A Guide to Data Quality

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

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

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.

4 Key Aspects of Master Data Management in Manufacturing

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

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.

4 Concepts in the Gartner Hype Cycle for Digital Business Capabilities that will Shape MDM

Some months ago, Gartner published the latest Hype Cycle for Digital Business Capabilities.

The hype cycle includes 4 concepts that in my mind will shape the future of Master Data Management (MDM) and data management in all. These are:

  • Industrie 4.0
  • Business Ecosystems
  • Digital Twin
  • Machine Customer

Industrie 4.0

You will find Industrie 4.0 near the trough of disillusionment almost ready to climb the slope of enlighten. Several of the recent MDM blue prints I have worked with have Industrie 4.0 as an overarching theme.

Industrie 4.0 is about using intelligent devices in manufacturing and thus closely connected to the term Industrial Internet of Things (IIoT). The impact of industry 4.0 is across the whole supply chain encompassing not only product manufacturing companies but also for example product merchants and product service providers.

With intelligent devices in the supply chain product MDM will evolve from handling data about product models to handle data about each instance of a product.

Business Ecosystems

The concept of business ecosystems has just passed the peak of inflated expectations.

In a modern business environment, no organization can do everything – or even most things – themselves. Therefore, any enterprise needs to partner with other organizations when working on new digital powered business models.

This also calls for increasing sharing of data, including master data, with business partners. This leads to the rise of interenterprise MDM, which by the way is at about the same position in the Hype Cycle for Data Analytics and MDM.

An example of interenterprise data sharing is Product Data Syndication.

Digital Twin

On the climbing side of the peak of inflated expectations we find the concept of a digital twin.

A digital twin is a virtual representation of a real-world entity such as an asset, person, organization, or process. This fits somehow with what MDM is doing which traditionally has been providing virtual descriptions of customers, suppliers, and products.

With the digital twin flavour, you can sharpen and extend MDM in two ways:

  • Have a more real-world on customers and suppliers by looking at those has roles of business partners along with handling many other external and internal organizational entities
  • Putting more asset types than direct products under the MDM umbrella with improved data governance as a result

Machine Customers

A bit further down the climbing side of the peak you will see the concept of the machine customer.

The expectation is that more and more buying tasks will be automated so there will be no human interaction in the bulk of purchasing processes.

This will only be possible if the products involved at those who sell them are digitally described in sufficient details and categorized the same way on the selling and buying side.

This seems like a job for Master Data Management and the adjacent Product Information Management (PIM) discipline where the buying side needs the right capabilities not only for direct trading products but also indirect supplies.

Also, the concept of augmented MDM will play a role here by applying Artificial Intelligence (AI) to the MDM and PIM side of enabling the machine customer.

The Full Report

You can download the full hype cycle report including the complete visual cycle from the parsionate website: Gartner Hype Cycle for Digital Business Capabilities.

A Guide to Master Data Management

Over the recent months, I have been engaged with the boutique Master Data Management (MDM) consultancy parsionate on some visionary projects in the multi-domain MDM area.

The overall approach at parsionate is that MDM is much more than just an IT issue. It is a strategic necessity. This is based on an experience that I share, which is that if you treat MDM as an isolated technological problem, you will inevitably fail!

Multi-domain MDM

MDM implementations today are increasingly becoming enterprise wide and are thus also multi-domain meaning that they cover business partner domains as customer and supplier/vendor, the product domain and a longer tail of other core business entities that matters within the given business model.

The primary goal of a multi-domain MDM implementation is to unify and consolidate heterogeneous data silos across the enterprise. The more source systems are integrated, the more information will be available and the more your enterprise will benefit from a 360° view of its data.

To achieve the desired goal for your multi-domain MDM program, you need to have a clear vision and a long-term strategic roadmap that shows how the various MDM implementation stages fit together with other related initiatives within your enterprise and where the Return of Investment (ROI) is expected and how it can be measured.

Seven Phases of Forming the Roadmap

In the approach at parsionate there are seven phases to go through when forming the roadmap and launching an MDM program.

Phase 1: Identify business needs

Before embarking on an MDM program, consider what data is most important to your business and where you can create the most value by putting this data under the MDM umbrella.

The main rationale is that through MDM organizations can control the way their most important data is viewed and shared in a more efficient, traceable way and thus improve their performance and productivity. An effective MDM implementation helps you streamline your workflows. It breaks down data silos that prevent data from being reused and shared across the enterprise

Phase 2: Set up a data committee

Establishing a data committee (with any equivalent name for a data focussed body) is perhaps the most frequently mentioned aspect of an MDM strategy. This team would usually consist of many different stakeholders who are in a position to enforce the roadmap and the underlaying activities.

This body must be able to convey the importance of data across the enterprise at all organizational levels. The main concern is to make data a top priority in the enterprise.

