Precisely Nabs Another Old One

The major data quality tool vendor Precisely announced yesterday that they are to acquire Infogix.

Infogix is a four-decade old provider of solutions for data quality and adjacent disciplines as data governance, data catalog and data analytics.

Precisely was recently renamed from Syncsort. Under this brand they nabbed Pitney Bowes software two years ago as told in the post Syncsort Nabs Pitney Bowes Software Solutions. Back in time Pitney Bows nabbed veteran data quality solution provider Group1.

Before being Syncsort their data quality software solution was known as Trillium. This solution also goes a long way back.

So, it is worth noticing that the M&A activity revolves around data quality software that was born in the previous millennium.

As told in the post Opportunities on The Data Quality Tool Market, this market is conservative.

Opportunities on The Data Quality Tool Market

The latest Information Difference Data Quality Landscape is out. This is a generic ranking of major data quality tools on the market.

You can see the previous data quality landscape in the post Congrats to Datactics for Having the Happiest DQM Customers.

There are not any significant changes in the relative positioning of the vendors. Only thing is that Syncsort has been renamed to Precisely.

As stated in the report, much of the data quality industry is focused on name and address validation. However, there are many opportunities for data quality vendors to spread their wings and better tackle problems in other data domains, such as product, asset and inventory data.

One explanation of why this is not happening is probably the interwoven structure of the joint Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) markets and disciplines. For example, a predominant data quality issue as completeness of product information is addressed in PIM solutions and even better in Product Data Syndication (PDS) solutions.

Here, there are some opportunities for pure play vendors within each speciality to work together as well as for the larger vendors for offering both a true integrated overall solution as well as contextual solutions for each issue with a reasonable cost/benefit ratio.

Get Your Free Bespoke MDM / PIM / DQM Solution List

Many analyst market reports in the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) space have a generic ranking of the vendors.

The trouble with generic ranking is that one size does not fit all.

On the sister site to this blog, The Disruptive MDM / PIM / DQM List, there is no generic ranking. Instead there is a service where you can provide your organization’s context, scope and requirements and within 2 to 48 hours get Your Solution List.

The selection model includes these elements:

  • Your context in terms of geographical reach and industry sector.
  • Your scope in terms of data domains to be covered and organizational scale stretching from specific business units over enterprise-wide to business ecosystem wide (interenterprise).
  • Your specific requirements covering the main capabilities that differentiate the vendors on market.
  • Vendor capabilities.
  • A model that combines those facts into a rectangle where you can choose to:
    • Go ahead with a Proof of Concept with the best fit vendor
    • Make an RFP with the best fit vendors in a shortlist
    • Examine a longlist of best fit vendors and other alternatives like combining more than one solution.
The vendors included are both the major players on the market as well as emerging solutions with innovative offerings.

You can get your free solution list here.

Welcome Winpure on The Disruptive MDM / PIM / DQM List

There is a new kid on the block on The Disruptive MDM / PIM / DQM List. Well, Winpure is not a new solution at all. It is a veteran tool in the data matching space.

Recently the folks at Winpure have embarked on a journey to take best-of-breed data matching into the contextual MDM world.

Data matching is often part of Master Data Management implementations, not at least when the party domain (customers, suppliers, other business partners) is encompassed.

However, it not always the best approach to utilize the data matching capabilities in MDM platforms. In some cases, these are not very effective. In other cases, the matching is needed before data is loaded into the MDM platform. And then many MDM initiatives do not include an MDM platform, but relies on capabilities in ERP and CRM applications.

Here, there is a need for a contextual MDM component with strong data matching capabilities as Winpure.

Learn more about Winpure here.

B2B2C in Data Management

The Business-to-Business-to-Consumer (B2B2C) scenario is increasingly important in Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM).

This scenario is usually seen in manufacturing including pharmaceuticals as examined in the post Six MDMographic Stereotypes.

One challenge here is how to extend the capabilities in MDM / PIM / DQM solutions that are build for Business-to-Business (B2B) and Business-to-Consumer (B2C) use cases. Doing B2B2C requires a Multidomain MDM approach with solid PIM and DQM elements either as one solution, a suite of solutions or as a wisely assembled set of best-of-breed solutions.B2B2C MDM PIM DQMIn the MDM sphere a key challenge with B2B2C is that you probably must encompass more surrounding applications and ensure a 360-degree view of party, location and product entities as they have varying roles with varying purposes at varying times tracked by these applications. You will also need to cover a broader range of data types that goes beyond what is traditionally seen as master data.

In DQM you need data matching capabilities that can identify and compare both real-world persons, organizations and the grey zone of persons in professional roles. You need DQM of a deep hierarchy of location data and you need to profile product data completeness for both professional use cases and consumer use cases.

