Tag Archive for Master Data Management

Three Steps to High Quality Master Data

Data quality is critical to business, because poor business data leads to poor operations and poor management decisions. For any business to succeed, especially now in this digital-first era, data is “the air your business needs to breathe”.  If leadership at your organization is starting to consider what digital transformation means to your business or industry – and how your business needs to evolve to thrive in these changing times, they will likely assess the current business and technology state. One of the most common outcomes management may observe is that the business systems are “outdated” and “need to be replaced”. As a result, many businesses resolve to replace legacy systems with modern business systems as part of their digital transformation strategy.

Digital Transformation Starts with Data

More than likely, those legacy systems did a terrible job with your business data. They often permitted numerous, incomplete master data records to be entered into the system. Now, you have customer records which aren’t really customers. The “Bill To’s” are “Sold-To’s”, the “Sold-To’s” are “Ship-To’s”, and the data won’t tell you which is which. You might even have international customers with all of their pertinent information in the NOTES section. Each system which shares customer master data with other systems contains just a small piece of the customer, not the complete record.

This may have been the way things were “always done” or departments made due with the systems available, but now it’s a much larger problem, because in order to transform itself, a business must leverage its data assets. It’s a significant problem when you consider all the data your legacy systems maintain. Parts, assets, locations, vendors, material, GL accounts: each suffer from different, slightly nuanced data quality problems. Now it hits you: your legacy systems have resulted in legacy data.  And as the old saying goes – “garbage in, garbage out.” In order to modernize your systems, you must first get a handle on data and your data practices.

Data Quality Processes

The data modernization process should begin with Master Data Management (MDM), because MDM can be an effective data quality improvement tool to launch your business’ Digital Transformation journey. Here’s how a data quality process works in MDM.

Data Validation – Master Data Management systems provide the ability to define data quality rules for the master data. You’ll want these rules to be robust — checking for completeness and accuracy. Once defined and applied, these rules highlight the gaps you have in your source data and anticipate problems which will present themselves when that master data is loaded into your shiny new modern business applications.

Data Standardization – Master Data thrives in a standardized world. Whether it is address standardization, ISO standardization, UPC standardization, DUNS standardization, standards assist greatly with the final step in the process.

Matching and Survivorship – If you have master data residing in more than one system, then your data quality process must consider the creation of a “golden record”. The golden record is the best, single representation of the master data, and it must be arrived at by matching similar records from heterogeneous systems and grouping them into clusters. Once these clusters are formed, a golden record emerges which contains the “survivors” from the source data. For example, the data from a CRM system may be the most authoritative source for location information, because service personnel are working in CRM regularly, but the AR system may have the best DUNS credit rating information.

Modernize Your Data and Modernize Your Business

BB Art

These three data quality processes result in a radical transformation in the quality of master data, laying the foundation for critical steps which follow. Whether or not your digital transformation involves system modernization, your journey requires clean, usable data. Digital transformation can improve your ability to engage with customers, but only if you have a complete view of who your customers are. Digital transformation can empower your employees, but only if your employees have accurate information about the core assets of the business. Digital transformation can help optimize operations, but only if management has can make informed data driven decisions. Finally, digital transformation can drive product innovation, but only if you know what your products can and cannot currently do.

Berry_Brian-240About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics. 

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Save Operational MDM for Phase 2

In my final installment on the 5 Critical Success Factors for Initiating Master Data Management, I want to discuss why tackling Operational MDM is valuable, and when to do it. 

A major contributor to disruptions in MDM projects is lack of stakeholder agreement with respect to what the team is trying to accomplish with MDM.  It’s important to get everyone on the team clear on the two purposes of MDM.

The first purpose is to facilitate better reporting (Reporting MDM).  The goal with Reporting MDM is to gather and aggregate data (sales, invoices, purchase orders, etc.) such that in an Enterprise Reporting Context, all of the data is included from a set of source systems.  A Reporting MDM system does this by Matching Master Data records from each of these systems.  Then, it provides Views of these matches (groups of master records) to subscribing systems and users to consume and use for their own purposes.  It sounds simple, and in fact, it is pretty simple.

