Three Not So Easy Steps to a 360-Degree Customer View

Getting a 360-degree view (or single view) of your customers has been a quest in data management as long as I can remember.

This has been the (unfulfilled) promise of CRM applications since they emerged 25 years ago. Data quality tools has been very much about deduplication of customer records. Customer Data Integration (CDI) and the first Master Data Management (MDM) platforms were aimed at that conundrum. Now we see the notion of a Customer Data Platform (CDP) getting traction.

There are three basic steps in getting a 360-degree view of those parties that have a customer role within your organization – and these steps are not at all easy ones:

360 Degree Customer View

  • Step 1 is identifying those customer records that typically are scattered around in the multiple systems that make up your system landscape. You can do that (endlessly) by hand, using the very different deduplication functionality that comes with ERP, CRM and other applications, using a best-of-breed data quality tool or the data matching capabilities built into MDM platforms. Doing this with adequate results takes a lot as pondered in the post Data Matching and Real-World Alignment.
  • Step 2 is finding out which data records and data elements that survives as the single source of truth. This is something a data quality tool can help with but best done within an MDM platform. The three main options for that are examined in the post Three Master Data Survivorship Approaches.
  • Step 3 is gathering all data besides the master data and relate those data to the master data entity that identifies and describes the real-world entity with a customer role. Today we see both CRM solution vendors and MDM solution vendors offering the technology to enable that as told in the post CDP: Is that part of CRM or MDM?

CDP: Is that part of CRM or MDM?

The notion of a data centred application type called a Customer Data Platform (CDP) seems to be trending these days. A CDP solution is a centralized registry of all data related to parties regarded as (prospective) customers at an enterprise.

This kind of solution comes from two solution markets:

  • Customer Relationship Management (CRM)
  • Master Data Management (MDM)

The CRM track was recently covered in a Venture Beat article telling that Salesforce announces a Customer Data Platform to unify all marketing data. In this article it is also stated that Oracle just announced a similar solution named CX Unity and Adobe announced triggered journeys based on a rich pool of centralized data.

Add to that last year´s announcement from Microsoft, Adobe and SAP on their Open Data Initiative as told in the LinkedIn article Using a Data Lake for Data Sharing.

Some MDM solution providers are also on that track. Reltio Cloud embraces all customer data and Informatica Customer 360 Insights, formerly known as Allsight, is also going there as reported in the post Extended MDM Platforms.

Will be interesting to follow how CDP solutions evolve and if it is CRM or MDM vendors who will do best in this discipline. One guess could be that MDM vendors will provide “the best” solutions but CRM vendors will sell most licenses. We will see.

CDP CRM MDM

MDM vs ADM

The term Application Data Management (ADM) has recently been circulating in the Master Data Management (MDM) world as touched in The Disruptive MDM List blog post MDM Fact or Fiction: Who Knows?

Not at least Gartner, the analyst firm, has touted this as one of two Disruptive Forces in MDM Land. However, Gartner is not always your friend when it comes to short, crisp and easy digestible definitions and explanations of the terms they promote.

In my mind the two terms MDM and ADM relates as seen below:

ADM MDM.png

So, ADM takes care of a lot of data that we do not usually consider being master data within a given application while MDM takes care of master data across multiple applications.

The big question is how we handle the intersection (and sum of intersections in the IT landscape) when it comes to applying technology.

If you have an IT landscape with a dominant application like for example SAP ECC you are tempted to handle the master data within that application as your master data hub or using a vendor provided tightly integrated tool as for example SAP MDG. For specific master data domains, you might for example regard your CRM application as your customer master data hub. Here MDM and ADM melts into one process and technology platform.

If you have an IT landscape with multiple applications, you should consider implementing a specific MDM platform that receives master data from and provides master data to applications that takes care of all the other data used for specific business objectives. Here MDM and ADM will be in separated processes using best-of-breed technology.

Social Selling: Does it Work?

Social Master Data Management (Social MDM) has been on my radar for quite a long time. Social MDM is the natural consequence of Social CRM and social selling.

Social MDMNow social selling has become very close to me in the endeavour of putting a B2B (Business-to-Business) cloud service called Product Data Lake on the market.

In our quest to do that we rely on social selling for the following reasons:

  • If we do not think too much about, that time is money, social selling is an inexpensive substitution for a traditional salesforce, not at least when we are targeting a global market.
  • We have a subscription model with a very low entry level, which really does not justify many onsite meetings outside downtown Copenhagen – but we do online meetings based on social engagement though 🙂
  • The Product Data Lake resembles a social network itself by relying on trading partnerships for exchange of product information.

