Precisely Becomes a Multidomain Vendor

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Yesterday Precisely announced that they are going to acquire Winshuttle.

This acquisition comes just after that Precisely took over Infogix as reported in the post Precisely Nabs Another Old One. Also, Precisely, then named Syncsort, took over a part of Pitney Bowes not too long ago as examined in the post Syncsort Nabs Pitney Bowes Software Solutions.

The previous acquisitions have strengthened the Precisely offerings around data quality for the customer master data domain and the adjacent location domain.

The Winshuttle take over will make Precisely a multidomain vendor adding cross domain capabilities and specific product domain capabilities.

The original Winshuttle capabilities revolves around process automation for predominately SAP environments covering all master data domains and further Application Data Management (ADM).

As Winshuttle recently took over the Product Information Management (PIM) solution provider Enterworks, this will bring capabilities around product master data management and thus make Precisely a provider for a broad spectrum of master data domains.

The interesting question will be in what degree Precisely over the time will be willing to and able to integrate these different solutions so a one-stop-shopping experience will become a one-stop digital experience for their clients.

Conflicting Analyst Views on the PIM Market

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As reported in the previous post here on this blog Forrester published their Product Information Management (PIM) 2021 Q2 Wave last week.

Practically simultaneously Ventana Research published their 2021 Vendor and Product Assessment for Product Information Management (PIM).

The two vendor rankings are here:

The methodology and lingo differ a bit, however the ranking is, as with all these kinds of analyst rankings, based on that the vendors are assessed more positive the closer they are to the top right corner.

The two analyst firms are in more or less agreement about some vendors while some vendors are assessed quite different. These are in particular:

  • Informatica, who is assessed much more negative by Forrester than by Ventana. It is a part of the story that Informatica for a long time has declined to participate in Forrester’s PIM assessments.
  • Akeneo, who is a new vendor among the major players, and has a better debut at Ventana than at Forrester.
  • Stibo Systems, who has been a leader at Forrester for some years but has moved down to a modest position at Ventana in the latest ranking.

Looking at assessing the vendors against the others is close to me as part of the Select Your Solution service on The Disruptive MDM / PIM / DQM List. Here the assessment is based on the actual context, scope and requirements for you as a potential buyer (or someone who is helping a potential buyer). When doing that it is natural that a given vendor can be closest to the top right corner in some cases and not in other cases.

That analysts in a generic ranking reaches a different result only underpins that solution selection is not easy and requires a substantial knowledge about the available solutions, where they come from and where they are heading.

If you need help navigating in this jungle, ping me here:

Forrester PIM Wave Q2 2021

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The Forrester Wave™ Product Information Management Q2 2021 is out.

In here, Forrester has identified the in their eyes 10 most significant solutions — Akeneo, Contentserv, IBM, Informatica, inRiver, Riversand, Salsify, Stibo Systems, Syndigo, and Winshuttle — and researched, analyzed, and scored them.

The previous PIM wave from 2018 was examined here on the blog in the post There is no PIM quadrant, but there is a PIM wave.

Here is the new one and the old one:

So, what is status quo and what has changed?

Status Quo

Stibo Systems is still close to the right top corner and thereby cementing their role as a leader in PIM.

Informatica still has a dysfunctional relationship with Forrester and has not participated in this report either. This has not helped with their positioning in the ranking.

IBM is still in the lower rankings.

Changed

Salsify has moved up and grown.

Riversand has moved up and grown a bit – and has been accompanied by Syndigo who by the way just bought them today.

Enterworks, now as part of Winshuttle, has moved down – but grown.

Contentserv has moved down and shrunk. So has inRiver.

Akeneo has entered the PIM wave.

SAP and Agility Multichannel (now part of Magnitude) has been dropped from this report.

Missing

Compared to Gartner, who only has a Master Data Management (MDM) Quadrant, Via Medici is a major MDM/PIM player missing in this report.

Big Data vs Small and Wide Data under a Master Data Lens

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One of the 10 trends in data and analytics in 2021 identified by Gartner, the analyst firm, is a shift from big data to small and wide data.

A press release from yesterday elaborates on this topic outside the paywall. Here Gartner Says 70% of Organizations Will Shift Their Focus from Big to Small and Wide Data By 2025.

As said in there: “Potential areas where small and wide data can be used are demand forecasting in retail, real-time behavioural and emotional intelligence in customer service applied to hyper-personalization, and customer experience improvement.”

This is a topic close to me and something I wrote about, still using the term big data, last year in a Reltio whitepaper as mentioned in the post How to Use Connected Master Data to Enable New Revenue Models.

Small data is in my eyes very much equivalent to master data besides the meaning promoted by Gartner, which is approaches involving “certain time-series analysis techniques or few-shot learning, synthetic data, or self-supervised learning”.

The concrete wide data to be used and connected in the retail scenario is customer data and product data. There is a current trend of mastering wide customer data in a Customer Data Platform (CDP). Wide product data are best handled in a Product Information Management (PIM) platform with a collaborative Product Data Syndication (PDS) add-on.  

In the quest of providing hyper-personalization, you need to connect well identified customer data with product information elements aimed for customization and personalization by applying Artificial Intelligence (AI) methodologies.

