MDM For the Finance Domain

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Most Master Data Management implementations revolve around the business partner (customer/supplier) domain and/or the product domain. But there is a growing appetite for also including the finance domain in MDM implementations. Note that the finance domain here is about finance related master data that every organization has and not the specific master data challenges that organizations in the financial service sector have. The latter topic was covered on this blog in a post called “Master Data Management in Financial Services”.

In this post I will examine some high-level considerations for implementing an MDM platform that (also) covers finance master data.

Finance master data can roughly be divided into these three main object types:

  • Chart of accounts
  • Profit and cost centers
  • Accounts receivable and accounts payable

Chart of Accounts

The master data challenge for the chart of accounts is mainly about handling multiple charts of accounts as it appears in enterprises operating in multiple countries, with multiple lines of business and/or having grown through mergers and acquisitions.

For that reason, solutions like Hyperion have been used for decades to consolidate finance performance data for multiple charts of accounts possibly held in multiple different applications.

Where MDM platforms may improve the data management here is first and foremost related to the processes that take place when new accounts are added, or accounts are changed. Here the MDM platform capabilities within workflow management, permission rights and approval can be utilized.

Profit and Cost Centers

The master data challenge for profit and cost centers relates to an extended business partner concept in master data management where external parties as customers and suppliers/vendors are handled together with internal parties as profit and cost centers who are internal business units.

Here silo thinking still rules in my experience. We still have a long way to go within data literacy in order to consolidate financial perspectives with HR perspectives, sales perspectives and procurement perspectives within the organization.

Accounts Receivable and Accounts Payable

The accounts receivable data has an overlap and usually share master data with customer master data that are mastered from a sales and service perspective and accounts payable data has an overlap and usually share master data with supplier/vendor master data that are mastered from a procurement perspective.

But there are differences in the selection of parties covered as touched on in the post “Direct Customers and Indirect Customers”. There are also differences in the time span of when the overlapping parties are handled. Finally, the ownership of overlapping attributes is always a hard nut to crack.

A classic source of mess in this area is when you have to pay money to a customer and when you get money from a supplier. This most often leads to creation of duplicate business partner records.

MDM Platforms and Finance Master Data

Many large enterprises use SAP as their ERP platform and the place of handling finance master data. Therefore, the SAP MDG-F offer for MDM is a likely candidate for an MDM platform here with the considerations explored in the post “SAP and Master Data Management”.

However, if the MDM capabilities in general are better handled with a non-SAP MDM platform as examined in the above-mentioned post, or the ERP platform is not (only) SAP, then other MDM platforms can be used for finance master data as well.

Informatica and Stibo STEP are two examples that I have worked with. My experience so far is though that compared to the business partner domain and the product domain these platforms at the current stage do not have much to offer for the finance domain in terms of capabilities and methodology.

SAP and Master Data Management

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SAP is the predominant ERP solution for large companies, and it seems to continue that way as nearly all these organizations are currently on the path of updating and migrating from the old SAP version to the new SAP S/4 HANA solution.

During this process there is a welcomed focus on master data and the surrounding data governance. For the technical foundation for that we see 4 main scenarios:

  • Handling the master data management within the SAP ERP solution
  • Adding a plugin for master data management
  • Adding the SAP MDG offering to the SAP application stack
  • Adding a non-SAP MDM offering to the IT landscape

Let’s have a brief look into these alternative options:

MDM within SAP ERP

In this option you govern the master data as it is represented in the SAP ERP data model.

When going from the old R/3 solution to the new S/4 solution there are some improvements in the data model. This includes:

  • All business partners are in the same structure instead of having separate structures for customers and suppliers/vendors as in the old solutions.
  • The structure for material master objects is also consolidated.
  • For the finance domain accounts now also covers the old cost elements.

The advantage of doing MDM within SAP ERP is avoiding the cost of ownership of additional software. The governance though becomes quite manual typically with heavy use of supplementary spreadsheets.

