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).
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).
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
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.
It has been good to see that the first 2-digit number of people have requested their solution list based on their specific requirements. A few have also provided the feedback, that they actually already had made a list. In these cases, I am happy that the responses were, that the result from the automated selection process corresponded very well with their traditional and (I guess) time and money consuming selection project.
The set of requirements I have processed have been very varying and thus the solution lists have also been somewhat different encompassing a lot of solution vendors both being on The Disruptive MDM / PIM List as well as those recognized by Gartner, Forrester and Information Difference.
Anyone else who would like to jumpstart a tool selection? Start here.
In a data hub encompassing master data, reference data, critical application data and more, data discovery can play a significant role in the continuous improvement of data quality and how data is governed, managed and measured along with an ever evolving business model and new data driven services.
Data discovery serves as the weapon used when exploring the as-is data landscape at your organization with the aim of building a data hub that reflects your data model and data portfolio. As the data maturity is continuously improved reflected in step-by-step maturing to-be states, data discovery can be used when increasing the data hub scope by encompassing more data sources, when new data driven services are introduced and the business model is enhanced as part of a digital transformation.
In that way data discovery is an indispensable node in maturing the data supply chain and the continuously data quality improvement cycle that must underpin your digital transformation course.
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.
If you are a merchant (retailer or a B2B dealer) of tangible goods a huge challenge in today’s data driven world is the get complete product information from your suppliers being the importers, brand owners and/or manufacturers of the products.
There are plenty of bad ways of trying to do that:
Send them a spreadsheet to be filled in
Build a supplier portal where they can do the work
Get the data from a data pool
Outsource the collection process to someone far away
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.
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.
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.
Tony continues: “The investment in master data within ecosystems is going to increase dramatically. People are going to realise that most of the waste that happens is at the seams of large organisations – not having a common language between the accounts payable of one company and the accounts receivable of another company means both companies are wasting resources and money.”
This way of looking at MDM as something that goes beyond each organization and evolves to be ecosystem wide is also called Multienterprise MDM.
The Disruptive MDM / PIM List is list of solutions in the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) space.
The list presents both larger solutions that also is included by the analyst firms in their market reports and smaller solutions you do not hear so much about, but may be exactly the solution that addresses the specific challenges you have.
The latest entry on the list, Reifier, is one of the latter ones.
Matching data records and identifying duplicates in order to achieve a 360-degree view of customers and other master data entities is the most frequently mentioned data quality issue. Reifier is an artificial intelligence (AI) driven solution that tackles that problem.