From Where Will the Data Quality Machine-Learning Disruption Come?

The 2020 Gartner Magic Quadrant for Data Quality Solutions is out.

In here Gartner assumes that: “By 2022, 60% of organizations will leverage machine-learning-enabled data quality technology for suggestions to reduce manual tasks for data quality improvement”.

The data quality tool vendor rankings according to Gartner looks pretty much as last year. Precisely is the brand that last year was in there as Syncsort and Pitney Bowes.

Gartner DQ MQ 2020

Bigger picture here.

You can get a free reprint of the report from Talend or Informatica.

The question is if we are going to see the machine-learning based solutions coming from the crowd of vendors in a bit stalled quadrant or the disruption will come from new solution providers. You can find some of the upcoming machine-learning / Artificial Intelligence (AI) based vendors on The Disruptive MDM / PIM DQM List.

How to Create Great CX Using the Full Potential of MDM

Improved customer experience (CX) is a key driver for digitalization and having optimal Master Data Management (MDM) is a core prerequisite for being successful in providing customer experience.

First, MDM underpins your insights into:

  • Customer identity
  • Customer hierarchies
  • Customer locations
  • Customer transactions
  • Customer footprint on websites
  • Customer footprint in social media
  • Customer preferences
  • Customer privacy and data protection settings and rights

Next, MDM gives you the insight into how to provide a tailored product experience by managing the data supply chain from your suppliers/vendors to each of the customer touch points by:

  • Having your suppliers/vendors syndicating all the product-, service- and other information that is required by your customers
  • Transforming these data into the data structure that fits your customers
  • Consolidating all sources relevant for your customers
  • Enriching with your internal competitive information that delight and engage your customers
  • Customizing to each channel where you have a touch point with your customers
  • Personalizing utilizing rich and structured customer insight

PXM and CX

Learn more in an on-demand webinar hosted by Reltio – you can access the recording here,

Data Marketplaces, Exchanges and Multienterprise MDM

In the recent Gartner Top 10 Trends in Data and Analytics for 2020 trend number 8 is about data marketplaces and exchanges. As stated by Gartner: “By 2022, 35% of large organizations will be either sellers or buyers of data via formal online data marketplaces, up from 25% in 2020.”

The topic of selling and buying data was touched here on the blog in the post Three Flavors of Data Monetization

A close topic to data marketplaces and exchanges is Multienterprise MDM.

In the 00’s the evolution of Master Data Management (MDM) started with single domain / departmental solutions dominated by Customer Data Integration (CDI) and Product Information Management (PIM) implementations. These solutions were in best cases underpinned by third party data sources as business directories as for example the Dun & Bradstreet (D&B) world base and second party product information sources as for example the GS1 Global Data Syndication Network (GDSN).

In the previous decade multidomain MDM with enterprise wide coverage became the norm. Here the solution typically encompasses customer-, vendor/supplier-, product- and asset master data. Increasingly GDSN is supplemented by other forms of Product Data Syndication (PDS). Third party and second party sources are delivered in the form of Data as a Service that comes with each MDM solution.

Data Marketplaces and Exchange

In this decade we will see the rise of multienterprise MDM where the solutions to some extend become business ecosystem wide, meaning that you will increasingly share master data and possibly the MDM solutions with your business partners – or else you will fade in the wake of the overwhelming data load you will have to handle yourself.

The data sharing will be facilitated by data marketplaces and exchanges.

On July 23rd I will, as a representative of The Disruptive MDM/PIM/DQM List, present in the webinar How to Sustain Digital Ecosystems with Multi-Enterprise MDM. The webinar is brought to you by Winshuttle / Enterworks. It is a part of their everything MDM & PIM virtual conference. Get the details and make your free registration here.

MDM of Material and Parts Data

The Often-overlooked MDM Scenario

Most presentations of Master Data Management (MDM) solutions revolve around the scenario of having multiple data stores holding customer master data and the needed capabilities of federation and deduplication in the quest for getting a 360 degree of customers.

