Tag Archives for " master data management "

When Worlds Collide: Blockchain and Master Data Management

Master Data Management (MDM) is an approach to the management of golden records that have been around for over a decade only to find a growth spurt lately as some organizations are exceeding pain thresholds in the management of common data.

Blockchain has a slightly shorter history, coming aboard with bitcoin, but also is seeing its revolution these days as data gets distributed far and wide and trust has taken center stage in business relationships.

Volumes could be written about each on its own, and given that most organizations still have a way to go with each discipline, that might be appropriate.

However, good ideas wait for no one and today’s idea is MDM on Blockchain.

Thinking back over our MDM implementations over the years, it is easy to see the data distribution network becoming wider.

As a matter of fact, master data distribution is usually the most time-intensive and unwieldy part of an MDM implementation anymore.

The blockchain removes overhead, costs, and unreliability from authenticated peer-to-peer network partner transactions involving data exchange.

It can support one of the big challenges of MDM with governed, bi-directional synchronization of master data between the blockchain and enterprise MDM.

The single version of the truth

Another core MDM challenge is arriving at the “single version of the truth”.

It’s elusive even with MDM because everyone must tacitly agree to the process used to instantiate the data in the first place.

While many MDM practitioners go to great lengths to utilize the data rules from a data governance process, it is still a process subject to criticism.

The consensus that blockchain can achieve is a governance proxy for that elusive “single version of the truth” by achieving group consensus for trust as well as a full lineage of data.

The major challenges in MDM

Blockchain enables the major components and tackles the major challenges in MDM.

Blockchain provides a distributed database, as opposed to a centralized hub, that can store data that is certified, and for perpetuity.

By storing timestamped and linked blocks, the blockchain is unalterable and permanent.

Though not for low latency transactions yet, transactions involving master data, such as financial settlements, are ideal for blockchain and can be sped up by an order of magnitude since blockchain removes the grist in a normal process.

Blockchain uses pre-defined rules that act as gatekeepers of data quality and governs the way in which data is utilized.

Blockchains can be deployed publicly (like bitcoin) or internally (as an implementation of Hyperledger).

There could be a blockchain per subject area (like customer or product) in the implementation.

Hyperledger

MDM will begin by utilizing these internal blockchain networks, also known as Distributed Ledger Technology, though utilization of public blockchains is inevitable.

A shared master data ledger beyond company boundaries can, for example, contain common and agreed master data including public company information and common contract clauses with only counterparties able to see the content and destination of the communication.

Hyperledger is quickly becoming the standard for the open-source blockchain.

Hyperledger is hosted by The Linux Foundation. IBM, with the Hyperledger Fabric, is establishing the framework for blockchain in the enterprise.

Supporting master data management with a programmable interface for confidential transactions over a permissioned network is becoming a key inflection point for blockchain and Hyperledger.

Data management is about the right data at the right time and master data is fundamental to great data management, which is why centralized approaches like the discipline of master data management have taken center stage.

Characteristics of Blockchain

MDM can utilize the blockchain for distribution and governance and blockchain can clearly utilize the great master data produced by MDM.

Blockchain data needs data governance like any data. This data actually needs it more given its importance on the network.

MDM and blockchain are going to be intertwined now.

• It enables the key components of establishing and distributing the single version of the truth of data. Blockchain enables trusted governed data.
• It integrates this data across broad networks.
• It prevents duplication and provides data lineage.
• It will start in MDM in niches that demand these traits such as financial, insurance, and government data.

You can get to know the customer better with native fuzzy search and matching in the blockchain.

You can track provenance, ownership, relationship, and lineage of assets, do trade/channel finance, and post-trade reconciliation/settlement.

Blockchain is now a disruption vector for MDM.

MDM vendors need to be at least blockchain-aware today, creating the ability for blockchain integration in the near future, such as what IBM InfoSphere Master Data Management is doing this year.

Others will lose ground.

Source: gigaom.com

Master Data Management Joins the Machine Learning Party

In a normal master data management (MDM) project, a current state business process flow is built, followed by a future state business process flow that incorporates master data management.

The current state is usually ugly as it has been built piecemeal over time and represents something so onerous that the company is finally willing to do something about it and inject master data management into the process.

Many obvious improvements to process come out of this exercise and the future state is usually quite streamlined, which is one of the benefits of MDM.

I present today that these future state processes are seldom as optimized as they could be.

Consider the following snippet, supposedly part of an optimized future state.

This leaves in the process four people to manually look at the product, do their (unspecified) thing and (hopefully) pass it along, but possibly send it backwards to an upstream participant based on nothing evident in particular.

The challenge for MDM is to optimize the flow. I suggest that many of the “approval jails” in business process workflow are ripe for reengineering.

What criteria are used? It’s probably based on data that will now be in MDM.

If training data for machine learning (ML) is available, not only can we recreate past decisions to automate future decisions, we can look at the results of those decisions and take past outcomes and actually create decisions in the process that should have been made and actually do them, speeding up the flow and improving the quality by an order of magnitude.

This concept of thinking ahead and automating decisions extends to other kinds of steps in a business flow that involve data entry, including survivorship determination.

As with acceptance & rejection, data entry is also highly predictable, whether it is a selection from a drop-down or free-form entry. Again, with training data and backtesting, probable contributions at that step can be manifested and either automatically entered or provided as default for approval.