The second purpose is to improve the overall data quality in each operational system (Operational MDM).  The goal with Operational MDM is to ensure that each representation of the same “thing” (e.g. Vendor) is the same in all systems which house master data for that “thing”.  An Operational MDM system does this by Matching Master Data records from each source and then Harmonizing the records (i.e. makes all of the master records in a group “line up”).  Finally, it Distributes the harmonized data back to the source systems.  Imagine knowing that all representations of your most valued customers, are verifiably represented in a logically consistent way in all of your AR systems.

Visually, an Operational MDM synchronization process might look like this.

Operational MDM in Action

Once, we have those two concepts solidly understood, the question becomes: can we have both?  Yes, you can have both.  However, if delivering value to the business quickly is a consideration (it should be), I recommend that you tackle Reporting MDM first.  Reporting MDM has fewer technology hurdles, initiates a Data Governance program, and delivers real value quickly.

Here is what Operational MDM will take:

  1. A Data Bus – you’ll need a integration solution which can handle connections to all of the LOB systems which you want to synchronize.  My team uses Microsoft BizTalk Server for this.
  2. Subject Matter Expertise – you’ll need access to the people who understand the target systems extremely well.  Often they will need to expose API’s to the MDM team, so that synchronization can be “real-time” (a change is made to MDM and the change event propagates to all of the affected systems)
  3. Business Process Review – your Data Governance team will likely need to consider the full lifecycle of the master data- creation, maintenance  and archive.

In summary, Operational MDM is achievable and yields tremendous value.    But first, build the foundation and “put some points on the board”.  If you build a  Federated Data Model, Keep MDM Separate, Flip the Script and Formulate your Governance Plan, Phase 1 will be successful, and you’ll get funding for Operational MDM in Phase 2.

Good luck!

Formulate Essential Data Governance Practices

The creation of a  Data Governance function at your organization is a critical success factor in implementing Master Data Management.  Just like any machine on a factory floor, Master Data is an Asset.  An Asset implies ownership, maintenance and value creation: so too with Master Data.  To borrow an analogy from the Manufacturing world, Transactional data is the Widget, and Master data is one of the machines that makes the Widget.  It is part of your organization’s Value Chain.

Unfortunately, firms starting on the road to MDM fall peril to one of two pitfalls on either extreme of the Data Governance mandate.  The first pitfall is to treat MDM as a one-time project, not a program.  Projects have an end-date, Programs are continual.  Have you ever heard of an Asset Maintenance Project?  That’s a recipe for crisis.  Firms which maintain their assets as a Program do far better.

The second pitfall is ask the Data Governance team to do too much, too fast.  Governance cannot do much without some asset to govern.  Have you ever heard of a Machinery Maintenance Program instituted before you figured out what type of machinery you needed, what the output requirements were, or before you made the capital purchase?  I haven’t either.  First, you acquire the capital.  You do so with the expectation that you will maintain it.  Then you put it into production. Then you formulate the maintenance schedule and execute that plan.

In order to successfully stand up a Data Governance function for your Master Data Program, you’ll need to understand these essential roles in Data Governance: Executive Steering, Data Owners, Data Stewards. 

Follow these Do’s and Don’ts:

Do establish an Executive Steering Committee for all Master Data practices in your enterprise, focused upon strategic requirements, metrics and accountability.

Do establish Data Quality Metrics.   Tie them to strategic drivers of the business. Review them regularly.  Your MDM toolset should provide analytics or dashboards to provide this view.

Don’t ask the Steering Committee to own the data model or processes – that is the Data Ownership role.

Do establish a Data Ownership group for each domain of Master Data.  Ownership teams are typically cross-functional, not simply the AR manager for Customer Master Data, or the HR manager for Employee Master Data.  As you evolve down the maturity path, you will find that master data has a broad set of stakeholders – Do be ready to be inclusive.

Do establish regular status meetings where Data Ownership meets with the Executive Steering Committee to review priorities and issues.

Don’t require that Data Owners “handle the data”.  That is the Data Stewardship role.