Plugins

There are various forms of third-party plugins that help with data governance and ensuring the data quality for master data within SAP ERP. One of those I have worked with is it.master data simplified (it.mds).

Here the extra cost of ownership is limited while the manual tasks in question are better automated.

SAP MDG

MDG is the master data management offering from SAP. As such, this solution is very well suited for handling the master data that sits in the SAP ERP solution as it provides better capabilities around data governance, data quality and workflow management.

MDG comes with an extra cost of ownership compared to pure SAP ERP. An often-mentioned downside of MDG compared to other MDM platforms is that handling master data that sits outside SAP is cumbersome in SAP MDG.

Other MDM platforms

Most companies have master data outside SAP as well. Combined with that other MDM platforms may have capabilities better suited for them than SAP MDG and/or have a lower cost of ownership than SAP MDG. Therefore, we see that some organizations choose to handle also SAP master data in such platforms.

Examples of such platforms I have worked with are Informatica, Stibo STEP, Semarchy, Reltio and MDO (Master Data Online from Prospecta Software).

While MDO is quite close to SAP other MDM platforms, for example Informatica and Stibo STEP, have surprisingly very little ready-made capability and methodology for co-existing with SAP ERP.

Master Data Management in Financial Services

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Master Data Management (MDM) has a lot of common considerations regardless of the industry where MDM will be blueprinted and implemented and then there are some key aspects to consider specifically for a given industry.

A recent blog post here on the blog examined the specialties of doing Master Data Management (MDM) in the manufacturing sector. The post was called 4 Key Aspects of Master Data Management in Manufacturing.

In this post two specialties of doing MDM in the financial service sector will be examined. These are:

  • The impact of regulations
  • Data domains in focus

The Impact of Regulations

Doing business in the financial service sector is heavily impacted by the various regulations enforced by authorities across the globe. These regulations aim at improving market confidence, financial stability, and consumer protection and stretch across concepts as Know Your Customer (KYC), Anti Money Laundering (AML) and many more. Doing data management in the financial service sector revolves a lot around adhering to these regulations and the embedded concepts.

When implementing a Master Data Management (MDM) solution in the financial sector you will need a broad set of capabilities that both cover the traditional goals of managing master data as for example getting a Single Customer View (SCV) and then also the specifics of what is entailed in and demanded for Knowing Your Customer (KYC).  

Data Domains in Focus

In master data management we often focus on three major data domains: Customer master data, supplier master data and product master data. These domains are also relevant for master data management in financial services however often with some other meaning, naming, and additions.

The customer is king in any business. In financial services a customer is though more often being regarded as one of several different party roles that are involved in business processes. The customer in various roles is one of more counterparties being involved in transactions taking place. Therefore, we for example see the party domain being more naturally accepted by business stakeholders in the financial service sectors than we until now see in other industries.

From Master Data Management to Data Hub Strategy

The requirements for master data management in the financial service sector will quite early in the journey lead to that solutions must be able to encompass Extended Master Data Management. This can also be seen as a data hub strategy where data quality and data governance play a crucial role.

You can learn more about these considerations in a webinar co-hosted by Semarchy with participants from Sumitomo, HSBC and Bloomberg discussing How to establish data quality and data governance for analytics in financial services.

Which Data Management KPIs Should You Measure?

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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.

A Guide to Data Quality

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While working with some exciting strategic data management projects together with the data management consultancy firm parsionate, the quest of ensuring data quality in large companies is one of the key topics.

Your Success Factors

In their latest whitepaper parsionate has put data quality in context. The idea behind is this is that only when your data quality initiatives are connected with business goals they will be acknowledged and sustained in business operations.

Marketing departments today want to drive more sales through online channels. To do that you will need a bunch of data quality improvements like having convincing product descriptions for all products put on sale online and having consistent and updated prices across all channels.

In operative management you always strive for making better decisions. To be able to do that you need accurate, updated, and well-related information about markets, products, competitors.

