How to Use Connected Master Data to Enable New Revenue Models

In the era of digital transformation there are two frontiers in utilizing modern master data management to achieve business outcomes:

  • Automating existing business processes to achieve better operational effectiveness, cutting costs, reducing risk, and pursuing incremental growth opportunities.
  • Developing new data-driven revenue models that result in substantial growth opportunities.

Customer centricity is at the heart of digitalization and improved customer experience (CX) must be the key driver in a digital transformation roadmap.

Getting a comprehensive 360-degree view of customers in digital business processes involves the ability to connect customer master data with other master data entities, hierarchies, transactions, big data, and reference data.

A connected and extended master data landscape (aka data hub) underpins the essential capabilities needed in understanding customers and utilizing this knowledge in creating new revenue models and herein providing both the best customer experience and relevant product experience through hyper-personalization.    

Learn more in the white paper co-authored by Reltio and yours truly: Taking Customer 360 to The Next Level: Fueling New Digital Business

Organizations can deliver more value when they collaborate in sharing data externally

The saying in the title of this blog post is taken from a recent Gartner article with the tile Data Sharing Is a Business Necessity to Accelerate Digital Business.

In this article authored by Laurence Goasduff a key takeaway is that:

‘By 2023, organizations that promote data sharing will outperform their peers on most business value metrics”.

Regular readers of this blog will know that many good data things come from data sharing as for example pondered in the 11 years old post called Sharing data is key to a single version of the truth.

A consequence of the business benefits in sharing data will be a rise in data management disciplines aiming at business ecosystem wide data sharing, where product data syndication is an obvious opportunity.

During the last years I have been working on such a solution. This one is called Product Data Lake.

MDM / PIM / DQM Case Studies

The Disruptive List of MDM / PIM / DQM Solutions is growing both in terms of the solutions presented and the content provided on the list.

The latest piece of content is the Case Study List.

This is a list of case studies from innovative solution providers. The aim is to give inspiration for organizations having the quest to implement or upgrade their Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) capability.

The list is divided into industries, so you can have an overview of case studies from organizations comparable to your organization.

Check out the list here.

MDM Terms in Use in the Gartner Hype Cycle

The latest Gartner Hype Cycle for Data and Analytics Governance and Master Data Management includes some of the MDM trends that have been touched here on the blog.

If we look at the post peak side, there are these five main variant – or family of variant – terms in motion:

  • Single domain MDM represented by the two most common domains being MDM of Product Data and MDM of Customer Data.
  • Multidomain MDM.
  • Cloud MDM.
  • Data Hub Strategy which I like to coin Extended MDM.
  • Interenterprise MDM, which before was coined Multienterprise MDM by Gartner and I like to coin Ecosystem Wide MDM.

It is also worth noticing that Gartner has dropped the term Multivector MDM from the hype cycle. This term never penetrated the market lingo.

Another term that is almost only used by Gartner is Application Data Management (ADM). That term is still in there.

Why are so many businesses drowning in data?

Today’s guest blogger is Sam Phipps, who is a supply chain blogger & marketing manager at Slimstock. As a supply chain blogger with a passion for inventory, Sam helps businesses to optimise their processes to boost availability, save cost and maximise customer satisfaction. In this article, Sam explores why ‘good’ master data is critical to supply chain success.

As the inventory expert, Tony Wild, once highlighted: “inventory is the physical consequence of missing data.”

But almost all businesses manage some form of master data. In fact, many organisations have an abundance of it. So, what’s the problem then?

Just because lots of data exists within a business, this does not mean that it is correct or complete. Furthermore, just because the data is in place, this doesn’t mean that master data is used effectively.

Every department within a business depends on good quality master data. However, in certain areas of business such as operations and supply chain management, poor data can quickly result in bad decisions that impact the entire organization.

Yet, around 50% of all businesses lack the core master data which are a pre-requisite for ‘good’ supply chain management. And even for the remaining 50%, master data is often seen as an area that could be improved upon.

Fundamental to supply chain success

In essence, supply chain master data includes all of the product and transactional information related to a given item. From determining logistics routes to setting up promotions, this information is used to make thousands of decisions.

But in the context of supply chain management, correct and reliable master data is an absolute must for satisfying customer demand. After all, the foundation of inventory and supply chain success revolves around two key questions:

  • When should you place an order?
  • How big should your order be?

To determine both of these points, we depend on several bits of key information. And, without this data, it would be impossible to know how much inventory you need to fulfil your customer’s demand.

To give a few examples, the supply chain master data typically encompasses the following areas:

  • Specific details about the product in question (size, SKU number)
  • Information about the supplier (lead times, MOQs)
  • Details about the current inventory position (location, inventory level)
  • Details about the customer
  • Information around the past demand
  • As well as many other key data elements

Driving long-term efficiency improvements

So far, we have only touched upon the basics: aligning supply with demand. However, this is just the start.

Through some fairly simple analysis techniques, master data can be used to explore new opportunities for optimisation.

For example, the first area we can review is the ABC analysis. By focusing on how each item contributes to the overall business goals (whether than be profitability, sales turnover or something else), management can use this to determine which (and how many) products the company should prioritise.

