New terms are constantly emerging in the data management space. One of these are “Data Fabric”.
According to Gartner, the analyst firm, data fabric “enables frictionless access and sharing of data in a distributed network environment.” Usually, one would associate data fabric with big data and edge computing. However, data fabric does embrace all kind of data and computing from the ones mentioned over multi-cloud to traditional on-premise computing and the data stores within.
Data fabric and Master Data Management (MDM) have the same aim, which is that all (master) data must be shared across the enterprise – and eventually also in business ecosystems. This is a prerequisite for successful digital transformation.
Lately, there has also been a development in the conception of MDM, where other data than master data are encompassed in some of the platforms offered as examined in the post Master Data, Product Information, Reference Data and Other Data.
So, there is clearly a union and an intersection of data fabric and MDM.
There is a trend on the data management market that the solutions are either going very niche (best-of-breed) in the data domain covered or they are encompassing a broader range of data types.
This can be seen in the spectrum of master data and product information as reported in the post MDM, PIM or Both.
We also see that governance and management of reference data is included in addition to managing master data as told in the post What is Reference Data Management (RDM)?
Some MDM (and RDM) solutions also extend the reach to cover aspects of transaction data and big data. The main scenarios covered are:
- Matching of party entities in traditional systems of record with the parties referenced in social streams and weblogs (systems of engagement) as well as in sensor data. This can be used in creating a Customer Data Platform (CDP).
- Extending data quality and data performance dashboards related to master data to cover aggregated transaction data and big data held in data warehouses and data lakes by using a shared set of reference data.
When product information is to be shared in business ecosystems through Product Data Syndication (PDS), this can be accelerated by using a data lake concept and new data stores as staging areas. This is due to that a main challenge here is that the data quality standards on the providing side most often are different from the data quality standards on the receiving side.
The diagram is a variation of a diagram included in the whitepaper Intelligent Data Hub – Taking MDM to the Next Level. The original is developed together with Salah Kamel, CEO at Semarchy.
It is hard to find an organization who do not want to be on the digital transformation wagon today. But how can you ensure that your digital transformation journey will be a success? One of the elements in making sure that this data driven process will be a success will be to have a solid foundation of Master Data Management (MDM) including Product Information Management (PIM).
The core concepts here are:
- Providing a 360-degree view of master data entities: Engaging with your customers across a range of digital platforms is a core part of any digital transformation. Having a 360-degree view of your customer has never been more important, and that starts with well-organized and maintained customer master data. The same is true for supplier master data and other party master data. 360-degree view of locations is equally important. The same goes for products and assets as pondered in post Golden Records in Multidomain MDM.
- Enabling happy self-service scenarios: Customer data are gathered from many sources and digital self-registration is becoming the most common used method. The self-service theme has also emerged in handling supplier master data as self-service based supplier portals have become common as the place where supplier/vendor master data is captured and maintained. Interacting with your trading partners on digital platforms and having the most complete product information in front of your customers in self-service online selling scenarios requires a solid foundation for product master data and Product experience Management (PxM).
- Underpinning the best customer experience: Customer experience (CX) and MDM must go hand in hand. Both themes involve multiple business units and digital environments within your enterprise and in the wider business ecosystem, where your enterprise operates. Master data is the glue that brings the data you hold about your customers together as well as the glue that combines this with the data you share about your product offering.
- Encompassing Internet of Things (IoT): Smart devices that produces big data can be used to gain much more insight about parties (in customer and other roles), products, locations and the things themselves. You can only do that effectively by relating IoT and MDM.
Product experience Management (PxM or PXM) is a trending term in the Master Data Management (MDM) and Product Information Management (PIM) world.
As reported in the post Product Something Management the acronym PxM is one of several three letter acronyms starting with P and ending with M in play in this space.
Some of the topics handled in a PxM solution that can be seen as a development of traditional PIM solution are:
- Analysis of product content including encompassing other data domains
- Emphasis on product content that is search engine optimized (SEO)
- Context aware presentation of product content
- Personalization of product content
- Exploiting Machine Learning (ML) and Artificial Intelligence (AI) in the above-mentioned scenarios
Solution vendors on the market are positioning themselves in relation to PxM as some vendors are going in the direction of being a PxM vendor while other vendors embrace the span from MDM over PIM to PxM. This urges solution buyers on the market to focus on implementing PxM from a best of breed vendor in this realm or to look for one-stop-shopping.
You can meet both kind of solution vendors on this list.
Data monetization is about harvesting direct financial results from having access to data that is stored, maintained, categorized and made accessible in an optimal manner. Traditionally data management & analytics has contributed indirectly to financial outcome by aiming at keeping data fit for purpose in the various business processes that produced value to the business. Today the best performers are using data much more directly to create new services and business models.
There are three ways of exploiting data monetization:
- Selling data: This is something that have been known to the data management world for years. Notable examples are the likes of Dun & Bradstreet who is selling business directory data. Another example is postal services around the world selling their address directories. This is the kind of data we know as third party data.
- Wrapping data around products: If you have a product – or a service – you can add tremendous value to these products and services and make them more sellable by wrapping data, potentially including third party data, around those products and services. These data will thus become second party at trading partners.
- Advanced analytics and decision making: You can combine third party data, second party data and first party data (your own data) in order to make advanced analytics and fast operational decision making in order to sell more, reduce costs and mitigate risks.
Master Data Management (MDM) is the core foundation for ensuring the multipurpose data quality of the data you sell, purchase from a third-party provider, push to trading partners, pull from trading partners and use in analytics and decision making. Product Information Management (PIM) is crucial when wrapping data around products and Product Data Syndication is essential when linking the data between trading partners.
On this site there is a list of forward-looking MDM, PIM and Product Data Syndication vendors who can enable your data monetization efforts. Find the list here.