In strategic management your aim is to exploit economies of scale. During mergers and acquisitions, managers must pay particular attention to data quality. In the case of mergers, it must be ensured that the data quality of the previously separate systems is impeccable so that weaknesses are not ported to the new overall situation.

For HR key objectives are to find the best candidates and develop potential. These processes are being digitalized with machine decisions involved. This can only work if the undelaying data is complete, updated and consistent.

For logistics the future belongs to the intelligent supply chain. In many cases the data needed to support this is available, however not in the right quality at the right time. Here, the right data quality management can make a huge difference. 

Source: parsionate, data quality in context

The Right Steps to Drive Business Forward

Your roadmap to high data quality that will pave the way to successful business should involve the following 8 steps:

1: Appoint responsible persons for the data

2: Set targets and Key-Performance-Indicators

3: Evaluate data quality of existing data

4: Cleanse and harmonize data inventories

5: Define standards and processes

6: Automate data quality maintenance

7: Regulate data quality across divisions, groups and borders

8: Continuously improve data quality

Learn More

To get more details on the range of success factors for the various business areas and the 8 step roadmap you can download a free copy of the parsionate Data Quality in Context guide here.

Extended MDM Revisited

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Master Data Management (MDM) continues to scale as explored in the post about how MDM inflates.

One inflating trend can be named Extended MDM, which is about taking other data domains than the traditional customer, supplier, and product domains under the MDM umbrella and thus creating a governed data hub.

Which entities that will be is industry specific. Examples from retail are warehouses, stores, and the equipment within those. Examples from pharma are own and affiliated plants, hospitals and other served medical facilities. Examples from manufacturing are plants, warehouses as well as the products, equipment and facilities where the produced products are used within.

Extended MDM can be seen as a twin discipline to a data hub strategy with the twist that the MDM approach adds governance to reflecting, integrating, and meshing critical application in a centralized fashion. This trend was touched in the post MDM Terms on the Move in the Gartner Hype Cycle with reference to the latest Hype Cycle for Data and Analytics Governance and Master Data Management.

The digital twin concept is another trend that, so to speak, is a twin of the Extended MDM trend. You can view all the traditional objects (domains) in MDM and the new to come as digital twins as explained in the post 4 Concepts in the Gartner Hype Cycle for Digital Business Capabilities that will Shape MDM.

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.

Three Augmented Data Management Flavors

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What is Augmented Data Management?

The term augmented data management has become a hyped topic in the data management world. “Augmented” is here used to describe an extension of the capabilities that is now available for doing data management with these characteristics:

  • Inclusion of Machine Learning (ML) and Artificial Intelligence (AI) methodology and technology to handle data management challenges that until now have been poorly solved using traditional methodology and technology
  • Encompassing graph approaches and technology to scale and widen data management coverage towards data that is lesser structured and have more variation than data that until now has been formally managed as an asset
  • Aiming at automating data management tasks that until now have been solved in manual ways or simply not been solved at all due to the size and complexity of the work involved.

Augmented data management can be applied to all the data management disciplines we know. In the following I will have a look at three data management disciplines where we today see solutions and implementations emerging. These are:

  • Augmented Metadata Management
  • Augmented Master Data Management
  • Augmented Data Quality Management

Augmented Metadata Management

The word metadata has been around for ages and the importance of metadata management as a prerequisite for proper data management is commonly agreed on among data management professionals. However, the concrete examples of successful enterprise-wide implementations are sparse. Even more, examples of solutions that are governed and maintained over time are rare.

Metadata management is a daunting task. Doing a snapshot of the metadata in play within an enterprise just now is hard enough. Maintaining this as new data types are utilized, applications are replaced, the organization changes, new standards are adopted, and more is even more daunting.

So, here augmented metadata management comes with a promise of automating this task by providing active metadata management, that is enabled by using machine learning and artificial intelligence components and relying on graph approaches that are able to picture complex relationships between metadata.

