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:
- 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?
During the end of last century data quality management started to gain traction as organizations realized that the many different applications and related data stores in operation needed some form of hygiene. Data cleansing and data matching (aka deduplication) tools were introduced.
In the 00’s Master Data Management (MDM) arised as a discipline encompassing the required processes and the technology platforms you need to have to ensure a sustainable level of data quality in the master data used across many applications and data stores. The first MDM implementations were focused on a single master data domain – typically customer or product. Then multidomain MDM (embracing customer and other party master data, location, product and assets) has become mainstream and we see multienterprise MDM in the horizon, where master data will be shared in business ecosystems.
MDM also have some side disciplines as Product Information Management (PIM), Digital Asset Management (DAM) and Reference Data Management (RDM). Sharing of product information and related digital assets in business ecosystems is here supported by Product Data Syndication.
Lately data governance has become a household term. We see multiple varying data governance frameworks addressing data stewardship, data policies, standards and business glossaries. In my eyes data governance and data governance frameworks is very much about adding the people side to the processes and technology we have matured in MDM and Data Quality Management (DQM). And we need to combine those themes, because It is not all about People or Processes or Technology. It is about unifying all this.
In my daily work I help both tool providers and end user organisations with all this as shown on the page Popular Offerings.
A Request for Proposal (RFP) process for a Master Data Management (MDM) and/or Product Information Management (PIM) solution has a hard fact side as well as there are The Soft Sides of MDM and PIM RFPs.
The hard fact side is the detailed requirements a potential vendor has to answer to in what in most cases is the excel sheet the buying organization has prepared – often with the extensive help from a consultancy.
Here are what I have seen as the most frequently included topics for the hard facts in such RFPs:
- MDM and PIM: Does the solution have functionality for hierarchy management?
- MDM and PIM: Does the solution have workflow management included?
- MDM and PIM: Does the solution support versioning of master data / product information?
- MDM and PIM: Does the solution allow to tailor the data model in a flexible way?
- MDM and PIM: Does the solution handle master data / product information in multiple languages / character sets / script systems?
- MDM and PIM: Does the solution have capabilities for (high speed) batch import / export and real-time integration (APIs)?
- MDM and PIM: Does the solution have capabilities within data governance / data stewardship?
- MDM and PIM: Does the solution integrate with “a specific application”? – most commonly SAP, MS CRM/ERPs, SalesForce?
- MDM: Does the solution handle multiple domains, for example customer, vendor/supplier, employee, product and asset?
- MDM: Does the solution provide data matching / deduplication functionality and formation of golden records?
- MDM: Does the solution have integration with third-party data providers for example business directories (Dun & Bradstreet / National registries) and address verification services?
- MDM: Does the solution underpin compliance rules as for example data privacy and data protection regulations as in GDPR / other regimes?
- PIM: Does the solution support product classification and attribution standards as eClass, ETIM (or other industry specific / national standards)?
- PIM: Does the solution support publishing to popular marketplaces (form of outgoing Product Data Syndication)?
- PIM: Does the solution have a functionality to ease collection of product information from suppliers (incoming Product Data Syndication)?
Learn more about how I can help in the blog page about MDM / PIM Tool Selection Consultancy.
In the latest Gartner Master Data Management Solutions magic quadrant it is stated that “Gartner-estimated 2016 revenues of the four largest vendors commanded over 77% of the market (SAP, IBM, Oracle and Informatica). These four held a Gartner-estimated market share of just over 73% in 2017.”
There seems to be a little decrease in the dominance from the megavendors, though the market according to Gartner is still ruled by the big four. The number of licenses sold by these vendors and those midsize vendors who also are in the quadrant can be found in the post Counting MDM Licenses.
For the future trend it is also worth noticing, that Oracle is not a part of the MDM magic quadrant anymore, as Oracle in the Gartner lingo is not an MDM vendor, but an ADM vendor today. Oracle is not included in the latest Forrester MDM wave either.
Another market distinction is around MDM versus Product Information Management (PIM) solutions. The post Several Sources of Truth about MDM / PIM Solutions examines the positioning by Gartner and Forrester and in that sense the magavendors are better at MDM than PIM.
