MDM Trend: Data as a Service

A recent post on this blog was called Five Disruptive MDM Trends. One of the trends mentioned herein is MDM in the cloud and one form of Master Data Management in the cloud in the picture is Data as a Service (DaaS).

DaaS within MDM

Using Data as a Service in the cloud within MDM solutions is a great way of ensuring data quality. You have access to real-time validation and enrichment of master data and you can also use third party and second party services in the on-boarding processes and then avoid typing in data with the unavoidable human errors that else is the most common root cause of data quality issues.

Some of the most common data services useful in MDM are:

Address Verification and Geocoding

When handling location data having a valid and standardized description of postal addresses and in many cases also a code that tells about the geographic position is crucial in MDM.

Postal address verification can either be exploited by a global service such as Loqate from GB Group or AddressDoctor, which is part of the Informatica offering. Alternatively, you can use national services that are better (but also narrowly) aligned with a given address format within a country and the specific extra services available in some countries.

Geocodes can either by latitude and longitude or flat map friendly geocoding systems such as UTM coordinates or WGS84 coordinates.

Business Directory Services

When handling party master data as B2B customers, suppliers and other business partners in is useful to validate and enrich the data with third party reference data and in some cases even onboard through these sources.

Again, there are global and local options. The most commonly used global is Dun & Bradstreet, who operates a database called WorldBase that holds business entities from all over the world in a uniform format and also provides data about the company family trees on a global basis. Alternatively, many countries have a national service provided by each government with formats and data elements specific to that country.

Citizen Directory Services

When handling party master data as B2C customers, employees and other personal data the third-party possibilities are sparser in general, naturally because of privacy concerns.

In Scandinavia, where I live, these data are available from public sources based on either our national ID or a correct name and address.

Data pools and Product Data Lake

When handling product master data and product information there are for some product groups and product attributes in some geographies data pools available. The most commonly used global service is GDSN from GS1.

Alternatively (or supplementary), for all other product groups, product attributes and digital assets and in all other geographies, you can use a service like the one I am working with and is called Product Data Lake.

Connecting Silos

The building next to my home office was originally two cement silos standing in an industrial harbor area among other silos. These two silos are now transformed into a connected office building as this area has been developed into a modern residence and commercial quarter.

Master Data Management (MDM) is on similar route.

The first quest for MDM has been to be a core discipline in transforming siloed data stores within a given company into a shared view of the core entities that must be described in the same way across different departmental views. Going from the departmental stage to the enterprise wide stage is examined in the post Three Stages of MDM Maturity.

But as told in this post, it does not stop there. The next transformation is to provide a shared view with trading partners in the business ecosystem(s) where your company operates. Because the shared data in your organization is also a silo when digital transformation puts pressure on each company to become a data integrated part of a business ecosystem.

A concept for doing that is described on the blog page called Master Data Share.

Silos
Connected silos in Copenhagen North Harbor – and connecting data silos enterprise wide and then business ecosystem wide

The Intelligent Enterprise of the Future, Informatica Style

Yesterday I had the pleasure of attending the Informatica MDM 360 and Data Governance Summit in London including being in a panel discussing best practices for your MDM 360 journey. The rise of Artificial Intelligence (AI) in Master Data Management (MDM) was a main theme at this event.

Informatica has a track record of innovating in new technologies in the data management space while also acquiring promising newcomers in order to fast track their market offering. So it is with AI and MDM at Informatica too. Informatica currently has two tracks:

  • clAIre – the clairvoyant component in the Informatica portfolio that “using machine learning and other AI techniques leverages the industry-leading metadata capabilities of the Informatica Intelligent Data Platform to accelerate and automate core data management and governance processes”.
  • Informatica Customer 360 Insights which is the new branding of the recent AllSight acquisition. You can learn about that over at The Disruptive Master Data Management Solutions List in the entry about Informatica Customer 360 Insights.

At the Informatica event the synergy between these two tracks was presented as the Intelligent 360 View. Naturally, marketing synergies are the first results of an acquisition. Later we will – hopefully – see actual synergies when the technologies are to be aligned, positioned and delivered to customers who want to be an intelligent enterprise of the future.

Infa Intelligent Enterprise of the Future

Five Disruptive MDM Trends

As any other IT enabled discipline Master Data Management (MDM) continuously undergo a transformation while adopting emerging technologies. In the following I will focus on five trends that seen today seems to be disruptive:

Disruptive MDM

MDM in the Cloud

According to Gartner the share of cloud-based MDM deployment has increased from 19% in 2017 year to 24 % in 2018 and I am sure that number will increase again this year. But does it come as SaaS (Software as a Service), PaaS (Platform as a Service) or IaaS (Infrastructure as a Service)? And what about DaaS (Data as a Service). Learn more in the post MDM, Cloud, SaaS, PaaS, IaaS and DaaS.

Extended MDM Platforms

There is a tendency on the Master Data Management (MDM) market that solutions providers aim to deliver an extended MDM platform to underpin customer experience efforts. Such a platform will not only handle traditional master data, but also reference data, big data (as in data lakes) as well as linking to transactions. Learn more in the post Extended MDM Platforms.

