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.

Data Matching and Real-World Alignment

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.

Data Matching and 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.

Location Intelligence

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.

Identity Resolution

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.

Hierarchy Management

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.

Tibco, Orchestra and Netrics

Today’s Master Data Management (MDM) news is that Tibco Software has bought Orchestra Networks. So, now the 11 vendors in last year’s Gartner Magic Quadrant for Master Data Management Solutions is down to 10.

If Gartner is still postponing this year’s MDM quadrant, they may even manage to reflect this change. We are of course also waiting to see if newcomers will make it to the quadrant and make the crowd of vendors in there go back to an above 10 number. Some of the candidates will be likes of Reltio and Semarchy.

Else, back to the takeover of Orchestra by Tibco, this is not the first time Tibco buys something in the MDM and Data Quality realm. Back in 2010 Tibco bought the data quality tool and data matching front runner Netrics as reported in the post What is a best-in-class match engine?

Then Tibco didn’t defend Netrics’ position in the Gartner Magic Quadrant for Data Quality Tools. The latest Data Quality Tool quadrant is also as the MDM quadrant from 2017 and was touched on this blog here.

So, will be exciting to see how Tibco will defend the joint Tibco MDM solution, which in 2017 was a sliding niche player at Gartner, and the Orchestra MDM solution, which in 2017 was a leader at the Gartner MDM quadrant.

Tibco Orchestra Netrics