Augmented Master Data Management

Master Data Management (MDM) solutions are being implemented around the clock in large and midsize organizations. As these solutions become a part of business processes there are people responsible for controlling and maintaining master data. While some of this work can be automated through Robotic Automation Processes (RPA) there is still a substantial amount of work that relies on decision making not easily solved that way. Add to that, that more and more data will become part of MDM solutions.

So, here augmented master data management comes with a promise of automating these tasks by using machine learning and artificial intelligence components that where feasible can rely on graph approaches that are able to picture complex relationships between master data.

Augmented Data Quality

The promise of automating data quality tasks through machine learning and artificial intelligence is not new at all. For decades this approach has been tried out in areas such as data matching and product classification.

What we see now is that this approach has matured and is more widespread utilized, including going from being standalone specialty solutions to being components in broader data management solutions.  

One example of how data quality, master data management and metadata management is supported by augmented data management in a mature solution is showcased in a video embedded in the Semarchy blog post How Augmented Data Management Adds Value to Your Business.

PS: The term augmented stems from music – raising a chord to new height

4 Key Aspects of Master Data Management in Manufacturing

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Master Data Management (MDM) has a lot of common considerations regardless of the industry where MDM will be blueprinted and implemented and then there are some key aspects to consider specifically for a given industry.

For manufacturing I have found these 4 aspects as key areas when making the roadmap and deciding on Master Data Management architecture principles:

● The impact of Internet of Things (IoT) and Industrial Internet of Things (IIoT)
● Balancing global and local
● Mix of implementation styles
● Direct and indirect customers

The impact of Internet of Things (IoT) and Industrial Internet of Things (IIoT)

More and more produced products are smart devices. This goes for household appliances, power tools, cars and much more. Thus, they are part of the Internet of Things (IoT) meaning that each instance of the product (each produced thing) has its own identity, with a specific configuration, with specific ownership and caretaker-ship and each thing is producing streams of data. This will considerably extend the reach of Data Management and will require your Master Data Management to be open towards business partners.

For manufacturing the producing equipment is also smart devices with a lot of data involved and this can only be sustainable maintained and governed by a master data approach. This realm is sometimes called Industrial Internet of Things (IIoT) which is a facet of Industry 4.0.

Balancing global and local

In manufacturing you can only centralize master data management to a certain degree. There are manufacturing and adjacent processes that are best kept localized due to essential variances in product characteristics, geographic differences and other specializations in line of work.

Therefore, finding the right balance between global and local is a crucial point in blueprinting your manufacturing data management solution, reaching the best fit Master Data Management architecture and building the overarching data governance framework.

Mix of implementation styles

For the same reasons you will not be able to follow a full-blown centralized Master Data Management implementation style. You will need to go for a consolidation Master Data Management style but not necessarily for all data domains and subdomains. You can mix these two styles in what can be seen as a co-existence Master Data Management style.

Direct and indirect customers

In manufacturing your direct customers are typically distributors and retailers who have the end-user customers as their direct customers. However, it happens that you have a business scenario where the same end-users also become your direct customer as a manufacturer. Also, you as a manufacturer for many reasons will benefit from loyally share the end-user customer data with your business partners.

Your Master Data Management implementation should cater for providing a true 360-degree view on customers in this complex business setup.

Learn more

One good resource for a deeper dive into the Master Data Management Architecture considerations in manufacturing is a presentation by my long-time data management peer Magnus Wernersson and Pekka Tamminen of Solita. Find the YouTube video here provided by Semarchy: Volvo Cars – Data Centricity & Digital Innovations with MDM Architecture.

The Business Value Behind Top 3 MDM Trends

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The title of this post is also the title of a webinar I will present on the 24th March 2022 together with Michael Weiss of parsionate.

The top 3 Master Data Management (MDM) trends in question are:

  • Interenterprise MDM
  • Extended MDM
  • Augmented MDM

Please join me on the webinar for a discussion about what is behind these trends and not at least what will be the business impact.

Register here for The Business Value Behind Top 3 Master Data Management Trends.