I will be keen to know about your experiences and opinions about social selling. Does it work? Does it pay off to sell socially? Does it feel good to buy socially?

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MDM Tools Revealed

Every organization needs Master Data Management (MDM). But does every organization need a MDM tool?

In many ways the MDM tools we see on the market resembles common database tools. But there are some things the MDM tools do better than a common database management tool. The post called The Database versus the Hub outlines three such features being:

  • Controlling hierarchical completeness
  • Achieving a Single Business Partner View
  • Exploiting Real World Awareness

Controlling hierarchical completeness and achieving a single business partner view is closely related to the two things data quality tools do better than common database systems as explained in the post Data Quality Tools Revealed. These two features are:

  • Data profiling and
  • Data matching

Specialized data profiling tools are very good at providing out-of-the-box functionality for statistical summaries and frequency distributions for the unique values and formats found within the fields of your data sources in order to measure data quality and find critical areas that may harm your business. These capabilities are often better and easier to use than what you find inside a MDM tool. However, in order to measure the improvement in a business context and fix the problems not just in a one-off you need a solid MDM environment.

When it comes to data matching we also still see specialized solutions that are more effective and easier to use than what is typically delivered inside MDM solutions. Besides that, we also see business scenarios where it is better to do the data matching outside the MDM platform as examined in the post The Place for Data Matching in and around MDM.

Looking at the single MDM domains we also see alternatives. Customer Relation Management (CRM) systems are popular as a choice for managing customer master data.  But as explained in the post CRM systems and Customer MDM: CRM systems are said to deliver a Single Customer View but usually they don’t. The way CRM systems are built, used and integrated is a certain track to create duplicates. Some remedies for that are touched in the post The Good, Better and Best Way of Avoiding Duplicates.

integriertWith product master data we also have Product Information Management (PIM) solutions. From what I have seen PIM solutions has one key capability that is essentially different from a common database solution and how many MDM solutions, that are built with party master data in mind, has. That is a flexible and super user angled way of building hierarchies and assigning attributes to entities – in this case particularly products. If you offer customer self-service, like in eCommerce, with products that have varying attributes you need PIM functionality. If you want to do this smart, you need a collaboration environment for supplier self-service as well as pondered in the post Chinese Whispers and Data Quality.

All in all the necessary components and combinations for a suitable MDM toolbox are plentiful and can be obtained by one-stop-shopping or by putting some best-of-breed solutions together.

Three Stages of MDM Maturity

If you haven’t yet implemented a Master Data Management (MDM) solution you typically holds master data in dedicated solutions for Supply Chain Management (SCM), Enterprise Resource Planning (ERP), Customer Relation Management (CRM) and heaps of other solutions aimed at taking care of some part of your business depending on your particular industry.

MDM Stage 1
Multiple sources of truth

In this first stage some master data flows into these solutions from business partners in different ways, flows around between the solutions inside your IT landscape and flows out to business partners directly from the various solutions.

The big pain in this stage is that a given real world entity may be described very different when coming in, when used inside your IT landscape and when presented by you to the outside. Additionally it is hard to measure and improve data quality and there may be several different business processes doing the same thing in an alternative way.

The answer today is to implement a Master Data Management (MDM) solution. When doing that you in some degree may rearrange the way master data flows into your IT landscape, you move the emphasis on master data management from the SCM, ERP, CRM and other solutions to the MDM platform and orchestrate the internal flows differently and you are most often able to present a given real world entity in a consistent way to the outside.

MDM Stage 2
Striving for a single source of truth

In this second stage you have cured the pain of inconsistent presentation of a given real world entity and as a result of that you are in a much better position to measure and control data quality. But typically you haven’t gained much in operational efficiency.

You need to enter a third stage. MDM 3.0 so to speak. In this stage you extend your MDM solution to your business partners and take much more advantage of third party data providers.

MDM Stage 3
Single place of trust

The master data kept by any organization is in a large degree a description of real world entities that also is digitalized by business partners and third party data providers. Therefore there are huge opportunities for reengineering your business processes for master data collection and interactive sharing of master data with mutual benefits for you and your business partners. These opportunities are touched in the post MDM 3.0 Musings.

