Data Governance

Data Governance Necessities

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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.

What’s in a Data Governance Framework?

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When you are going to implement data governance one key prerequisite is to work with a framework that outlines the key components of the implementation and ongoing program.

There are many frameworks available. A few are public while most are legacy frameworks provided by consultancy companies.

Anyway, the seven main components that you will (or should) see in a data governance framework are these:

  • Vision and mission: Formalizing a statement of the desired outcome, the business objectives to be reached and the scope covered.
  • Organization: Outlaying how the implementation and the continuing core team is to be organized, their mandate and job descriptions as well as outlaying the forums needed for business engagement.
  • Roles and responsibilities: Assigning the wider roles involved across the business often set in a RACI matrix with responsible, accountable, to be consulted and to be informed roles for data domains and the critical data elements within.
  • Business Glossary: Creation and maintenance of a list of business terms and their definitions that must be used to ensure the same vocabulary are used enterprise-wide when operating with and analyzing data.
  • Data Policies and Data Standards: Documentation of the overarching data policies enterprise-wide and for each data domain and the standards for the critical data elements within.
  • Data Quality Measurement: Identification of the key data quality indicators that support general key performance indicators in the business and the desired goals for these.
  • Data Innovation Roadmap: Forecasting the future need of new data elements and relationships to be managed to support key business drivers as for example digitalization and globalization.

Other common components in and around a data governance framework are the funding/business case, data management maturity assessment, escalation procedures and other processes.

What else have you seen or should be seen in a data governance framework?   

The Forrester Data Governance Wave 2021

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Solutions for data governance are still rare. However, more and more organizations are looking for the technology part of the data governance discipline to underpin the else predominant people and process part of this challenge.

The Forrester Data Governance Wave 2021 is a list of solutions for data governance. As rightfully stated in the report: “Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. While doing so, organizations must take care to maintain employee, partner, and customer trust in their approach of leveraging data (and technology fueled by data). This requires data governance and data governance solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.”

The wave looks like this:

The solutions included seems to be a mix of data governance pure players, data privacy and data protection specialists and more general data management solution providers.

Erwin has been better known for their data modelling technology, which they still do also.

Infogix was acquired by Precisely recently and as they also recently have acquired PIM/MDM technology, the Infogix solution may become part of a wider stack.

Ataccama is also a recognized MDM and Data Quality Tool vendor.

Not surprisingly Informatica is missing from the list as Informatica and Forrester seem to have dysfunctional relationship. I think the list is incomplete without Informatica – and IBM as well, though they do all the other data management stuff too. Like SAP who is in there.

You can, against your Personal Identifiable Information, get a free copy of the report from Ataccama here.

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.  

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.

2021 Data Management Mind Map

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Disciplines come and go in the data management world. Here is a mind map of the disciplines on top of my mind today. Some of the disciplines goes back to the emerge of IT in the previous millennium and some have risen during the latest years.

MDM of Material and Parts Data

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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

Customer Data Platform (CDP) vs Master Data Management (MDM)

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A recent Gartner report states that: “Organizations that fail to understand their use cases, desired business outcomes and customer data governance requirements have difficulty choosing between CDPs and MDM solutions, because of overlapping capabilities.”

Indeed. This topic was examined here on the blog last year in the post CDP: Is that part of CRM or MDM?

Gartner compare the two breeds of solutions this way:

  • CDPs are marketing-managed tools designed for the creation, segmentation and activation of customer profiles. … These platforms have less governance functionality than MDM solutions and tend to focus on delivering a complete view through the amalgamation of data generated by digital customer interactions.
  • MDM solutions are more mature technology that also enable customer 360 insights by creating and managing a central, persisted system or index of record for master customer records. They enable governance and management of the core data that uniquely identifies one customer as distinct from another. They were built to support enterprisewide sources and applications of customer data.

CDP platforms (via CRM applications) seems to hit from outside in without getting to the core of customer centrecity. MDM solutions are hitting the bullseye and some of the MDM solutions are moving inside out in the direction of extended MDM, where all customer data, not just customer master data, is encompassed under the same data governance umbrella.

CDP vs MDM

Get the Gartner report Choose Between Customer Data Platforms and MDM Solutions for 360-Degree Customer Insights through Reltio here.

MDM / PIM / DQM Resources

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Last week The Resource List went live on The Disruptive MDM / PIM / DQM List.

The rationale behind the scaling up of this site is explained in the article Preaching Beyond the Choir in the MDM / PIM / DQM Space.

These months are in general a yearly peak for conferences, written content and webinars from solution and service providers in the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) space. With the covid-19 crises conferences are postponed and therefore the content providers are now ramping up the online channel.

As one who would like to read, listen to and/or watch relevant content, it is hard to follow the stream of content being pushed from the individual providers every day.

The Resource List on this site is a compilation of white papers, ebooks, reports, podcasts, webinars and other content from potentially all the registered tool vendors and service providers on The Solution List and coming service list. The Resource List is divided into sections of topics. Here you can get a quick overview of the content available within the themes that matters to you right now.

The list of content and topics is growing.

Check out The Resource List here.

MDM PIM DQM resourcesPS: The next feature on the site is planned to be The Case Study List. Stay tuned.

How the Covid-19 Outbreak Can Change Data Management

Henrik Gabs Liliendahl2 Comments

From sitting at home these are my thoughts about how data management can be changed due to the current outbreak of the Covid-19 (Corona) virus and the longer-term behaviour impact after the pandemic hopefully will be over.

Ecommerce Will Grow Faster

Both households and organizations are buying more online and this trend is increasing due to the urge of keeping a distance between humans. The data management discipline that underpins well executed ecommerce is Product Information Management (PIM). We will see more organizations implementing PIM solutions and we must see more effective and less time-consuming ways of implementing PIM solutions.

Data Governance Should Mature Faster

The data governance discipline has until now been quite immature and data governance activities have been characterized by an endless row of offline meetings. As data governance is an imperative in PIM and any other data management quest, we must shape data governance frameworks that are more ready to use, and we must have online learning resources available for both professionals and participating knowledge workers with various roles.

Data Sharing Could Develop Faster

People, organizations and countries initially act in a selfish manner during a crisis, but we must realize that collaboration including data sharing is the only way forward. Hopefully we will see more widespread data sharing enterprise wide as this will ease remote working. Also, we could see increasing interenterprise (business ecosystem wide) data sharing which in particular will ease PIM implementations through automated Product Data Syndication (PDS).

Covid Data Management