MDM / PIM / DQM List September 2019 Achievements

September 2019 were in many ways a record month for the sister site – The Disruptive MDM / PIM / DQM List.

The number of visitors has doubled compared to last month and increased three times compared to last year’s average – and so have the page views.

Number of LinkedIn page followers has increased 27 % since last month.

The new Select your solution service has got a good debut with 16 requests for a tailored list of solutions fit for your context, scope and requirements.

A new solution has entered the list: Check out Reifier here.

MDMlist stats 2019 09

Thanks to all visitors, followers, solution selectors and registrants.

Data Quality Tool Market Musings

There is a market for data quality tools and most of the tools on this market have been operating since before year 2k either still by an independent data quality tool provider or now as part of a data management suite.

The prominent market reports telling about the market with generic ranking of the solutions are from Gartner and Information Difference (and earlier on from Forrester, who though have not published a report on data quality tools for years now).

The latest Gartner report from March 2019 was examined in the post Data Quality Tools are Vital for Digital Transformation. Information Difference has their report available here: The Data Quality Landscape – Q1 2019.

As told in the post DQM Tools In and Around MDM Tools there are three main ways of providing Data Quality Management (DQM) capabilities: As an independent data quality tool, as part of a data management suite or inside an MDM platform. The data quality tool market reports encompasses the first two of three options (while the MDM solution market reports encompasses the latter two of three).

DQ Tool Market

The data quality tools represented in the above-mentioned DQ markets reports are:

  • Informatica, who acquired the veteran DQ service SSA-Name3, Similarity Systems and other DQ services, now part of the Informatica data management suite
  • Experian, who acquired among others veteran DQ service QAS and also offers other solutions mainly related to credit check and fraud prevention
  • Syncsort, who acquired veteran DQ service Trillium, recently also Pitney Bowes and also have some other data management services
  • IBM, who acquired DQ services as Ascential now being part of a data management suite with several overlapping services
  • SAP, who bought several DQ services now being part of the huge ERP/data management suite
  • SAS Institute, who acquired Dataflux that now is part of the BI focused suite
  • Talend, as part of a data management suite
  • Oracle, who acquired veteran DQ service Datanomic and other DQ services now being part of the ERP/data management suite
  • Information Builders, as part of a data management and BI suite
  • Ataccama, together with MDM services
  • Melissa, a veteran company in DQ
  • Uniserv, a veteran company in DQ
  • Innovative, a veteran company in DQ
  • Datactics, a veteran company in DQ
  • MIQsoft
  • RedPoint Global
  • BackOffice Associates, as part of a data governance focused solution

However, there are a lot of other data quality tools on the market.

PS: If you represent a vendor providing DQM capabilities as an independent data quality tool, as part of a data management suite or inside an MDM / PIM solution, you are welcome to register your solution on The Disruptive MDM / DQM List here.

First Experiences with the MDM / PIM Solution Selection Service

As announced 3 weeks ago here on the blog I have started a service for MDM / PIM solution ranking based on your context, scope and requirements.

It has been good to see that the first 2-digit number of people have requested their solution list based on their specific requirements. A few have also provided the feedback, that they actually already had made a list. In these cases, I am happy that the responses were, that the result from the automated selection process corresponded very well with their traditional and (I guess) time and money consuming selection project.

The set of requirements I have processed have been very varying and thus the solution lists have also been somewhat different encompassing a lot of solution vendors both being on The Disruptive MDM / PIM List as well as those recognized by Gartner, Forrester and Information Difference.

Anyone else who would like to jumpstart a tool selection? Start here.

Select your solution process

Longlist, Shortlist and Proof of Concept

When selecting a tool for a Master Data Management (MDM) / Product Information Management (PIM) / Data Quality Management (DQM) solution you can:

  • Select a longlist of 5 to 10 solutions that you after some research narrow down to a shortlist and after some more thorough research you will from this select a solution for a PoC / contract.
  • Select a shortlist of 3 to 5 solutions and after some research select a solution for a PoC / contract.
  • Directly select a solution for a Proof of Concept (PoC) and Business Case.

How would you – or did you – select a tool?

 

By the way: There are also some different approaches to get the work done:

Longlist shortlist PoC

Welcome Reifier on the Disruptive MDM / PIM List

The Disruptive MDM / PIM List is list of solutions in the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) space.

The list presents both larger solutions that also is included by the analyst firms in their market reports and smaller solutions you do not hear so much about, but may be exactly the solution that addresses the specific challenges you have.

The latest entry on the list, Reifier, is one of the latter ones.

Matching data records and identifying duplicates in order to achieve a 360-degree view of customers and other master data entities is the most frequently mentioned data quality issue. Reifier is an artificial intelligence (AI) driven solution that tackles that problem.

Read more about Reifier here.

New entry Reifier

Syncsort Nabs Pitney Bowes Software Solutions

Today it was announced that Syncsort acquires Pitney Bowes Software Solutions. In the announcement it is said that “The acquisition, Syncsort’s largest ever, brings to the company best-in-class location intelligence, data enrichment, customer information management and customer engagement solutions that are highly complementary to its existing portfolio”.

Pitney Bowes has offered a data quality oriented suite called Spectrum. Back in the 00’s Pitney Bowes acquired pioneer data quality tool vendor Group1 (and intended to buy FirstLogic).

Syncsort has its data quality tool from the Trillium Software acquisition.

Both Pitney Bowes and Syncsort were well positioned in the latest Gartner Magic Quadrant for Data Quality Tools as reported in the post Data Quality Tools are Vital for Digital Transformation.

Pitney Bowes has also been recognized as a MDM platform vendor by Forrester as mentioned in the post Several Sources of Truth about MDM / PIM Solutions.

Will be exciting to see how long Syncsort will move into the data management space. Will Syncsort stay with name and address data quality and customer data or will they, as many other vendors on the market, move towards a multidomain MDM and more comprehensive data management offering?

Syncsort Pitney Bowes Group1 FirstLogic Trillium

The Disruptive MDM, PIM and Data Quality Solution List

This blog has a sister site called The Disruptive MDM / PIM List.

The site is a list of available solutions for:

  • Master Data Management (MDM) and
  • Product Information Management (PIM)

as well as:

  • Application Data Management (ADM) – kind of NEW,
  • Customer Data Integration (CDI),
  • Customer Data Platform (CDP),
  • Data Quality Management (DQM) – NEW on the list,
  • Digital Asset Management (DAM),
  • Product Data Syndication (PDS),
  • Product experience Management (PxM) and
  • Reference Data Management (RDM) – NEW.

You can use this site as an addition to the likes of Gartner, Forrester, MDM Institute and others when selecting your new MDM, PIM and data quality solution, not at least because this site will include both larger and smaller disruptive solutions.

Vendors can register their solutions here.

Organizations on the look for a solution within these disciplines can inspect the solutions here.

 

Data Management New Wordle

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?

Unifying Data Quality Management, MDM and Data Governance

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.

DG DQ and MDM

 

Looking at The Data Quality Tool World with Different Metrics

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:

Information Difference DQ Landscape Q1 2019

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.