So, is the term “small and wide data” better than “big data”?

I think it, besides the narrow analytic purpose forwarded by Gartner, can help unlocking the opportunities in master data underpinned big data that have existed the past decade but that have- by far – not been utilized as much as it could.   

The Intersection of Supplier MDM and Customer MDM

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When blueprinting a Master Data Management (MDM) solution one aspect is if – or in what degree – you should combine supplier MDM and customer MDM. This has been a recurring topic on this blog as for example in the post How Bosch is Aiming for Unified Partner Master Data Management.

In theory, you should combine the concept for these two master domains in some degree. The reasons are:

  • There is always an overlap of the real-world entities that has both a customer and a supplier role to your organization. The overlap is often bigger than you think not at least if you include the overlap of company family trees that have members in one of these roles.
  • The basic master data for these master data domains are the same: Identification numbers, names, addresses, means of communication and more.
  • The third-party enrichment opportunities are the same. The most predominant possibilities are integration with business directories (as Dun & Bradstreet and national registries) and address validation (as Loqate and national postal services).

In practice, the problem is that the business case for customer MDM and supplier MDM may not be realized at the same time. So, one domain will typically be implemented before the other depending on your organization’s business model.

Solution Considerations

Most MDM solutions must coexist with an – or several – ERP solutions. All popular enterprise grade ERP solutions have adapted the business partner view with a common data model for basic supplier and customer data. This is the case with SAP S/4HANA and for example the address book in Microsoft Dynamics AX and Oracle JD Edwards.

MDM solutions themselves does also provide for a common structure. If you model one domain before the other, it is imperative that you consider all business partner roles in that model.

Data Governance Considerations

A data governance framework may typically be rolled out one master data domain at the time or in parallel. It is here essential that the data policies, data standards and business glossary for basic customer master data and basic supplier master data is coordinated.

Business Case Considerations

The business case for customer MDM will be stronger if the joint advantages with supplier MDM is incorporated – and vice versa.

This includes improvement in customer/supplier engagement and the derived supply/value chain effectiveness, cost sharing of third-party data enrichment service expenses and shared gains in risk assessment.  

Product Model vs Product Instance

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When working with the product domain in Master Data Management (MDM) and with Product Information Management (PIM) we have traditionally been working with the product model meaning that we manage data about how a product that can be produced many times in exactly the same way and resulting in having exactly the same features. In other words, we are creating a digital twin of the product model.

As told in the post Spectre vs James Bond and the Unique Product Identifier the next level in product data management is working with each product instance meaning each produced thing that have a set of data attached that is unique to that thing. Such data can be:

  • Serial number or other identification as for example the Unique Device Identification (UDI) known in healthcare
  • Manufacturing date and time
  • Specific configuration
  • Current and historical position
  • Current and historical owner
  • Current and historical installer, maintainer and other caretaker
  • Produced sensor data if it is a smart device

There is a substantial business potential in being better than your competitor in managing product instances. This boils down to that data is power – if you use the data.

When managing this data, we are building a digital twin of the product instance.

Maintaining that digital twin is a collaborative effort involving the manufacturer, the logistic service provider, the owner, the caretaker, and other roles. For that you need some degree of Interenterprise MDM.

Precisely Nabs Another Old One

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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.

Privacy and Confidentiality Concerns in Interenterprise Data Sharing

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Exchange of data between enterprises – aka interenterprise data sharing – is becoming a hot topic in the era of digital transformation. As told in the post Data Quality and Interenterprise Data Sharing this approach is the cost-effective way to ensure data quality for the fast-increasing amount of data every organization has to manage when introducing new digital services.

McKinsey Digital recently elaborated on this theme in an article with the title Harnessing the power of external data. As stated in the article: “Organizations that stay abreast of the expanding external-data ecosystem and successfully integrate a broad spectrum of external data into their operations can outperform other companies by unlocking improvements in growth, productivity, and risk management.”

The arguments against interenterprise data sharing I hear most often revolves around privacy and confidentiality concerns.

Let us have a look at this challenge within the two most common master data domains: Party data and product data.

Party Data

The firm CDQ talk about the case for sharing party data in the post Data Sharing: A Brief History of a Crazy Idea. As said in here: The pain can be bigger than the concern.

Privacy through the enforced data privacy and data protection regulations as GDPR must (and should) be adhered to and sets a very strict limit for exchanging Personal Identifiable Information only leaving room for the legitimate cases of data portability.

However, information about organizations can be shared not only as exploitation of public third-party sources as business directories but also as data pools between like-minded organizations. Here you must think about if your typos in company names, addresses and more really are that confidential.

Product Data

The case for exchanging product data is explained in the post The Role of Product Data Syndication in Interenterprise MDM.

Though the vast amount of product data is meant to become public the concerns about confidentiality also exist with product data. Trading prices is an obvious area. The timing of releasing product data is another concern.

In the Product Data Lake syndication service I work with there are measures to ensure the right level of confidentiality. This includes encryption and controlling with whom you share what and when you do it.

Data governance plays a crucial role in orchestrating interenterprise data sharing with the right approach to data privacy and confidentiality. How this is done in for example product data syndication is explained in the page about Product Data Lake Documentation and Data Governance.