Everyone agrees that the result your data management efforts should be measured and the way to do that should be to define some Key Performance Indicators that can be tracked.
But what should those KPIs be? This has been a key question (so to speak) in almost all data management initiatives I have been involved with. You can with the tools available today easily define some technical indicators close to the raw data such as percentage of duplicate data records and completeness of data attributes. The harder thing to do is to relate data management efforts to business terms and quantify the expected and achieved results in business value.
A recent Gartner study points out five areas where such KPIs can be defined and measured. The aim is that data / information become a monetizable asset. The KPIs revolves around business impact, time to action, data quality, data literacy and risk.
Get a free copy of the Gartner report on 5 Data and Analytics KPIs Every Executive Should Track from the parsionate site here.
When the talk is about data governance necessities, we often jump to the topic of what a data governance framework should encompass.
However, before embarking on this indeed essential agenda or when reviewing the current state of a data governance framework it is useful to take one step back and consider some core fundamentals to have as a prerequisite to implement and mature a data governance framework.
These prerequisites were recently coined in the Gartner report 7 Must-Have Foundations for Modern Data and Analytics Governance.
A reminder of the importance of having these fundamentals in mind is that only one fifth of organizations who in 2022 are investing in information governance according to Gartner will succeed in scaling governance for digital business.
The 7 Foundations
The first and central foundation in the Gartner approach is Value and Outcomes. Having this in the bullseye is probably not new and surprising to anyone. So, assuming the why is in place it is a question of what and how. According to Gartner the answer is to become less data-oriented and more business-oriented. In the end you will have to make a business-oriented data governance framework.
The next foundation is Accountability and Decision Rights. This theme is a common component of data governance frameworks often disguised as data ownership. Here, think of decision ownership as a starting point.
Then we have Trust. We are going to have less internal data compared to external data. The known data governance frameworks emphasize on internal data. You must mature your data governance framework to provide trust in external data too. And the connection between these two worlds also.
Next one is Transparency and Ethics. Again, this is a well-known topic that is much mentioned probably under the compliance theme, however seldom really infused in the details. Consider this not as a separate course in a data governance framework but as an ingredient in all the courses.
Adjacent to that we have Risk and Security. Business opportunity, risk and security are often handled separately in the organization. A data governance framework is a good place not to handle this in parallel, but to tie these disciplines together.
Don’t forget Education and Training. Make sure that this is defined in the data governance framework but then can be lifted out into the general training and education provided within the organization.
Finally, there is Collaboration and Culture. Data governance goes hand in hand with change management. You must find the best way in your organization to blend policies and standards with storytelling and culture hacks.
Learn More
Would you prefer that your organization will be among the one out of five who will succeed in scaling governance for digital business? Then, as the next step, please find a free link via the parsionate site for the Gartner report on 7 Must-Have Foundations for Modern Data and Analytics Governance here.
In Q4 2021 both Gartner and Forrester published their ranking of MDM solutions.
The two rankings are in some agreement. Both analyst firms have Informatica closest to the top-right corner. However, there are also these divergences:
Leader at Forrester, Challenger at Gartner:
· Reltio has during the last couple of years been better rated at Forrester than by Gartner. According to Forrester, Reltio leverages modern capabilities such as Artificial Intelligence / Machine Learning (AI/ML), knowledge graph, and embedded analytics to support large and complex MDM deployments.
Leaders at Gartner. Strong Performers at Forrester:
· Semarchy has, according to Gartner, a strong understanding of the MDM market, and its longer-term strategy of positioning MDM as a core capability within a data management platform is in sync with the MDM market direction. · Riversand received, according to Gartner, relatively high scores across the breadth of categories representative of the customer engagement life cycle from presales through delivery and support.
Included by Forrester, in Honorable Mentions by Gartner:
· Prospecta Master Data Online provides, according to Forrester, a cloud-based MDM platform, leveraging predefined integration and data models, AI/ML capabilities for an intelligent rules-based approach to enriching data, and integration of data quality and governance. · Magnitude Kalido MDM helps, according to Forrester, define and model critical business information from many domains, including customer, product, financial, and vendor/supplier, to create and manage accurate, integrated, and governed data.
Included by Gartner, but not by Forrester:
· Contentserv had, according to the Gartner survey, the highest levels of satisfaction for overall product capabilities and had the jointhighest satisfaction for ease of deployment. · Tamr utilizes, according to Gartner, augmented data management as a foundational element to its solution, with particular focus on the use of human-trained ML processes to augment entity resolution, data profiling, data mapping and data modelling. · PiLog reviewers highlight, according to Gartner, its strength in duplicate material identification and prevention. Reviewers repeatedly credited their MRDM solution for asset optimization, inventory and procurement cost savings.