Another common scenario is the Product Information Management (PIM) theme, where the quest is to get a 360 degree of the products that is sold to the customers.

However, in for example the manufacturing sector there is a frequent and complex scenario around governing product master data before the products become sellable to customers.

In that scenario we usually use the term material as an alternative to product and we use the term parts for the products bought from suppliers as either components of a finished product, as materials used in Maintenance, Repair and Operation (MRO) and as other supplies.

MDM Capabilities for Material and Parts Data

Some of the key MDM capabilities needed in the material and parts data scenario are:

  • Auto-generated material descriptions which helps with identification and distinction between materials which leads to better utilization of the inventory. This capability can by the way also be used by merchants in reselling PIM scenarios as pondered in the post What’s in a Product Name?.
  • Advanced mass maintenance of material attributes which leads to improved accuracy and consistency and thereby better operational efficiency. Again, this capability is also useful in other MDM scenarios.
  • User friendly maintenance of complex material relationship structures as for example Bill of Material (BOM). This leads to less scrap and rework and improved compliance reporting. This capability can successfully be extended to other internally defined relationship structures in PIM and MDM.

Workflow Management and Data Governance

Handling material and parts master data is collaboration intensive with many business units involved as for example:

  • Procurement
  • Supply Chain / Logistics
  • Production
  • Research & Development
  • Finance

This means that operational efficiency can only be obtained through cross business unit workflows tailored to the data requirements and compliance obligations held by each business unit.

With the degree of enterprise data sharing needed this must be encompassed by data governance framework elements as:

  • Roles and responsibilities for data
  • Data policies and data standards according to business rules
  • Data quality measurement
  • A commonly shared business glossary

Solution Example: Master Data Online (MDO)

In my experience MDM of material and parts data is often done utilizing the ERP application with the shortcomings around data overview, workflow management and data governance that entail.

Therefore, it is good to see when an MDM solution that has the material and parts master data management covered as well as touched in the post Welcome Master Data Online (MDO) from Prospecta on The Disruptive MDM / PIM / DQM List.

The Master Data Online (MDO) solution was born around material and parts master data which is an refreshing exception from the many MDM solutions that were born either from the Customer Data Integration (CDI) solution branch or the Product Information Management (PIM) branch.

You can learn more about material and parts MDM at Prospecta and MDO here.

MDO Material and Parts

Welcome Master Data Online (MDO) from Prospecta on The Disruptive MDM / PIM / DQM List

There is yet a new featured entry on the Disruptive MDM / PIM /DQM List. Master Data Online (MDO) is the flagship product of Prospecta. The solution entered the market in 2008 with its unique ability to solve multiple problems of standardizing spare parts and has grown at a steady pace into a far more evolved and matured solution which focuses around multiple data solutioning across systems like SAP, Salesforce and other leading Enterprise Systems.

With the heritage from solving product master data issues in manufacturing MDO stands out from the crowd of MDM solutions that else have their heritage in Customer Data Integration (CDI) or Product Information Management (PIM). The coexistence with SAP that was part of the original heritage has along with the multi-domain MDM capabilities been developed to cover SalesForce and other popular applications you meet in most IT landscapes.

Besides the core MDM and coexistence with ERP and CRM applications MDO also have a Data Intelligence Workbench (DIW) and a component called MDO Fuse that is made to ease onboarding and provide workflow capabilities.

Learn more about Master Data Online (MDO) here.

Role of MDO

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.

Collaborative Product Data Syndication vs Data Pools and Marketplaces

The previous post on this blog was called Inbound and Outbound Product Data Syndication.

As touched in this post there are two kinds of Product Data Syndication (PDS):

  • The public kind where everyone shares the same product information. The prominent examples are marketplaces and data pools.
  • The collaborative kind where you can exchange the same product information with all your accepted trading partners but also supplement with one-to-one product information that allows the merchant to stand out from the crowd.

