DQM

2022 Data Management Predictions

Henrik Gabs Liliendahl2 Comments

On the second last day of the year it is time to predict about next year. My predictions for the year gone were in the post Annus Horribilis 2020, Annus Mirabilis 2021?. These predictions were fortunately fluffy enough to claim that they were right.

There is no reason not to believe that the wave of digitalization will go on and even intensify. Also, it seems obvious that data management will be a sweet spot of digitalization.

The three disciplines within data management focussed on at this blog are:

  • MDM: Master Data Management
  • PIM: Product Information Management
  • DQM: Data Quality Management

So, let`s look at what might happen next year within these overlapping disciplines.

MDM in 2022

MDM will keep inflating as explained in the post How MDM inflates. 

More organizations will go for enterprise wide MDM implementations and those who accomplish that will continue to do interenterprise MDM.

More business objects will be handled within the MDM discipline. Multidomain MDM will in more and more cases extend beyond the traditional customer, supplier and product domain.

Intelligent capabilities as Machine Learning (ML) and Artificial Intelligence (AI) will augment the basic IT capabilities currently used within MDM.

PIM in 2022

As with MDM also PIM will go more interenterprise wide. As organizations get a grip on internal product data stores the focus will move to collaborating with external suppliers of product data and external consumers of product data through Product Data Syndication.

In some industries PIM will start extending from the handling the product model to also handling each instance of each product as examined in the post Product Model vs Product Instance.

There will also be a term called augmented PIM meaning using Machine Learning and Artificial Intelligence to improve product data quality. In fact, classification of products using AI has been an early use case of AI in data management. This use case will be utilized more and more besides other product information use cases for AI and ML.

DQM in 2022

Data quality management will also go wider as data quality requirements increasingly will be a topic in business partnerships. More and more contracts between trading partners will besides pricing and timing also emphasize on data quality.

Data quality improvement has for many years been focused on the quality of customer data. This is now extending to other business objects where we will see data quality tools will get better support for other data domains and the data quality dimensions that are essential here.

ML and AI data quality use cases will continue to be implemented and go beyond the current trial stage to be part of operational business processes though still at only a minority of organizations.  

Happy New Year.

The Disruptive MDM/PIM/DQM List 2022: Informatica

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The next vendor to be included on The Disruptive MDM / PIM / DQM List 2022 is Informatica.

Informatica has been a leader on the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) space for many years latest as seen in their 6th time front position on the Gartner MDM quadrant.

Their success is also apparent in the recent Earnings Report and their return to the public markets.

You can learn more about Informatica here.

Few Movements in the Gartner Magic Quadrant for Data Quality Solutions 2021

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The new Gartner® Magic Quadrant™ for Data Quality Solutions is out.

There are only few movements in this quadrant compared to the previous quadrant which was examined in the post From Where Will the Data Quality Machine-Learning Disruption Come?

With vendor positioning some movements are:

  • Ataccama has crossed the line into the leaders quadrant
  • Syniti has become a visionary
  • Datactics has entered the quadrant

Also, Tibco has acquired Information Builders and thus taken their position.

Again, this year Informatica is the most top-right positioned vendor. Good to know, as I am right now involved in some digital transformation programs where Informatica Data Quality (iDQ) is part of the technology stack.

You can get a free copy of the report from Ataccama here.

Precisely Nabs Another Old One

Henrik Gabs Liliendahl2 Comments

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

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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

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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.

2021 Data Management Mind Map

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Disciplines come and go in the data management world. Here is a mind map of the disciplines on top of my mind today. Some of the disciplines goes back to the emerge of IT in the previous millennium and some have risen during the latest years.

Welcome Winpure on The Disruptive MDM / PIM / DQM List

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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

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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

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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.