The latest Gartner Magic Quadrant for Master Data Management Solutions is out.
If we look at the vendor positioning not much has happened since the January 2021 quadrant. Tamr is a new solution in the quadrant while Cluedin and Pimcore are now listed under honorable mentions. Informatica is as always closest to the top-right corner.
Augmented data management and augmented MDM are the new main buzzwords as seen in the strategic planning assumptions. Also, the vendors capabilities for doing the augmented stuff are stated as strengths and cautions.
So, what is this augmented data management and augmented MDM by the way? It is in short a compilation of utilizing several trending technologies as Machine Learning (ML), Artificial Intelligence (AI), graph approaches (as examined in the post MDM and Knowledge Graph) with the aim of automating and scaling data management including MDM.
If this will be the final shape of whatever augmented MDM is though in my eyes a bit unsure and even Gartner has doomed augmented MDM to be obsolete before reaching the plateau of productivity in their latest hype cycle for Data and Analytics Governance and Master Data Management. Anyway, I am sure we will see some form of extended MDM covering more domains than customer, supplier and product and utilizing the emerging technologies.
If you want to read the full Gartner report for free, you can get it from Informatica here.
Data fabric has been named a key strategic technology trend in 2022 by Gartner, the analyst firm.
According to Gartner, “by 2024, data fabric deployments will quadruple efficiency in data utilization while cutting human-driven data management tasks in half”.
Master Data Management (MDM) and data fabric are overlapping disciplines as examined in the post Data Fabric vs MDM. I have seen data strategies where MDM is put as a subset to data fabric and data strategies where they are separate tracks.
In my head, there is a common theme being data sharing.
Then there is a different focus, where data fabric seems to be focusing on data integration. MDM is also about data integration, but more about data quality. Data fabric takes care of all data while MDM obviously is about master data, though the coverage of business entities within MDM seems to be broadening.
Another term closely tied to data fabric – and increasingly with MDM as well – is knowledge graph. Knowledge graph is usually considered a mean to achieve a good state of data fabric. In the same way you can use a knowledge graph approach to achieve a good state of MDM when it comes to managing relationships – if you include a data quality facet.
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.
This is the fourth and final blog post on the main take away from the fresh published Gartner Magic Quadrant for Master Data Management Solutions 2021.
The first post here touched on the quadrant advancements being the vendors that have moved between the 4 quadrants.
Unfortunately, Gartner has not, as in previous years, stated the revenue for all the vendors, so that you can determine the growth directly. Gartner though mentions, that Semarchy, Reltio and Ataccama had 2-digit revenue growth and that IBM had shrinking MDM revenue – again. We may then assume that the other recurring vendors had 1-digit revenue growth. However, it is mentioned that Riversand had a 10m USD revenue growth, which could indicate a 2-digit revenue growth for them too.
Combining quadrant advancements and revenue growth statements results in this movement overview:
“By 2023, organizations with shared ontology, semantics, governance and stewardship processes to enable interenterprise data sharing will outperform those that don’t.“
Interenterprise data sharing must be leveraged through interenterprise MDM, where master data are shared between many companies as for example in supply chains. The evolution of interenterprise MDM and the current state of the discipline was touched in the post MDM Terms In and Out of The Gartner 2020 Hype Cycle.
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.
In this decade we will see the rise of interenterprise 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.
So, watch out for not applying interenterprise MDM.
PS: That goes for MDM end user organizations and MDM platform vendors as well.
The 2021 Magic Quadrant for Master Data Management (MDM) Solutions went public yesterday as reported here.
Quasimodo is the main protagonist of the novel The Hunchback of Notre-Dame. Somehow the plot of vendors in this year’s MDM quadrant looks like (a caricature of) a hunchback. The vendors are in general better in “Ability to Execute” than in “Completeness of Vision”.
So, MDM vendors in general may lack something in market understanding, marketing strategy, product strategy, innovation and more.
This does resonate with me. As also stated in the quadrant some vendors are too invisible in the market buzz. There are heaps of emerging MDM use cases where it is not that easy to find a suitable solution not to say finding one well-fit solution for a range of use cases in a given organization with a given IT landscape.
Liliendahl on Data Quality 