10 Data Management TLAs You Should Know

TLA stands for Three Letter Acronym. The world is full of TLAs. The IT world is full of TLAs. The Data Management world is full of TLAs. Here are 10 TLAs from the data management world that have been mentioned a lot of times on this blog and the sister blog over at The Disruptive MDM / PIM / DQM List:

MDM = Master Data Management can be defined as a comprehensive method of enabling an enterprise to link all of its critical data to a common point of reference. When properly done, MDM improves data quality, while streamlining data sharing across personnel and departments. In addition, MDM can facilitate computing in multiple system architectures, platforms and applications. You can find the source of this definition and 3 other – somewhat similar – definitions in the post 4 MDM Definitions: Which One is the Best?

PIM = Product Information Management is a discipline that overlaps MDM. In PIM you focus on product master data and a long tail of specific product information related to each given classification of products. This data is used in omni-channel scenarios to ensure that the products you sell are presented with consistent, complete and accurate data.

DAM = Digital Asset Management is about handling rich media files often related to master data and especially product information. The digital assets can be photos of people and places, product images, line drawings, brochures, videos and much more. You can learn more about how these first 3 mentioned TLAs are connected in the post How MDM, PIM and DAM Stick Together.

DQM = Data Quality Management is dealing with assessing and improving the quality of data in order to make your business more competitive. It is about making data fit for the intended (multiple) purpose(s) of use which most often is best to achieved by real-world alignment. It is about people, processes and technology. When it comes to technology there are different implementations as told in the post DQM Tools In and Around MDM Tools.

RDM = Reference Data Management encompass those typically smaller lists of data records that are referenced by master data and transaction data. These lists do not change often. They tend to be externally defined but can also be internally defined within each organization. Learn more in the post What is Reference Data Management (RDM)?

10 TLA

CDI = Customer Data Integration, which is considered as the predecessor to MDM, as the first MDMish solutions focussed on federating customer master data handled in multiple applications across the IT landscape within an enterprise. You may ask: What Happened to CDI?

CDP = Customer Data Platform is an emerging kind of solution that provides a centralized registry of all data related to parties regarded as (prospective) customers at an enterprise. Right now, we see such solutions coming both from MDM solution vendors and CRM vendors as reported in the post CDP: Is that part of CRM or MDM?

ADM = Application Data Management, which is about not just master data, but all critical data however limited to a single (suite of) application(s) at the time. ADM is an emerging term and we still do not have a well-defined market as examined in the post Who are the ADM Solution Providers?

PXM = Product eXperience Management is another emerging term that describes a trend to distance some PIM solutions from the MDM flavour and more towards digital experience / customer experience themes. Read more about it in the post What is PxM?

PDS = Product Data Syndication, which connects MDM, PIM (and other) solutions at each trading partner with each other within business ecosystems. As this is an area where we can expect future growth along with the digital transformation theme, you can get the details in the post What is Product Data Syndication (PDS)?

Data Quality Tool Market Musings

There is a market for data quality tools and most of the tools on this market have been operating since before year 2k either still by an independent data quality tool provider or now as part of a data management suite.

The prominent market reports telling about the market with generic ranking of the solutions are from Gartner and Information Difference (and earlier on from Forrester, who though have not published a report on data quality tools for years now).

The latest Gartner report from March 2019 was examined in the post Data Quality Tools are Vital for Digital Transformation. Information Difference has their report available here: The Data Quality Landscape – Q1 2019.

As told in the post DQM Tools In and Around MDM Tools there are three main ways of providing Data Quality Management (DQM) capabilities: As an independent data quality tool, as part of a data management suite or inside an MDM platform. The data quality tool market reports encompasses the first two of three options (while the MDM solution market reports encompasses the latter two of three).

