needs include the development of real-time composite applications, reporting framework, and divisional data governance.

Where ever you are in your MDM Journey careful thought to the advantages of a flexible MDM solution will allow you to plan ahead. Hopefully you aren’t still standing at the station as your competitors speed steadily ahead.  

Posted in Master Data Management | Tagged , , , , , | Leave a comment

Ravi Shankar, Senior Director of Product Marketing

Ravi Shankar is the Senior Director of Product Marketing for Siperian. With more than 20 years of experience, Ravi directs product and technical marketing activities for the Siperian MDM Hub. Ravi is a published author and a frequent speaker on master data management (MDM) and data governance. Previously, Ravi has directed product marketing, product management, and business development efforts at Oracle. Ravi holds BS (Honors) and MS in Computer Science and MBA from the Haas School of Business at the University of California, Berkeley.

Posted in Uncategorized | Tagged , , | Comments Off on Ravi Shankar, Senior Director of Product Marketing