AI and MDM

There is an interdependency between MDM and Artificial Intelligence (AI). AI and Machine Learning (ML) depends on data quality, that is sustained with MDM, as examined in the post Machine Learning, Artificial Intelligence and Data Quality. And you can use AI and ML to solve MDM issues as told in the post Six MDM, AI and ML Use Cases.

IoT and MDM

The scope of MDM will increase with the rise of Internet of Things (IoT) as reported in the post IoT and MDM. Probably we will see the highest maturity for that first in Industrial Internet of Things (IIoT), also referred to as Industry 4.0, as pondered in the post IIoT (or Industry 4.0) Will Mature Before IoT.

Ecosystem wide MDM

Doing Master Data Management (MDM) enterprise wide is hard enough. But it does not stop there. Increasingly every organization will be an integrated part of a business ecosystem where collaboration with business partners will be a part of digitalization and thus we will have a need for working on the same foundation around master data. Learn more in the post Multienterprise MDM.

Six MDM, AI and ML Use Cases

One of the hottest trends in the Master Data Management (MDM) world today is how to exploit Artificial Intelligence (AI) and ignite that with Machine Learning (ML).

This aspiration is not new. It has been something that have been going on for years and you may argue about when computerized decision support and automation goes from being applying advanced algorithms to being AI. However, the AI and ML theme is getting traction today as part of digital transformation and whatever we call it, there are substantial business outcomes to pursue.

As told in the post Machine Learning, Artificial Intelligence and Data Quality perhaps all use cases for applying AI is dependent on data quality and MDM is playing a crucial role in sustaining data quality efforts.

Some of the use cases for AI and ML in the MDM realm I have come across over the years are:

6 MDM, AI and ML use cases

Translating between taxonomies: As reported in the post Artificial Intelligence (AI) and Multienterprise MDM emerging technologies can help in translating between the taxonomies in use when digital transformation sets a new bar for utilizing master data in business ecosystems.

Transforming unstructured to structured: A lot of data is kept in an unstructured way and to in order to systematically exploit these data in AI supported business process we need make data more structured. AI and ML can help with that too.

Data quality issue prevention: Simple rules for checking integrity and validating data is good – but unfortunately not good enough for ensuring data quality. AI is a way to exploit statistical methods and complex relationships.

Categorizing data: Digital transformation, spiced up with increasing compliance requirements, has made data categorization a must and AI and ML can be an effective way to solve this task that usually is not possible for humans to cover across an enterprise.

Data matching: Establishing a link between multiple descriptions of the same real-world entity across an enterprise and out to third party reference data has always been a pain. AI and ML can help as examined in the post The Art in Data Matching.

Improving insight: The scope of MDM can be enlarged to Extended MDM Platforms where other data as transactions and big data is used to build a 360-degree of the master data entities. AI and ML is a prerequisite to do that.

 

Artificial Intelligence (AI) and Multienterprise MDM

The previous post on this blog was called Machine Learning, Artificial Intelligence and Data Quality. In here the it was examined how Artificial Intelligence (AI) is impacted by data quality and how data quality can impact AI.

Master Data Management (MDM) will play a crucial role in sustaining the needed data quality for AI and with the rise of digital transformation encompassing business ecosystems we will also see an increasing need for ecosystem wide MDM – also called multienterprise MDM.

Right now, I am working with a service called Product Data Lake where we strive to utilize AI including using Machine Learning (ML) to understand and map data standards and exchange formats used within product information exchange between trading partners.

The challenge in this area is that we have many different classification systems in play as told in the post Five Product Classification Standards. Besides the industry and cross sector standards we still have many homegrown standards as well.

Some of these standards (as eClass and ETIM) also covers standards for the attributes needed for a given product classification, but still, we have plenty of homegrown standards (at no standards) for attribute requirements as well.

Add to that the different preferences for exchange methods and we got a chaotic system where human intervention makes Sisyphus look like a lucky man. Therefore, we have great expectations about introducing machine learning and artificial intelligence in this space.

AI ML PDL

Next week, I will elaborate on the multienterprise MDM and artificial theme on the Master Data Management Summit Europe in London.

A Master Data Mind Map

Please find below a mind map with some of the data elements that are considered to be master data.

Master Data Mind Map

The map is in no way exhaustive and if you feel some more very important and common data elements should be there, please comment.

The data elements are grouped within the most common master data domains being party master data, product master data and location master data.

Some of the data elements have previously been examined in posts on this blog. This include:

The mind map has a selection of flags around where master data are geographically dependent. Again, this is not exhaustive. If you have examples of diversities within master data, please also comment.

Who is on The Disruptive MDM / PIM List?

The Disruptive Master Data Management Solutions List is a sister site to this blog. This site is aimed to be a list of available:

  • Master Data Management (MDM) solutions
  • Customer Data Integration (CDI) solutions
  • Product Information Management (PIM) solutions
  • Digital Asset Management (DAM) solutions.

You can use this site as an alternative to the likes of Gartner, Forrester, MDM Institute and others when selecting a MDM / CDI / PIM / DAM solution, not at least because this site will include both larger and smaller disruptive MDM solutions.