Any Gartner estimation will be biased towards large vendors having large clients as these are the Gartner clients. In a LinkedIn discussion a big four person suggested that the market is fragmented and there are many MDM-like solutions.
This remark followed the market estimation from a fresh market report from Information Difference. The positioning results from here were shown in the post Movements in the MDM Vendor Landscape 2019. In here the megavendors did not perform so well on the technology axis, which is largely made up by customer satisfaction feedback in the underlying survey.
So, what do you think? Will the megavendors still rule the MDM market or will the midsize and smaller vendors get a larger piece of the cake?
For bigger picture click here.
The latest market report on data quality tools from Information Difference is out. In the introduction to the data quality landscape Q1 2019 this example of the consequences of a data quality issue is mentioned: “Christopher Columbus accidentally landed in America when he based his route on calculations using the shorter 4,856 foot Roman mile rather than the 7,091 foot Arabic mile of the Persian geographer that he was relying on.”.
Information Difference has the vendors on the market plotted this way:
As reported in the post Data Quality Tools are Vital for Digital Transformation also Gartner recently published a market report with vendor positions. The two reports are, in terms on evaluating vendors, like Roman and Arabic miles. Same same but different and may bring you to a different place depending on which one you choose to use.
Vendors evaluated by Information Difference but not Gartner are veteran solution providers Melissa and Datactics. On the other side Gartner has evaluated for example Talend, Information Builders and Ataccama. Gartner has a more spread out evaluation than Information Difference, where most vendors are equal.
PS: If you need any help in your journey across the data quality world, here are some Popular Offerings.
Data matching is a sub discipline within data quality management. Data matching is about establishing a link between data elements and entities, that does not have the same value, but are referring to the same real-world construct.
The most common scenario for data matching is deduplication of customer data records held across an enterprise. In this case we often see a gap between what we technically try to do and the desired business outcome from deduplication. In my experience, this misalignment has something to do with real-world alignment.
What we technically do is basically to find a similarity between data records that typically has been pre-processed with some form of standardization. This is often not enough.
Deduplication and other forms of data matching with customer master data revolves around names and addresses.
Standardization and verification of addresses is very common element in data quality / data matching tools. Often such at tool will use a service either from its same brand or a third-party service. Unfortunately, no single service is often enough. This is because:
- Most services are biased towards a certain geography. They may for example be quite good for addresses in The United States but very poor compared to local services for other geographies. This is especially true for geographies with multiple languages in play as exemplified in the post The Art in Data Matching.
- There is much more to an address than the postal format. In deduplication it is for example useful to know if the address is a single-family house or a high-rise building, a nursing home, a campus or other building with lots of units.
- Timeliness of address reference data is underestimated. I recently heard from a leader in the Gartner Quadrant for Data Quality Tools that a quarterly refresh is fine. It is not, as told in the post Location Data Quality for MDM.
The overlaps and similarities between data matching and identity resolution was discussed in the post Deduplication vs Identity Resolution.
In summary, the capability to tell if two data records represent the same real-world entity will eventually involve identity resolution. And as this is very poorly supported by data quality tools around, we see that a lot of manual work will be involved if the business processes that relies on the data matching cannot tolerate too may, or in some cases any, false positives – or false negatives.
Even telling that a true positive match is true in all circumstances is hard. The predominant examples of this challenge are:
- Is a match between what seems to be an individual person and what seems to be the household where the person lives a true match?
- Is a match between what seems to be a person in a private role and what seems to be the same person in a business role a true match? This is especially tricky with sole proprietors working from home like farmers, dentists, free lance consultants and more.
- Is a match between two sister companies on the same address a true match? Or two departments within the same company?
We often realize that the answer to the questions are different depending on the business processes where the result of the data matching will be used.
The solution is not simple. The data matching functionality must, if we want automated and broadly usable results, be quite sophisticated in order to take advantage of what is available in the real-world. The data model where we hold the result of the data matching must be quite complex if we want to reflect the real-world.
The Gartner Magic Quadrant for Data Quality Tools 2019 is out. It will take you 43 minutes to read through, so let me provide a short overview.
