Three Master Data Survivorship Approaches

One of the core capabilities around data quality in Master Data Management (MDM) solutions is providing data matching functionality with the aim of deduplicating records that describes the same real-world entity and thereby facilitate a 360 degree view of a master data entity.

Identifying the duplicates is one thing that is hard enough. However, how to resolve the result of the deduplication process is another challenge.

There are three main approaches for doing that:

Master Data Survivorship Approaches

Enlarge the image here.

In the above example we have three records: An orange, a green and a blue one. They are considered to be duplicates, meaning they describe the same real-world person. 

1: Survival of the fittest record

Selecting the record that according to a data quality rule is the most fit is the simplest approach. The rule(s) that determines which record that will survive is most often based on either:

  • Lineage, where the source systems are prioritized
  • Completeness, like for example which record has the most fields and characters filled

The downside of this approach is that surviving record only have data quality of that selected record, which might not be optimal, and that valuable information for deselected records might get lost.

Data quality tools that are good at identifying duplicates often has this simple method around survivorship.

In the above example the blue record wins and this record survives in the MDM hub, while the orange and the green record only survives in the source system(s).

2: Forming a golden record

In this approach the information from each data element (field) is selected from the record that, by given rules, is the best fit. These rules may be based on lineage, completeness, validity or other data quality dimensions.

Data elements may also be parsed, meaning that the element is split into discrete parts as for example an address line into house number and street name. The outcome may also be a union of the (parsed) data elements coming from the source systems.

In that way a new golden record is formed.

Additionally, values may also be corrected by using external directories which acts as a kind of source system.

This approach is more complex and while solving some of the data quality pain in the first approach, there will still be situations of mixing wrongly and lost information as well as it is hard to rollback an untrue result.

In the above example the golden record in the MDM hub is formed by data elements from the blue, green and orange record – and the city name is fetched from an external directory.

3: Context aware survivorship 

In this approach the identified duplicates are not physically merged and purged.

Instead, you will by applying lineage, completeness and other data quality dimension based rules be able to make several different golden record views that are fit in a given context. The results may differ both around the surviving data elements and the surviving data records.

This is the most complex approach but also the approach that potentially has the best business fit. The downsides include, besides the complexity, possible performance issues not at least in batch processing.

In the above example the MDM hub includes the orange, green and blue record and presents one surviving golden record for marketing purposes and two surviving golden records for accounting purposes.

 

Digital Transformation Success Rely on MDM / PIM Success

It is hard to find an organization who do not want to be on the digital transformation wagon today. But how can you ensure that your digital transformation journey will be a success? One of the elements in making sure that this data driven process will be a success will be to have a solid foundation of Master Data Management (MDM) including Product Information Management (PIM).

The core concepts here are:

  • Providing a 360-degree view of master data entities: Engaging with your customers across a range of digital platforms is a core part of any digital transformation. Having a 360-degree view of your customer has never been more important, and that starts with well-organized and maintained customer master data. The same is true for supplier master data and other party master data. 360-degree view of locations is equally important. The same goes for products and assets as pondered in post Golden Records in Multidomain MDM.
  • Enabling happy self-service scenarios: Customer data are gathered from many sources and digital self-registration is becoming the most common used method. The self-service theme has also emerged in handling supplier master data as self-service based supplier portals have become common as the place where supplier/vendor master data is captured and maintained. Interacting with your trading partners on digital platforms and having the most complete product information in front of your customers in self-service online selling scenarios requires a solid foundation for product master data and Product experience Management (PxM).
  • Underpinning the best customer experience: Customer experience (CX) and MDM must go hand in hand. Both themes involve multiple business units and digital environments within your enterprise and in the wider business ecosystem, where your enterprise operates. Master data is the glue that brings the data you hold about your customers together as well as the glue that combines this with the data you share about your product offering.
  • Encompassing Internet of Things (IoT): Smart devices that produces big data can be used to gain much more insight about parties (in customer and other roles), products, locations and the things themselves. You can only do that effectively by relating IoT and MDM.

Digital Transformation Success

3 Reasons MDM No Longer Delivers a Customer 360

Today’s guest blog post is from David Corrigan, CEO at AllSight

When Master Data Management (MDM) and Customer Data Integration (CDI) were designed over 15 years ago, they were touted as the answer to “Customer 360”.  But the art of mastering data and the art of creating a complete view of a customer are two very different things.  MDM is focused on managing a much smaller, core data set and aims to very deeply and truly master it.  Customer 360 solutions focus on “all data about the customer” to get the complete picture.  When it comes to a 360-degree view of the customer, master data is only part of the story.  Additional data has to be part of the 360 in order to have a full understanding of the customer – whether that be an individual or an organization. Additional data sources and data types required for today’s Customer 360 include transactions, interactions, events, unstructured content, analytics and intelligence – all of which are not managed in MDM.

Today, leading organizations are looking beyond MDM to a new era of Customer 360 technology to deliver the elusive complete view of the customer.  Here are 3 reasons why

  1. Customer 360 needs all data; MDM only stores partial data.  MDM focuses on core master data attributes, matching data elements and improving data quality.  Customer 360 has rapidly evolved requiring big data sets such as transactions and interactions, as well as unstructured big data like emails, call center transcriptions, and web chat interactions not to mention social media mentions, images and video.
  2. Customer 360 must serve analytical and operational needs; MDM only supports operational processing.  The original intent of MDM was to provide ‘good’ data to CRM and transactional systems.  While a branch of MDM evolved for ‘analytical MDM’ use cases, it was really a staging area for quality and governance to occur before data was loaded into warehouse for analysis and reporting.  A Customer 360 is meant to be analyzed and used by marketing analysts, data scientists as well as customer care and sales staff – it powers many different personas with different perspectives of the customer.
  3. Customer 360 is about improving the customer experience; Master data (core data) is used during a customer experience. Master Data is required during a customer interaction to understand key facts about a customer including name, contact info, account info, etc.  But the Customer 360 needs to blend all interactions, transactions and events into a comprehensive customer journey in order to analyze and personalize customer experiences.

But why does a Customer 360 now require all this information and capabilities beyond traditional MDM?  It is because the expectations of customers and the demands they make on businesses have changed.  Customers want personalized service and they want it now.  And they want a consistent experience across all channels – online, via phone, and in store.  They don’t want to have to repeat themselves or their preferences every time they interact with a business.  This requires companies to know more about their customers and to anticipate their next move in order to retain their business and loyalty.  Because, not only are customers more demanding than ever, but it is also easier for them to switch brands with little to no cost.

In order to meet these demands, many organizations assume they need to build these capabilities on their own using new technologies such as Apache Hadoop and Graph data stores.  These technologies can join together silos of master data, transactional data, raw data lake data, and experience/journey analytics.  However, a new class of software is emerging that bridges data, analytics and action and is based on these modern technologies. Customer Intelligence Platforms manage all customer information and synthesize it into an intelligent Customer 360.  Synthesizing all of those data sources is no easy task and that is where many organizations stall out.  What’s required is a machine-learning contextual matching engine that automates the process of linking customer data and evaluates data confidence.

Organizations such as Dell are seeing this shift first hand and have recognized that legacy MDM apps alone aren’t cutting it.  Deotis Harris, Senior Director, MDM at Dell EMC said “We saw an opportunity to leverage AllSight’s modern technology (Customer Intelligence), coupled with our legacy systems such as Master Data Management (MDM), to provide the insight required to enable our sellers, marketers and customer service reps to create better experiences for our customers.”

If you are like Dell and so many other organizations, a Customer 360 is high on your priority list.  A Customer Intelligence Platform might just be your next step.

MDM not 360