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.
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
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.
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.
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.
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.
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.
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:
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.
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.
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
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.
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!
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
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.
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.