Gartner says that “data quality tools are vital for digital business transformation, especially now that many have emerging features like automation, machine learning, business-centric workflows and cloud deployment models.”
The data quality software tools market was at 1.61 billion USD in 2017 which was an increase of 11.6% compared to 2016.
Gartner sees that end-user demand is shifting toward having broader capabilities spanning data management and information governance. Therefore, the data quality tool market continues to interact closely with the markets for data integration tools and for Master Data Management (MDM) products.
Among the capabilities mentioned is multidomain support meaning capabilities covering all the specific data subject areas, such as customer, product, asset and location. Interestingly Gartner continues to focus on customer as the one of several party data domains out there. In my experience, there are the same data quality challenges with vendor and other business partner data as well as with employee data.
According to Gartner, data quality tool vendors are competing to address shifting market requirements by introducing an array of new technologies, such as machine learning, interactive visualization and predictive/prescriptive analytics, all of which they are embedding in data quality tools. They are, according to Gartner, also offering new pricing models, based on open source and subscriptions.
The vendors included in the quadrant are positioned as seen below:
If you want a full copy of the report you can, against providing your personal data, get it from Information Builders here.
The Forrester Wave™: Master Data Management, Q1 2019 is out. The subtitle of the report is “Toward the Third Generation of Master Data Management.”
This resonates very well with my view as for example expressed is the post Three Stages of MDM Maturity.
The Forrester Report has this saying on that theme: “The internet of things has led to systems of automation and systems of design, which introduce new MDM usage scenarios to support co-design and the exchange of information on customers, products, and assets within ecosystems”.
Else, the report of course ranks the best selling MDM solutions as seen below:
You can get a free copy of the report from Riversand here or from Reltio here.
When it has been about mergers and acquisitions on the Master Data Management (MDM) solution market, there have until recently not been so much going around since 2012. Rather we have seen people leaving the established vendors and formed or joined new companies.
But, three months ago Tibco was coupled with Orchestra.
Then on Valentine’s day 2019 Symphony Technology Group Acquired PIM and MDM Provider EnterWorks with the aim of coupling their offerings with the ones from WinShuttle. WinShuttle has been more a data management generalist company with focus on ERP data – not at least in SAP. This merger ties into the trend of extending MDM platforms to other kinds of data than traditional master data. It will also make an alternative to SAPs own MDM and data governance offering called MDG.
Fourteen days later there was a new coupling as reported in the post MDM Market News: Informatica acquires AllSight. This must also be seen as a step in the trend of providing an extended MDM platform with Artificial Intelligence (AI) capabilities. Also, Informatica is here going against the new MDM solution provider Reltio, who has been successful in promoting their big data extended MDM platform.
Both Enterworks and AllSight (and Reltio too) are listed on The Disruptive Master Data Management List.
The Gartner Magic Quadrant for Master Data Management (MDM) Solutions 2018 was published last month.
Some of the numbers in the market that were revealed in the report was the number and distribution of MDM licenses from the included vendors. These covered their top-three master data domains and estimated license counts as well as the number of customers managing multiple domains:
One should of course be aware of the data quality issues related to comparing these numbers, as they in some degree are estimates based on different perceptions at the included vendors. So, let me just highlight these observations:
- The overall number of MDM licenses and unique MDM customers (at the included vendors) is not high. Under 10,000 organizations world-wide is running such a solution. The potential new market out there for the salesforce at the MDM vendors is huge.
- If you find an existing MDM solution user organization, they probably have a solution from SAP or Informatica – or maybe IBM. To be complete, Oracle has been dropped from the MDM quadrant, they practically do not promote their MDM solutions anymore, but there are still existing solutions operating out there.
- The reign of Customer MDM is over. Product MDM is selling and multidomain is becoming the norm. Several MDM vendors are making their way into the quadrant from a Product Information Management (PIM) base as reported in the post The Road from PIM to Multidomain MDM.
PS: If you, as an end customer organization or a MDM and PIM vendor, want to work with me on the consequences for MDM solutions, here are some Popular Offerings for you.