DQ Tool Market

The data quality tools represented in the above-mentioned DQ markets reports are:

  • Informatica, who acquired the veteran DQ service SSA-Name3, Similarity Systems and other DQ services, now part of the Informatica data management suite
  • Experian, who acquired among others veteran DQ service QAS and also offers other solutions mainly related to credit check and fraud prevention
  • Syncsort, who acquired veteran DQ service Trillium, recently also Pitney Bowes and also have some other data management services
  • IBM, who acquired DQ services as Ascential now being part of a data management suite with several overlapping services
  • SAP, who bought several DQ services now being part of the huge ERP/data management suite
  • SAS Institute, who acquired Dataflux that now is part of the BI focused suite
  • Talend, as part of a data management suite
  • Oracle, who acquired veteran DQ service Datanomic and other DQ services now being part of the ERP/data management suite
  • Information Builders, as part of a data management and BI suite
  • Ataccama, together with MDM services
  • Melissa, a veteran company in DQ
  • Uniserv, a veteran company in DQ
  • Innovative, a veteran company in DQ
  • Datactics, a veteran company in DQ
  • MIQsoft
  • RedPoint Global
  • BackOffice Associates, as part of a data governance focused solution

However, there are a lot of other data quality tools on the market.

PS: If you represent a vendor providing DQM capabilities as an independent data quality tool, as part of a data management suite or inside an MDM / PIM solution, you are welcome to register your solution on The Disruptive MDM / DQM List here.

The People Behind the MDM / PIM Tools

Over at the sister site, The Disruptive MDM / PIM List, there are some blog posts that are interviews with some of the people behind some of the most successful Master Data Management (MDM) and Product Information Management (PIM) tools.

People behind MDM tools

CEO & Founder Upen Varanasi of Riversand Technologies provided some insights about Riversand’s vision of the future and how the bold decisions he had made several years ago led to the company’s own transformational journey and a new MDM solution. Read more in the post Cloud multi-domain MDM as the foundation for Digital Transformation.

In a recent interview FX Nicolas, VP of Products at Semarchy, tells about his MDM journey and explains how the Semarchy Intelligent Data Hub™:

  • Extends the scope of data available via the data hub beyond core master data
  • Takes an end-to-end approach for the data management initiative
  • Transparently opens the initiative to the whole enterprise

Read the full interview here.

I hope to be able to present more people behind successful solutions on The Disruptive MDM / PIM List Blog.

When Vendors Decline to Participate in Analyst Research

In the Master Data Management (MDM) and Product Information Management (PIM) space there are some analyst market reports with vendor rankings used by organizations when doing a tool selection project.

These reports are based on the analyst’s survey at their customers and perhaps other end user organizations as well as the analysts research in corporation with the solution vendor. However, sometimes the latter part does not happen.

One example was the Gartner late 2017 Magic Quadrant for MDM solutions where IBM declined to participate as reported in the post Why IBM Declined to Participate in The Gartner MDM Magic Quadrant.

Another example is the Forrester and Informatica dysfunctional relationship. In the Forrester 2019 MDM Wave it is stated that “Informatica declined to participate in our research. This was also apparent in the Forrester 2018 PIM Wave where Forrester’s placement of Informatica as a Germany-based vendor didn’t reflect movements (and perhaps achievements) since 2012 as told in the post MDM Alternative Facts.

Both Gartner and Forrester have though positioned IBM and Informatica in their plot with the note that the research did not include interaction with the vendor.

Analyst Relationship

Information Difference has taken another approach and does not include nonparticipating vendors as discussed in the post Movements in the MDM Vendor Landscape 2019.

This challenge is a bit close to me as I am running a list of MDM / PIM / DQM vendors where there now also is a ranking service based on individual context, scope and requirements. Here I have chosen to include vendor solutions on the three above analyst reports and the list itself as noted in select your solution step 4.

MDM / PIM solution ranking based on your context, scope and requirements

Popular Master Data Management (MDM) and Product Information Management (PIM) market reports with solution vendor rankings as the Gartner MDM Magic Quadrant, the Forrester MDM / PIM wave and the Information Difference MDM Landscape are generic, meaning they are not based on the specific context, scope and requirements that every organization on the look for a solution has.