Do formalize a Data Stewardship team for each domain of Master Data.  Data Stewards are “data people” – business people who live in the data, but with no technical skills required, per se (though technology people can contribute to a Data Stewardship team).

Don’t restrict Data Stewards to just  the people who report to the Data Owner – think cross-functional!

Do anticipate conflicts – Data Owners should have some political skills.  The reality is that Master Data is valuable to a broad set of constituencies within an enterprise.  Be practical as it relates to one faction’s “Wish List” and keep moving the ball forward.

Without a Data Governance function, MDM tends to be a one-time project (“Clean it Once”) and fails to deliver real value.  Without a clear vision of how Data Governance support MDM, it can hold things up. A rational Data Governance function does not need to hold up the execution of a Master Data project – it supports it. Keep Data Governance strategic, cross functional, and flexible. Then, let the MDM technology team deliver the tools.

First Accommodate Master Data, Then Clean It

In this blog post, I want to challenge a deeply held notion of Data Quality and Master Data Management.

I have had many, many conversations with technology professionals seeking to implement MDM in their organization. In those first conversations, among the first questions asked is a complex one, disguised as a simple one – How can I start with clean data?

Listen: if you try to start your Master Data implementation with clean master data – you will never get started!

Instead you need to embrace two fundamental realities of Master Data Management. First, there is no clear authoritative source for your master data (if there were, you wouldn’t have a problem). Second: Data Quality is “Front of House” work. The IT department may have data integration, data profiling, third party reference data and matching algorithms in their toolbox, but they can only do so much. IT tools are Back Office Tools and the IT data cleanups happen in the shadows. When they get it wrong, they get it very wrong and comprehensively wrong (and the explanation is hard to understand).

This sequence of events is straightforward, enables the business to take ownership and provides a clear path to getting started.

  • Accommodate your Data – in order for business people to steward and govern their own data – they need to see it with their own eyes, and they need to see all of it, even the data they don’t like. In order to do this, you must:
    • Maintain a clear relationship between data in the MDM hub and its source – don’t attempt to reduce the volume of records. The Federated approach to MDM does this best.
    • Keep rationalization/mapping to a minimum – avoid cleaning the data as you load it. Its wasteful to do it in ETL code when your MDM toolset is ready to do it for you much more easily.
    • Take a “Come as You Are” approach – avoid placing restrictions on the data at this stage of the project, because this only serves to keep data out of your system. We want the data in.
  • Establish Governance of your Data – once you have all of the data loaded into a Federated data model, you have the opportunity to start addressing the gaps
    • First, take some baseline measurements. How incomplete is your data?
    • Next, begin developing rules which can be enforced in the MDM Hub. These rules should be comprehensible to a business user. Ideally, your toolset integrates the rules into the stewardship experience, so that rules declared in the hub are readily available to them. Once you have a healthy body of rules, validate the data and take another baseline measurement
    • Now your data stewardship team can get to work, and you’ll have real metrics to share with the business with regards to the progress you are making towards data compliance.
  • Improve your Data – MDM toolsets automate the process of improving master data sourced from different systems. They do this in three ways:
    • Standardize your Data – MDM tools help data stewards establish standards and check data against those standards
    • Match your Data – MDM tools help data stewards find similar records from multiple systems and establish a grouping of clusters of records. The Group becomes the “Golden Record” – none of the sources get to be the boss!
    • Harmonize your Data – MDM tools help data stewards make decisions about which sources are most authoritative and can automate the harmonization of data within a grouping

Organizations whose starting approach with MDM is “Get the data clean and Keep the data clean” often fail to even get started. Or worse, they spend a lot of time and money requiring IT to clean the data, and then abandon the project after 6 months with nothing to show for it. Clean, then Load is the wrong order: Flip the Script and stick to these principles.

  1. Design a Federated MDM Data model which simplifies identity management for the master data.
  2. Identify where your master data lives and understand the attributes you want to govern initially.
  3. Bring the master data in as it exists in the source systems.
  4. Remove restrictions to loading your data.
  5. Establish some baseline measurements.
  6. Devise your initial rules set.
  7. Use MDM Stewardship tools to automate standardizing, matching and harmonizing.