We could also carry out a so-called Incremental Margin Analysis, which provides management with insight into which products contribute positively to the net margin.

Furthermore, we could explore the Delivery Time Deviation Distribution. This is an instrument that the supply chain team can use to gain insight into the performance of suppliers.

Each of these analyses requires slightly different master data elements. The table below provides an overview of what data is required.

SCM MDM

Master data is everyone’s problem

No business can afford to overlook master data. But who should be the owner of the master data in your business?  Should it be the IT, finance, operations, or even the management team?

This is a difficult question to ask. And many businesses don’t have a clear-cut answer. Although there is a technological process or system that the IT team need to support master data should be seen as a priority by everyone!

To read more about how you can optimise you supply chain master data, click here: https://www.slimstock.com/en/master-data/

30% Network Economy and MDM

McKinsey Digital Network Economy and Digital Ecosystem

McKinsey Digital recently published an article with the title How do companies create value from digital ecosystems?

In here it is said that: “The integrated network economy could represent a global revenue pool of $60 trillion in 2025 with a potential increase in total economy share from about 1 to 2 percent today to approximately 30 percent by 2025”.

This dramatic shift will in my eyes mean a change of direction in the way we see Master Data Management (MDM) as well as Product Information Management (PIM) and Data Quality Management (DQM) solutions.

360 is a magic number in the master data and data quality world. It is about having a 360-degree view of customers, suppliers, and products. This is an inside-out view. The enterprise is looking at a world revolving around the enterprise just as back then when we thought the universe revolved around the planet Earth.

By 2025 forward looking enterprises must have changed that view and directed master data, product information and data quality management into a state fit for the network economy by having a business ecosystem wide MDM (PIM and DQM) solution landscape.

Gartner, the analyst firm, coins this Multienterprise MDM.

Analyst MDM / PIM / DQM Solution Reports Update August 2020

Analyst firms occasionally publish market reports with a generic solution overview for Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM).

Here is an overview of the latest major reports:

Analyst MDM PIM DQM reports

3 ways to learn more: 

  • You can check out many of the included solutions on The Disruptive MDM / PIM / DQM List.
  • You can get a free ranking that also include the rising stars on the solution market and is based on your context, scope and requirements here.
  • You can book a free short online meeting with me for further discussion on your business case as part of my engagement at the consultancy firm Astrocytia here.

Who is in the MDM Landscape Q2 2020?

The new Information Difference MDM Landscape is out.

MDM Landscape Q2 2020

Talend is not in the landscape this year, which is natural as Talend do not promote MDM anymore. Viamedici and Veeva is not in there as they were last year. This may, as discussed under the 2019 MDM Landscape with other vendors, be because they have declined to participate.

Recurring vendors are positioned quite like last year. As the vertical axis is technology, including customer satisfaction, and the horizontal axis is market strength, there still seems to be two main groups of vendors. Best-of-breed MDM with higher customer satisfaction and mega-vendors with not so high customer satisfaction.

Stibo Systems sits between these two groups according to this report. In my current work at the consultancy firm Astrocytia we have some engagements where we assist our clients in getting much more business benefit from MDM and thereby with these cases we strive to push Stibo Systems towards the top-right corner.

Gaining customer satisfaction, not at least with larger market strength, is dependent on both the capabilities of the MDM vendor and the approach from the consultancy firm that assist with the wider MDM strategy and implement the solutions.

From Where Will the Data Quality Machine-Learning Disruption Come?

The 2020 Gartner Magic Quadrant for Data Quality Solutions is out.

In here Gartner assumes that: “By 2022, 60% of organizations will leverage machine-learning-enabled data quality technology for suggestions to reduce manual tasks for data quality improvement”.

The data quality tool vendor rankings according to Gartner looks pretty much as last year. Precisely is the brand that last year was in there as Syncsort and Pitney Bowes.

Gartner DQ MQ 2020

Bigger picture here.

You can get a free reprint of the report from Talend or Informatica.

The question is if we are going to see the machine-learning based solutions coming from the crowd of vendors in a bit stalled quadrant or the disruption will come from new solution providers. You can find some of the upcoming machine-learning / Artificial Intelligence (AI) based vendors on The Disruptive MDM / PIM DQM List.

How to Create Great CX Using the Full Potential of MDM

Improved customer experience (CX) is a key driver for digitalization and having optimal Master Data Management (MDM) is a core prerequisite for being successful in providing customer experience.

First, MDM underpins your insights into:

  • Customer identity
  • Customer hierarchies
  • Customer locations
  • Customer transactions
  • Customer footprint on websites
  • Customer footprint in social media
  • Customer preferences
  • Customer privacy and data protection settings and rights

Next, MDM gives you the insight into how to provide a tailored product experience by managing the data supply chain from your suppliers/vendors to each of the customer touch points by:

  • Having your suppliers/vendors syndicating all the product-, service- and other information that is required by your customers
  • Transforming these data into the data structure that fits your customers
  • Consolidating all sources relevant for your customers
  • Enriching with your internal competitive information that delight and engage your customers
  • Customizing to each channel where you have a touch point with your customers
  • Personalizing utilizing rich and structured customer insight

PXM and CX

Learn more at a webinar hosted by Reltio on the 11th August 2020. Free registration here.