So, no organization can just pick the solution positioned in the top right corner at their favourite analyst firm, neither make a shortlist with the solutions being most top-right or even a longlist with the well positioned solutions. They will need own research or consultancy from the report makers or consultancy firms – or yours truly.

Well, until now.

At The Disruptive MDM / PIM List there is a new service that, based on information about your context, scope and requirements, will provide a solution list that is fit for you.

Your solution list

Curious? Go to select your solution here.

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

 

Top 15 MDM / PIM Requirements in RFPs

A Request for Proposal (RFP) process for a Master Data Management (MDM) and/or Product Information Management (PIM) solution has a hard fact side as well as there are The Soft Sides of MDM and PIM RFPs.

The hard fact side is the detailed requirements a potential vendor has to answer to in what in most cases is the excel sheet the buying organization has prepared – often with the extensive help from a consultancy.

Here are what I have seen as the most frequently included topics for the hard facts in such RFPs:

  • MDM and PIM: Does the solution have functionality for hierarchy management?
  • MDM and PIM: Does the solution have workflow management included?
  • MDM and PIM: Does the solution support versioning of master data / product information?
  • MDM and PIM: Does the solution allow to tailor the data model in a flexible way?
  • MDM and PIM: Does the solution handle master data / product information in multiple languages / character sets / script systems?
  • MDM and PIM: Does the solution have capabilities for (high speed) batch import / export and real-time integration (APIs)?
  • MDM and PIM: Does the solution have capabilities within data governance / data stewardship?
  • MDM and PIM: Does the solution integrate with “a specific application”? – most commonly SAP, MS CRM/ERPs, SalesForce?
  • MDM: Does the solution handle multiple domains, for example customer, vendor/supplier, employee, product and asset?
  • MDM: Does the solution provide data matching / deduplication functionality and formation of golden records?
  • MDM: Does the solution have integration with third-party data providers for example business directories (Dun & Bradstreet / National registries) and address verification services?
  • MDM: Does the solution underpin compliance rules as for example data privacy and data protection regulations as in GDPR / other regimes?
  • PIM: Does the solution support product classification and attribution standards as eClass, ETIM (or other industry specific / national standards)?
  • PIM: Does the solution support publishing to popular marketplaces (form of outgoing Product Data Syndication)?
  • PIM: Does the solution have a functionality to ease collection of product information from suppliers (incoming Product Data Syndication)?

Learn more about how I can help in the blog page about MDM / PIM Tool Selection Consultancy.

MDM PIM RFP Wordle

MDM Megavendors vs the Other MDM Vendors

In the latest Gartner Master Data Management Solutions magic quadrant it is stated that “Gartner-estimated 2016 revenues of the four largest vendors commanded over 77% of the market (SAP, IBM, Oracle and Informatica). These four held a Gartner-estimated market share of just over 73% in 2017.”

There seems to be a little decrease in the dominance from the megavendors, though the market according to Gartner is still ruled by the big four. The number of licenses sold by these vendors and those midsize vendors who also are in the quadrant can be found in the post Counting MDM Licenses.

For the future trend it is also worth noticing, that Oracle is not a part of the MDM magic quadrant anymore, as Oracle in the Gartner lingo is not an MDM vendor, but an ADM vendor today. Oracle is not included in the latest Forrester MDM wave either.

Another market distinction is around MDM versus Product Information Management (PIM) solutions. The post Several Sources of Truth about MDM / PIM Solutions examines the positioning by Gartner and Forrester and in that sense the magavendors are better at MDM than PIM.

Any Gartner estimation will be biased towards large vendors having large clients as these are the Gartner clients. In a LinkedIn discussion a big four person suggested that the market is fragmented and there are many MDM-like solutions.

This remark followed the market estimation from a fresh market report from Information Difference. The positioning results from here were shown in the post Movements in the MDM Vendor Landscape 2019. In here the megavendors did not perform so well on the technology axis, which is largely made up by customer satisfaction feedback in the underlying survey.

So, what do you think? Will the megavendors still rule the MDM market or will the midsize and smaller vendors get a larger piece of the cake?

MDM megavendors

For bigger picture click here.

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.