The latter approach can be used while growing a comfort level.

Manual, human-scale processes, are ripe for the picking and it’s really a dereliction of duty to “do” MDM without significantly streamlining processes, much of which is done by eliminating the manual.

As data volumes mount, it is often the only way to not watch process time increase over time. At the least, prioritizing stewardship activities or routing activities to specific stewards based on an ML interpretation of past results (quality, quantity) is required.

This approach is paramount to having timely, data-infused processes.

As a modular and scalable trusted analytics foundational element, the IBM Unified Governance & Integration platform incorporates advanced machine learning capabilities into MDM processes, simplifying the user experience and adding cognitive capabilities.

Machine learning can also discover master data by looking at actual usage patterns. ML can source, suggest or utilize external data that would aid in the goal of business processes.

Another important part of MDM is data quality (DQ). ML’s ability to recommend and/or apply DQ to data, in or out of MDM, is coming on strong.

Name-identity reconciliation is a specific example but generally, ML can look downstream of processes to see the chaos created by data lacking full DQ and start applying the rules to the data upstream.

IBM InfoSphere Master Data Management utilizes machine learning to speed the data discovery, mapping, quality and import processes.

In the last post (link), I postulated that blockchain would impact MDM tremendously. In this post, it’s machine learning affecting MDM. (Don’t get me started on graph technology).

Welcome to the new center of the data universe.

MDM is about to undergo a revolution.

Products will look much different in 5 years.

Make sure your vendor is committed to the MDM journey with machine learning.

Source: gigaom.com

When World’s Collide: Blockchain and Master Data Management

Master Data Management (MDM) is an approach to the management of golden records that have been around over a decade only to find a growth spurt lately as some organizations are exceeding pain thresholds in the management of common data.

Blockchain has a slightly shorter history, coming aboard with bitcoin, but also is seeing its revolution these days as data gets distributed far and wide and trust has taken center stage in business relationships.

Volumes could be written about each on its own, and given that most organizations still have a way to go with each discipline, that might be appropriate. However, good ideas wait for no one and today’s idea is MDM on Blockchain.

 

MDM on Blockchain.

Thinking back over our MDM implementations over the years, it is easy to see the data distribution network becoming wider. As a matter of fact, master data distribution is usually the most time-intensive and unwieldy part of an MDM implementation anymore.

The blockchain removes overhead, costs, and unreliability from authenticated peer-to-peer network partner transactions involving data exchange. It can support one of the big challenges of MDM with governed, bi-directional synchronization of master data between the blockchain and enterprise MDM.

 

Data Governance

Another core MDM challenge is arriving at the “single version of the truth”. It’s elusive even with MDM because everyone must tacitly agree to the process used to instantiate the data in the first place.

While many MDM practitioners go to great lengths to utilize the data rules from a data governance process, it is still a process subject to criticism.

The consensus that blockchain can achieve is a governance proxy for that elusive “single version of the truth” by achieving group consensus for trust as well as full lineage of data.

 

Blockchain Tackles Challenges in MDM.

Blockchain enables the major components and tackles the major challenges in MDM.

Blockchain provides a distributed database, as opposed to a centralized hub, that can store data that is certified, and for perpetuity. By storing timestamped and linked blocks, the blockchain is unalterable and permanent.

Though not for low latency transactions yet, transactions involving master data, such as financial settlements, are ideal for blockchain and can be sped up by an order of magnitude since blockchain removes the grist in a normal process.

Blockchain uses pre-defined rules that act as gatekeepers of data quality and governs the way in which data is utilized. Blockchains can be deployed publicly (like bitcoin) or internally (like an implementation of Hyperledger).

There could be a blockchain per subject area (like customer or product) in the implementation. MDM will begin by utilizing these internal blockchain networks, also known as Distributed Ledger Technology, though utilization of public blockchains is inevitable.

A shared master data ledger beyond company boundaries can, for example, contain common and agreed master data including public company information and common contract clauses with only counterparties able to see the content and destination of the communication.

Hyperledger is quickly becoming the standard for the open-source blockchain. Hyperledger is hosted by The Linux Foundation. IBM, with the Hyperledger Fabric, is establishing the framework for blockchain in the enterprise.

Supporting master data management with a programmable interface for confidential transactions over a permissioned network is becoming a key inflection point for blockchain and Hyperledger.

 

Data Management

Data management is about the right data at the right time and master data is fundamental to great data management, which is why centralized approaches like the discipline of master data management have taken center stage.

MDM can utilize the blockchain for distribution and governance and blockchain can clearly utilize the great master data produced by MDM. Blockchain data needs data governance like any data. This data actually needs it more given its importance on the network.

MDM and blockchain are going to be intertwined now. It enables the key components of establishing and distributing the single version of the truth of data. Blockchain enables trusted governed data. It integrates this data across broad networks. It prevents duplication and provides data lineage.

It will start in MDM in niches that demand these traits such as financial, insurance and government data. You can get to know the customer better with native fuzzy search and matching in the blockchain. You can track provenance, ownership, relationship and lineage of assets, do trade/channel finance and post-trade reconciliation/settlement.

Blockchain is now a disruption vector for MDM. MDM vendors need to be at least blockchain-aware today, creating the ability for blockchain integration in the near future, such as what IBM InfoSphere Master Data Management is doing this year. Others will lose ground.

Source: gigaom.com