Vendors can register their solutions here and the crowd, being processional users, can review the solutions.

So far these solutions have been listed:

Reltio thumb

Reltio provides all the benefits of cloud like simplicity, scale, and security. On top of that, Reltio breaks down data silos by providing a unified data set with personalized views of data across departments like sales, marketing and compliance. Learn more about Reltio Cloud here.

thumbnailRiversand is an innovative global pioneer in information management. The powerful MDM, PIM and DAM solution help enterprises to transform their raw data into an engine of growth by making data usable, useful and meaningful. Learn more about Riversand here.

Semarchy IconSemarchy xDM is a platform that enables Intelligent MDM and Collaborative Data Governance. It leverages smart algorithms, an agile design, and scales to meet enterprise complexity with solid ROI. Learn more about Semarchy xDM here.

Contentserv thumbContentserv offers a real-time Product Experience Platform being recognized and recommended by international analysts as one of the top worldwide innovators and strong performers in the PIM & MDM space. Learn more about Contentserv here.

ewEnterWorks, which recently was joint with Winshuttle is a multi-domain master data solution for acquiring, managing and transforming a company’s multi-domain master data into persuasive and personalized content for marketing, sales, digital commerce and new market opportunities. Learn about Enterworks here.

SyncForce-plus-icon

SyncForce helps international consumer & professional packaged goods manufacturers realize Epic Availability. With SyncForce, your product portfolio is digitally available with a click of a button, in every shape and form, both internal and external. Learn about SyncForce here.

Dynamicweb thumb

Dynamicweb PIM brings you fewer applications, integrations and systems. It is fast and inexpensive to implement and maintain, because it is part of an all-in-one platform for omni-channel commerce. Learn more about Dynamicweb PIM here.

Agility thumbAgility® empowers marketers to acquire, enrich and deliver accurate and timely product content through every touchpoint, channel and region along with the analytical support required to maximize effectiveness in the market. Learn more about Agility here.

Magnitude thumbMagnitude Software’s Master Data Management solution offers enterprises the core capabilities to model multiple data entities, harmonize the data sources and manage governance processes for reference data and master data. Learn more about Magnitude MDM here.

AllsightAllSight, which is now a part of Informatica, is using state-of-the-art AI-driven technology in an MDM and Customer 360 solution. AllSight matches and links all customer data and provides multiple views of the customer for different users.

Smallest

Product Data Lake, which is affiliated to this blog, is a cloud service for sharing product master data in the business ecosystems of manufacturers, distributors, merchants, marketplaces and large end users of product information. Learn more about Product Data Lake here.

Disruptive MDM M and A

 

Real-World Multidomain MDM Entities

In Master Data Management (MDM) we strive to describe the core entities that are essential to running a business. Most of these entities are something that exists in the real-world. We can organize these entities in various groups as for example parties, things and locations or by their relation to the business buy-side, sell-side and make-side (production).

Multidomain MDM

The challenge in MDM is, as in life in general, that we use the same term for different concepts and different terms for the same concept.

Here are some of the classic issues:

  • An employee is someone who works within an organization. Sometimes this term must be equal to someone who is on the payroll. But sometimes it is also someone who works besides people on the payroll but is contracting and therefore is more like a vendor. Sometimes employees buy stuff from the organization and therefore acts as a customer.
  • Is it called vendor or supplier? The common perception is that a vendor brings the invoice and the supplier brings the goods and/or services. This is often the same legal entity but not too seldom two different legal entities.
  • What is a customer? There are numerous challenges in this question. It is about when a party starts being a customer and when the relationship ends. It is about whether it is a direct or an indirect customer. And also: Is it a business-to-consumer (B2C) customer, a business-to-business (B2B) or a B2B2C customer?
  • Besides employees, vendors and customers (and similar terms) we also care about other parties being business partners. We care about those entities that we must engage with in order to influence our sales. In manufacturing or reselling building materials you for example build relationships with the architects and engineers who choose the materials to be used for a building.
  • Traditionally product master data management has revolved around describing a product model which can be produced and sold in many instances over time. With the rise of intelligent things and individually configured complex products, we increasingly must describe each instance of a product as an asset. This adds to the traditional asset domain, where only a few valuable assets have been handled with focus on the financial value.
  • Each party and each thing have one and most often several relationships with a geographic location (besides digital locations as for example websites).

The relationships within multi-domain MDM was examined further in the post 3 Old and 3 New Multi-Domain MDM Relationship Types.

The latest and hottest trends within MDM

Leading up to the Nordic Midsummer I am pleased to join Informatica and their co-hosts Capgemini and CGI at two morning seminars on how successful organizations can leverage data to drive their digital transformation, the needed data strategy and the urge to have a 360-view of data relationships and interactions.

My presentations will be an independent view on the question: What are the latest and hottest trends within Master Data Management?

In this session, I will give the audience a quick walk-through visiting some in vogue topics as MDM in the cloud, MDM for big data, embracing Internet of Things (IoT) within MDM, business ecosystem wide MDM and the impact of Artificial Intelligence (AI) on MDM.

The events will take place, and you can register to be there, as follows:

Infa Nordic morning seminars 2019