When you syndicate to marketplaces everyone can easily watch and get inspired. A creepy kind of inspiration is the one surfacing at the moment where Amazon is believed to copy product data in order to make a physical twin as examined in the Wall Street Journal article telling that Amazon Scooped Up Data From Its Own Sellers to Launch Competing Products.

When syndicating – or synchronizing – through data pools you are limited to the consensus on the range of data elements, structure and format enforced by those who control the data pool – which can be you and your competitors.

With a collaborative PDS solution you can get the best of two worlds. You can have the market standard that makes you not falling behind your competitors. However, you can also have unique content coming through that puts you ahead of your competitors.

Collaborative PDS Data pools and Marketplaces

Right now, I am working with a collaborative PDS solution. This solution welcomes other (collaborative) PDS solutions as part of the product information flow. The solution will also encompass data pools in a reservoir concept. This PDS solution is called Product Data Lake.

Inbound and Outbound Product Data Syndication

If you google for the term Product Data Syndication you will get the explanation in a post on the sister site to this blog. The Disruptive MDM / PIM / DQM list blog post is called What is Product Data Syndication (PDS).

Inbound and Outbound Scenarios

Digging further into this subject one can divide the Product Data Syndication (PDS) scenarios as seen from the individual organization within a supply chain into inbound and outbound product data syndication.

As a merchant/retailer/dealer being downstream in the supply chain you will have these main scenarios:

  • Outbound product data syndication to marketplaces. This is the scenario covered by most solutions that are marketed as PDS solutions. The challenge here is that there are hundreds of marketplaces both internationally and nationally. These marketplaces have each their way of getting the product information. The advantage of such a PDS solution is that you as a merchant only need one downstream feed to (in theory) all marketplaces.
  • Inbound product data syndication from suppliers either directly from the manufacturer or through distributors. There are many ways this is done today stretching exchanging spreadsheets, getting the product information in your supplier portal, fetching the product information from each of the manufacturers customer portal, through data pools and, still in the emerging stage, utilizing a collaborative PDS solution (see further down).
  • Outbound product data syndication to large end users often being manufacturers utilizing MRO (Maintenance, Repair and Operation) parts.

As a manufacturer/brand owner being upstream in the supply chain you will have these main scenarios:

  • Outbound product data syndication to marketplaces, which most often only covers a fraction of the revenue.
  • Outbound product data syndication of product information for finished products to distributors and/or merchants. There are many ways this is done today stretching exchanging spreadsheets, putting the product information in each of the distributors/merchant’s supplier portal, exhibiting the product information in your customer portal, through data pools and, still in the emerging stage, utilizing a collaborative PDS solution (see further down).
  • Inbound product data syndication of product information for raw materials and MRO (Maintenance, Repair and Operation) parts from suppliers being other manufacturers, distributors and/or merchants. There are many ways this is done today stretching exchanging spreadsheets, through data pools and, still in the emerging stage, utilizing a collaborative PDS solution (see further down).

As a distributor/wholesaler being midstream in the supply chain you share the outbound PDS scenarios at manufactures and the inbound PDS scenarios at merchants.

In some cases, a marketplace can act as a data pool too.

Collaborative PDS

A Collaborative PDS Solution

In my eyes a collaborative PDS solution have these capabilities:

  • Catering for a win-win scenario between trading partners by allowing one uniform way of outbound push of product information from upstream trading partners (manufacturers, distributors) and one uniform way of inbound pull of product information at downstream trading partners (distributors, merchants).
  • Ability to work with all in-house Product Information Management (PIM) solutions and/or other in-house applications where product information is managed both for outbound push and inbound pull.
  • Can encompass outbound push to and pull from data pools and even other PDS solutions as elements in the total product information flow embracing both market standard product information and flow of individual product information that makes the merchant stand out from the crowd.

Right now, I am working with a collaborative PDS solution. This solution welcomes other (collaboratve) PDS solutions as part of the product information flow. And of course, also every in-house Product Information Management (PIM) solution out there. This PDS solution is called Product Data Lake.