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CRM systems and Customer MDM

Last week I had some fun making a blog post called The True Leader in Product MDM. This post was about how product Master Data Management still in most places is executed by having heaps of MS Excel spreadsheets flowing around within the enterprise and between business partners, as I have seen it.

business partnersWhen it comes to customer Master Data Management MS Excel may not be so dominant. Instead we have MS CRM and the competing offerings as Salesforce.com and a lot of other similar Customer Relationship Management solutions.

CRM systems are said to deliver a Single Customer View. Usually they don’t. One of the reasons is explained in the post Leads, Accounts, Contacts and Data Quality. The way CRM systems are built, used and integrated is a certain track to create duplicates.

Some remedies out there includes periodic duplicate checks within CRM databases or creating a federated Customer Master Data Hub with entities coming from CRM systems and other databases with customer master data. This is good, but not good enough as told in the post The Good, Better and Best Way of Avoiding Duplicates.

During the last couple of years I have been working with the instant Data Quality service. This MDM service sits within or besides CRM systems and/or Master Data Hubs in order to achieve the only sustainable way of having a Single Customer View, which is an instant Single Customer View.

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Leads, Accounts, Contacts and Data Quality

business partnersMany CRM applications have the concepts of leads, accounts and contacts for registering customers or other parties with roles in sales and customer service.

Most CRM systems have a data model suited for business-to-business (B2B) operations. In a B2B environment:

  • A lead is someone who might become your customer some day
  • An account is a legal entity who has or seems to become your customer
  • A contact is a person that works at or in other ways represent an account

In business-to-consumer (B2C) environments there are different ways of making that model work.

The general perception is that data about a lead can be so and so while it of course is important to have optimal data quality for accounts and contacts.

However, this approach works against the essential data quality rule of getting things right the first time.

Converting a lead into an account and/or a contact is a basic CRM process and the data quality pitfalls in that process are many. To name a few:

  • Is the lead a new account or did we already have that account in the database?
  • Is the contact new or did we know that person maybe at another account?
  • How do we align the known data about the lead with external reference data during the conversion process?

In other words, the promise of having a 360-degree customer view is jeopardized by the concept of most CRM systems.

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Service Oriented MDM

puzzleMuch of the talking and doing related to Master Data Management (MDM) today revolves around the master data repository being the central data store for information about customers, suppliers and other parties, products, locations, assets and what else are regarded as master data entities.

The difficulties in MDM implementations are often experienced because master data are born, maintained and consumed in a range of applications as ERP systems, CRM solutions and heaps of specialized applications.

It would be nice if these applications were MDM aware. But usually they are not.

As discussed in the post Service Oriented Data Quality the concepts of Service Oriented Architecture (SOA) makes a lot of sense in deploying data quality tool capacities that goes beyond the classic batch cleansing approach.

In the same way, we also need SOA thinking when we have to make the master data repository doing useful stuff all over the scattered application landscape that most organizations live with today and probably will in the future.

MDM functionality deployed as SOA components have a lot to offer, as for example:

  •  Reuse is one of the core principles of SOA. Having the same master data quality rules applied to every entry point of the same sort of master data will help with consistency.
  •  Interoperability will make it possible to deploy master data quality prevention as close to the root as possible.
  •  Composability makes it possible to combine functionality with different advantages – e.g. combining internal master data lookup with external reference data lookup.

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OMG: Santa is Fake

santa facebook picturesThis blog has earlier had some December blog posts about how Santa Claus deals with data quality (Santa Quality) and master data management (Multi-Domain MDM Santa Style).

As I like to be on the top of the hype curve I was preparing a post about how Santa digs into big data, including social data streams, to be better at finding out who is nice and who is naughty and what they really want for Christmas. But then I suddenly had a light bulb moment saying: Wait, why don’t you take your own medicine and look up who that Santa guy really is?

santa on twitterStarting in social media checking twitter accounts was shocking. All profiles are fake. FaceBook, Linkedin and other social networks all turned out having no real Santa Claus. Going to commercial third party directories and open government data had the same result. No real Santa Claus there. Some address directories had a postal code with a relation like the postcode “H0 H0 H0” in Canada and “SAN TA1” in the UK, but they seem to kind of fake too.

So, shifting from relying on the purpose of use to real world alignment I have concluded that Santa Claus doesn’t exist and therefore he can’t have a data store looking like a toy elephant or any other big data operations going on.

Also I won’t, based on the above instant data quality mash up, register Santa Claus (Inc.) as a prospective customer in my CRM system. Sorry.

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