In PIM the content must be suitable for both the professional audience and the end consumers. The issues in achieving this stretch over having a flexible in-house PIM solution and a comprehensive outbound Product Data Syndication (PDS) setup.

As the middle B in B2B2C supply chains you must have a strategic partnership with your suppliers/vendors with a comprehensive inbound Product Data Syndication (PDS) setup and increasingly also a framework for sharing customer master data taking into account the privacy and confidentiality aspects of this.

This emerging MDM / PIM / DQM scope is also referred to as Multienterprise MDM.

TCO, ROI and Business Case for Your MDM / PIM / DQM Solution

Any implementation of a Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) solution will need a business case to tell if the intended solution has a positive business outcome.

Prior to the solution selection you will typically have:

  • Identified the vision and mission for the intended solution
  • Nailed the pain points the solution is going to solve
  • Framed the scope in terms of the organizational coverage and the data domain coverage
  • Gathered the high-level requirements for a possible solution
  • Estimated the financial results achieved if the solution removes the pain points within the scope and adhering to the requirements

The solution selection (jump-starting with the Disruptive MDM / PIM / DQM Select Your Solution service) will then inform you about the Total Cost of Ownership (TCO) of the best fit solution(s).

From here you can, put very simple, calculate the Return of Investment (ROI) by withdrawing the TCO from the estimated financial results.

MDM PIM DQM TCO ROI Business Case

You can check out more inspiration about ROI and other business case considerations on The Disruptive MDM / PIM /DQM Resource List.

Congrats to Datactics for Having the Happiest DQM Customers

The latest Information Difference Data Quality Landscape is out. The Data Quality Management (DQM) market is, based on the changes seen from last year, a stable market with little movement.

Information Difference DQ Landscape 2019 and 2020

Atacama and Active Prime have joined this year’s landscape and Pitney Bowes has left the market after the take over by Syncsort as reported in the post Syncsort Nabs Pitney Bowes Software Solutions.

The report also measures how happy the end customers are with the vendors: “The happiest customers based on this survey were those of Datactics, followed by those of Syncsort and Active Prime, closely followed by those of Innovative Systems and Melissa Data, then Experian. Congratulations to those vendors.”

Also, this time it strikes again that the mega vendors (IBM, SAP, Informatica) are not in this crowd.

Check out The Information Difference Data Quality Landscape Q1 2020 here.

Get Your Free Bespoke MDM / PIM / DQM Solution Ranking

The Disruptive MDM / PIM / DQM List has an interactive service that can help you jumpstart in your tool selection for a Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) solution.

MDM PIM DQM Context, Scope and RequirementsThe selection model is based on the context, scope and requirements for your solution.

The context includes the geographical reach and the industry where your organization operates.

The scope includes the number of entities as for example consumers (B2C customers), companies (B2B customers, suppliers and other business partners), products and digital assets as well as the organizational reach.

The requirements are those that differentiate the MDM / PIM / DQM solutions on the market.

MDM PIM DQM Vendor capabilitiesThe solution capabilities considered in the selection process are those of who are:

  • On this Disruptive MDM / PIM / DQM Solutions List or
  • Gartner MDM Magic Quadrant or
  • Forrester MDM Wave or Forrester PIM Wave or
  • Information Difference MDM Landscape

MDM PIM DQM AI EngineThese two sets of information are compared in a continuously supervised learning algorithm – also known in marketing as machine learning and artificial intelligence (AI).

Filling in the information usually takes less than 15 minutes. You will get your solution list within 1 to 48 hours.

MDM PIM DQM Ranking OutcomeThe outcome is:

  • The best fit solution for a Proof of Concept
  • Two more solutions to be in a shortlist
  • Four more solutions to be in a longlist
  • If fit, a couple of more solutions to be considered as alternatives or supplements

During the half year this service has been online, more than 100 end user organizations or their consultants have received their solution list.

This service is free. No information is shared with anyone unless requested. Are you ready? Start with step 1 here.

A Tricky Thing with Data Quality Evangelism

One of the major players on the data quality market, Experian, do a yearly survey of the current data management trends. This year is no exception and I just had the chance to read through the 2020 report.

This year’s report revolves around trusted data, data debt and the skills gap in the light of data literacy. As always, the report holds some good percentage take away you can use in your data quality evangelism.

My favourite this year is a bit tricky:

Experian 2020 Data Survey
Source: Experian

I think this one shows a challenging side of data quality evangelism. While operational efficiency is a bit ahead of other reasons to improve data quality, there are many good reasons to improve data quality. And advocating for every kind of goodness is often harder than being able to pinpoint one absolutely good reason.

Well, see for yourself. Get the 2020 Global data management research from Experian Data Quality here.