Tag Archives: Golden record

What is a Golden Record?

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Golden Record

If you are as long in the tooth as I am – you are familiar with Willy Wonka and the Chocolate Factory…one of the major plot points revolves around Charlie getting the “golden ticket” which allows him access to Willy Wonka’s factory…but there are only 5 golden tickets available.

Within the world of Master Data Management (MDM) – there is a concept of a “golden record.” This is (hopefully) not as elusive as the Wonka Golden Ticket – but equally as important. This golden record gives you access to the most pure, validated and complete picture of your individual records in your domain.

Let’s start with defining a lot of the terms from the previous paragraph:

  • Golden record (according to whatis) ” is a single, well-defined version of all the data entities in an organizational ecosystem. In this context, a golden record is sometimes called the “single version of the truth,” where “truth” is understood to mean the reference to which data users can turn when they want to ensure that they have the correct version of a piece of information. The golden record encompasses all the data in every system of record (SOR) within a particular organization.
  • Domain is an area of control or knowledge. In the context of MDM – it refers to the type of data you want to master. For the payer market – it is typical to start with a member or provider domain, but there can be many, many different types of domains.

One of the trickiest parts of implementing an MDM solution is creating the workflow around this golden record. You need to consider all of your data sources, which fields from which data sources tend to be more reliable and what are the criteria for allowing the field from one system to populate an MDM field over another. In other words, if you have an enrollment system that captures the member’s name and a claims system that also captures the member’s name – which of these two systems tend to have the most correct member’s name? Is there another source system that is particularly reliable for capturing address – but the member name tends to be off?

One of the main considerations in the creation and maintenance of the golden record is matching and merging records.  If there are two records that are pretty similar, what is the process for inclusion in the golden record? – for instance consider the two records below:

Last Name First Name Member # Phone Number Street Address City, State
Wayland Jennifer 201215 7065842 123 Maine Street Camden, Maine
Wayland Jenn 201211 2078675309 123 Main Street Camden, Maine

The last names are the same as are the City, State fields. All of the other fields are different – in the world of a golden record, they don’t create an automatic match/merge. For the sake of this example – let’s say that we know that the first record comes from a source that has great reliability for names and addresses, while the second record comes from a source that is known for highly accurate member numbers and phone numbers. With good MDM solutions there should be a toolset that allows you to automate the merge functionality as much as possible. For this example, you could set up the workflow to obtain the records from the two sources, set up the criteria for merging/matching (take the name from the first record, the member number and phone number from the second record, and the addresses from the first record. The following could be the golden record for this member:

Last Name First Name Member # Phone Number Street Address City, State
Wayland Jennifer 201211 2078675309 123 Maine Street Camden, Maine

Where matching and merging get interesting is when the source fields are not clear “winners.” There will be situations where manual intervention is necessary to determine which record should take precedence. For this – a workflow manager toolkit is very helpful. It will assign records to a data steward who can then make the judgment based on their specific experience and knowledge of the data set which field from which record should take precedence. It can also have approval mechanisms before a record is finally and truly merged resulting in a modification to the existing golden record for a specific member.

As a result of the complexity of implementing a Master Data Management solution – it will help to start with picturing your golden record. Can you answer the following questions?

  • What information needs to be captured in your golden record?
    • Related to this – is there any information that is not necessarily specific to the domain but may be interesting when attaching relationships to a record (attaching a provider to a specific member)
  • What are all the sources of data for the record?
  • Are all the sources currently integrated? How easily can new records or updated records be shared?
  • Which source is the best source for which fields?
  • What is the threshold your company can tolerate for automatic merges?

What approval process needs to be in place before a merge takes place? Who needs to look at the record/recommendation before the merge is complete?

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Three Valuable Ways To Use Your Metadata Manager MDM Tool

You probably know that you can and must validate the integrity of your Data Model using the Metadata Manager Tool workbench (MET), but you can also do much more. In this blog I focus on three additional things you can do with the MET.

Once you have used MET to obtain a valid Data Model, you can continue using MET to:

Migrate your project from development to test, and to production via change lists. (more…)

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MDM Customer Success Stories @ Informatica World

Are you planning to attend Informatica World 2010 next week?

If so, I invite you to hear these breakout session presentations from our master data management (MDM) customers, including:

  • Johnson & Johnson’s Charles Bloodworth, IT Director, Medical Devices & Diagnostics;
  • Merrill Lynch’s Sal Caruso, Director, Client & Account Data;
  • St. Jude Medical’s Mike Striefel, Senior Manager, Enterprise Information Delivery, and
  • GlaxoSmithKline’s Nipun Bhonsle, Manager, Strategy, Architecture & Information. (more…)
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Posted in Business Impact / Benefits, Business/IT Collaboration, CIO, Customers, Data Governance, Data Quality, Enterprise Data Management, Governance, Risk and Compliance, Informatica Events, Master Data Management, Operational Efficiency, Uncategorized | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

MDM – What’s The Cost Of Bad Data In Financial Services?

One of the most critical first steps for financial services firms looking to implement multidomain master data management (MDM) is to quantify the cost savings they could achieve.

Unfortunately, a thorough analysis of potential ROI is also one of the steps least followed (a key culprit being disconnects between business and IT).

This shortcoming is spotlighted in a new Informatica white paper, “Five Steps to Managing Reference Data More Effectively in Investment Banking,” which outlines key questions to ask in sizing up the cost implications of bad data and antiquated systems, such as:

  • How long does it take to introduce a new security to trade?
  • How many settlements need to be fixed manually?
  • How many redundant data feeds does your firm have to manage?
  • How accurate and complete are your end-of-day reports?
  • Do you have the data you need to minimize risk and exposure? (more…)
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Posted in Enterprise Data Management, Financial Services, Governance, Risk and Compliance, Master Data Management, Operational Efficiency | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment