14 years ago this was busy times for computer professionals, including yours truly, because of the upcoming year 2000 apocalypse. The handling of the problem indeed had elements of hysteria, but all in all it was a joint effort by heaps of IT people in meeting a non-postponable deadline around fixing date fields that were too short.
Data entry and data storage fields that are too short, have an inadequate format or are missing are frequent data quality issues. Some everyday issues are:
Too short name fields
Names can be very long. But even a moderate lengthy name as Henrik Liliendahl Sørensen can be a problem here and there. Not at least typing your name on Twitter, where the 20 characters name field corresponds very well to the 140 character message length, forces many of us to shorten our name. I found a remedy here from a fellow Sørensen on a work around in the post Getting around the real name length limit in Twitter. Not sure if I’m prepared to take the risk.
Too short and restricted postal code fields
When working with IT solutions in Denmark you see a lot of postal code fields defined as 4 digits. Works fine with Danish addresses but is a real show stopper when you deal with neighboring Swedish and German 5 digit postal codes and not at least postal codes with letters from the Netherlands and the United Kingdom and most other postal codes from around the world.
Missing placeholder for social identities
The rise of social media has been incredible during the last years. However IT systems are lacking behind in support for this. Most systems haven’t a place where you can fill in a social handle. Recently James Taylor wrote the blog post Getting a handle on social MDM. Herein James describes a work around in a IBM MDM solution. Indeed we need ways to link the old systems of records with the new systems of engagement.
In MDM (Master Data Management) there is the term Multi-Domain MDM being how we manage respectively parties, products, locations and other entity types and handling master data within a Multi-Channel environment encompassing offline, online and social channels is a huge challenge within MDM today. Yet another multi view of MDM is handling different facets of master data being:
Handling entities is the core of master data management. Ensuring that master data are fit for multiple purposes most often by ensuring real world alignment is the basic goal of master data management. Entity resolution is at key discipline in doing that. In the party master data domain doing Customer Data Integration (CDI) is the good old activity aiming at compiling all the customer data silos in the enterprise into a golden copy with golden records. Product Information Management (PIM) is another ancestor in the MDM evolution history predominately focusing at the entities.
As we get better and better solutions for handling entities the innovation shifts to handling the relationships between entities. These relations exists for example in Multi-Channel environments by linking entities in the old systems of record with the same real world entities in the new systems of engagement as told in the post Social MDM and Systems of Engagement.
Getting the master data right the first time is crucial.
In product master data management getting to that stage is often done by managing a flow of events where the product data are completed and approved by a team of knowledge workers.
In party master data management a way of ensuring first time right is examined in the post instant Single Customer View. But that is only the start. Party master data has a life cycle with important events as:
The distinction between IT and business is an often used concept in a lot of disciplines from Enterprise Architecture (EA) over data quality management to Master Data Management (MDM). While it may be a good concept to use when assigning responsibilities and finding out who should be driving what I have personally always kind of disliked the concept. It’s a concept practically only used by the IT side and in my eyes IT is part of the business just as much as marketing, sales, accounting and all the other departments are.
So, now when business is going social, how does that affect the IT and business distinction?
Social business is in large parts done today in the enterprises around without involving the internal IT department. The use of data and functionality in social business done with so called systems of engagement is much more external focused than the internal focused nature of the traditional systems of record.
When talking about Master Data Management (MDM) we deal with something that maybe could be better coined as Master Entity Management. As a good old (logical or not) data model in the relational database world also have relations between entities there must of course then also be something called Master Relationship Management. And indeed there is as mentioned by Aaron Zornes in the interview called MDM and Next-Generation Data Sources on Information Management.
As touched by Aaron Zornes the solution to handling relations in the future may come from outside the relational database world in the form of graph databases. This was also discussed in the post Will Graph Databases become Common in MDM?
An often mentioned driver for looking much more into relationships is the promise of finding customer, and other, insights in social data based on the match between traditional master entity data and social network profiles. Handling these relations is an important facet of social MDM, an often mentioned subject on this blog.
Building the relations doesn’t stop with party master entities. There are valuable relations to location master entities and not at least crucial relations between party master entities and product master entities as told in the post Customer Product Matrix Management.
So Master Relationship Management fits very well with the current main trends in the MDM world being embracing big data not at least social data and encompassing multi-domain MDM. The third main trend being MDM in the cloud also fits. It’s not that we can’t explore all the relations out there from on-premise solutions; it’s just that there is a better relationship in doing so in the cloud.
So while traditional product master data management is about having the right hard facts about products consistent across multiple channels, and having the right images and other rich media consistent as well, in the social era you will also need to include the right and consistent stories when the multiple channels embraces social media.
Sharing product data
How do we ensure that we share the same product information, including the same stories, across the ecosystem of product manufacturers, distributors, retailers and end users?
During recent times I have followed a new cloud service called Actualog. Actualog is aiming at doing exactly that with emphasis on the daunting task of sharing product data in an international environment with different measurement systems, languages, alphabets and script systems.
Listening to big data
As discussed in the post Big Data and Multi Domain Master Data Management a prerequisite for getting sense out of analyzing social data (and other big data sources) is, that you not only have a consistent view of the product data related to products that you sell yourself, but also have a consistent view of competing products and how they relate to your products.
Therefore social product master data management requires you to extend the volume of products handled by your product information management solution probably in alternate product hierarchies.
The title of a post on the Nimble blog has this question: Time To Turn Your Sales Team Social?´ The post has a lot of evidence on why sales teams that embrace social selling are doing better than teams that doesn’t do that.
We do see new applications supporting social selling where Nimble is one example from the Customer Relationship Management (CRM) sphere as explored in the post Sharing Social Master Data. Using social services and exploiting social data in sales related business processes will over time affect the way we are doing customer master data management.
Apart from having frontend applications being social aware we also need social aware data integration services and we do indeed need social aware Master Data Management (MDM) solutions for handling data quality issues and ensuring a Single Customer View (SCV) stretching from the old systems of record to the new systems of engagement.
One service capable of doing data integration between the old world and the new world is FlipTop and some months ago I was interviewed on the FlipTop blog about the links to Social MDM here. Currently I’m working with a social aware Master Data Management solution being the iDQ™ MDM Edition.
What about you? Are your Customer Master Data Management and related data quality activities becoming social aware?
Every time I walk in and out of a plane at London-Gatwick Airport I always nod at an advert from the HSBC bank saying that in the future, selling will be more social:
A natural consequence of this will also be that data quality improvement (and master data management) will be more social.
One example is how complex sales, being sales processes typically in business-to-business (B2B) environments, will be heavily depended on integrating the exploitation of professional social networks as discussed on the DataQualityPro interview about the benefits of Social MDM.
Traditional Master Data Management (MDM) and related data quality improvement in B2B environments has been a lot about a single view of the business account and the legal entity behind. As Social Customer Relation Management (CRM) is much about the relations to the business contacts, the people side of business, we need a solid master data foundation behind the people being those contacts.
The same individual may in fact be an important influencer related to a range of business accounts being the legal entity with who you are aiming for a sales contract. You need a single view of that. So many sales contracts are based on a relation to a buyer moving from one business account to another. You need to be the winner in that game and the answer to that may very well be your ability to do better social MDM and embrace the data quality issues related to that.
Social selling of course also relates to business-to-consumer (B2C) activities and in doing that we will see new data quality issues. When exploiting social networks, both in B2B and B2C activities you need to link the traditional attributes as name and address with new attributes in the online and social world as explained in the post Multi-Channel Data Matching.
Besides exploiting social networks we will also see social collaboration as a mean to improve data quality. Social collaboration will go beyond collaboration within a single company and extend to the ecosystems of manufacturers, distributors, resellers and end users. A good example of this is the social collaboration platform called Actualog, which is about sharing product master data and thereby improving product data quality.
A recent piece from Fliptop is called What’s the Score. It is a thorough walk through on what is usually called social scoring done in influence scoring platforms within social media, where Klout, Kred and PeerIndex are the most known services of that kind.
The Fliptop piece has a section around faking, which was also the subject in a post lately on this blog. The post is called Fact Checking by Mashing Up, and is about how to link social network profiles with other known external sources in order to detect cheat. Linking social network profiles with other external sources and internal sources is what is known as Social MDM, a frequent subject on this blog for several years.
A social score must of course be seen in context, as it matters a lot what you are influential about when you want to use social scoring for business. As told in the post Klout Data Quality this was a challenge two years ago, and this is probably still the case. Also here I think linking with other (big) data sources and letting Social MDM be the hub will help.
PS: I have no idea why moron ended up there. Einstein is OK.
As told in the post Psychographic Data Quality marketers are moving from demographic marketing to psychographic marketing where a lot more data than before are used to getting the right message, to the right suspect at the right time. This affects the way we are working with data quality around customer master data and eventually how we do multi-domain master data management.
Using data for building psychographic profiles not only deals with lead generation. It’s usable throughout the whole customer master data life cycle by for example:
Finding the best suspects at the right moment
Keeping the prospects on the optimal track coordinated with the prospects need
Ensuring a well received customer experience and facilitating up-sell and cross-sell.
Making win-back possible
These opportunities apply to business-to-consumer (B2C) and business-to-business (B2B) as well.
Location master data management is essential in this quest as well, because we are not abandoning the basic demographic attributes in the physiographic world. We are building a deeper data universe on top of the traditional demographic (and firmographic) data. Having accurate location master data only helps here.
Mastering product master data is essential in the psychographic world too. This does not only apply to having your product hierarchies well manages for your own products, but will eventually also lead to a need for handling data on your competitors products and services in order to listen to social data streams.
Master Data Management (MDM) will extend to Social Master Data Management and must support wider exploitation of big data sources by being the hub for the psychographic customer profiles and the reference for descriptions of the product and service realm related to the psychographic attributes.
In a recent interview with yours truly on the Fliptop blog I had the chance to answer a question about how Social MDM is different from traditional MDM (Master Data Management). Check out the interview here.
As said in the interview I think that:
“The main difference between MDM as it has been practiced until now and Social MDM is that traditional MDM has been around handling internal master data and Social MDM will be more around exploiting external reference data and sharing those data.”
I definitely think that the management part is there, but it is different. Management is different in the social sphere in general. Data governance is different when it comes to social data (and other big data for that matter). Relying on social collaboration when maintaining master data is different from implementing “a data steward regime”.
In my eyes the management part is about balancing the use of internal master and the use of external reference data. Every organization should very carefully assess if they are good at maintaining different aspects of their internal master data (Hint: Many aren’t). Getting help from traditional data collectors and the new social sources and using social collaboration may very well be an important part of the solution.