Phase 3: Set up a data governance program before defining the MDM roadmap

Many organizations shy away from data governance programs that consulting firms suggest because they seem too complex and costly.

The bitter truth is though that if you fail to implement data governance or embed it strategically into the way the organization works, you will inevitably have to address it at a later stage – in a more time-consuming and costly process.

Phase 4: Set clear goals to ingrain the MDM vision in the organization’s culture

It is difficult to promote an MDM program without a clear vision and objectives. At the executive level, the program could be misunderstood as a technological issue. Sometimes decision-makers struggle to understand the value an MDM program will generate. In this case, they will either not fund it or, if it does go ahead, consider it a failure because the result does not meet their expectations.

It is crucial to involve all relevant stakeholders in the roadmap development process at an early stage and engage with them to understand their expectations and requirements. Only then can you ensure that the core elements of a successful MDM program are aligned with the needs of your entire organization.

Phase 5: Choose a step-by-step approach and rapidly implement sub-projects

The most effective way to implement an MDM program is to start with a few key sources and provide meaningful information in a very short time. It is always difficult to implement everything at once (multiple sources, several integration points, and com­plex data) with a „big bang“ or build a data hub without a specific goal in mind.

If a pilot project quickly realizes a series of short-term bene­fits, users and business leaders will appreciate the value of the MDM program. As soon as it becomes clear that the initial project is successful, you can promote a wider roadmap that shows how the next steps will be carried out in line with the strategic goals of your organization. With this iterative approach, the long-term benefits will become clearer.

Phase 6: The mammoth task: Adopt a data-driven mindset

Building a data-driven corporate culture may be considered the supreme challenge of any MDM program. Data culture goes far beyond a simple corporate strategy that uses data for business processes. Rather, it is a mindset that encourages employees to appreciate the tremendous added value of high-quality data.

Many organizations believe they can simply buy new tools to drive digital transformation. That is an illusion. 

Phase 7: Integrate technology

This illusion does however not mean that that MDM technology is not important.

Hardly any other IT system affects as many different departments and data domains in a company and is, implemented the right way, as powerful as an MDM solution. The extreme importance of this type of software within the entire corporate IT infrastructure means that you need to select a system very carefully and strategically.

The Full Guide If you want to read the full guide to MDM mapping out the high road to a successful implementation you can download it from this parsionate site: Master Data Management

2022 Data Management Predictions

On the second last day of the year it is time to predict about next year. My predictions for the year gone were in the post Annus Horribilis 2020, Annus Mirabilis 2021?. These predictions were fortunately fluffy enough to claim that they were right.

There is no reason not to believe that the wave of digitalization will go on and even intensify. Also, it seems obvious that data management will be a sweet spot of digitalization.

The three disciplines within data management focussed on at this blog are:

  • MDM: Master Data Management
  • PIM: Product Information Management
  • DQM: Data Quality Management

So, let`s look at what might happen next year within these overlapping disciplines.

MDM in 2022

MDM will keep inflating as explained in the post How MDM inflates

More organizations will go for enterprise wide MDM implementations and those who accomplish that will continue to do interenterprise MDM.

More business objects will be handled within the MDM discipline. Multidomain MDM will in more and more cases extend beyond the traditional customer, supplier and product domain.

Intelligent capabilities as Machine Learning (ML) and Artificial Intelligence (AI) will augment the basic IT capabilities currently used within MDM.

PIM in 2022

As with MDM also PIM will go more interenterprise wide. As organizations get a grip on internal product data stores the focus will move to collaborating with external suppliers of product data and external consumers of product data through Product Data Syndication.

In some industries PIM will start extending from the handling the product model to also handling each instance of each product as examined in the post Product Model vs Product Instance.

There will also be a term called augmented PIM meaning using Machine Learning and Artificial Intelligence to improve product data quality. In fact, classification of products using AI has been an early use case of AI in data management. This use case will be utilized more and more besides other product information use cases for AI and ML.

DQM in 2022

Data quality management will also go wider as data quality requirements increasingly will be a topic in business partnerships. More and more contracts between trading partners will besides pricing and timing also emphasize on data quality.

Data quality improvement has for many years been focused on the quality of customer data. This is now extending to other business objects where we will see data quality tools will get better support for other data domains and the data quality dimensions that are essential here.

ML and AI data quality use cases will continue to be implemented and go beyond the current trial stage to be part of operational business processes though still at only a minority of organizations.  

Happy New Year.

Gartner MDM Quadrant vs Forrester MDM Wave, Q4 2021

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.

You can find a link to a free copy of the Gartner report in the post The Gartner MDM MQ of December 2021 and Augmented MDM.

The November 2021 Forrester MDM Wave is downloadable from Reltio here.