The Start of the History of Data and Information Quality Management

I am sad to hear that Larry English has passed away as I learned from this LinkedIn update by C. Lwanga Yonke.

As said in here: “When the story of Information Quality Management is written, the first sentence of the first paragraph will include the name Larry English”.

Larry pioneered the data quality – or information quality as he preferred to coin it – discipline.

He was an inspiration to many data and information quality practitioners back in the 90’s and 00’s, including me, and he paved the way for bringing this topic to the level of awareness that it has today.

In his teaching Larry emphasized on the simple but powerful concepts which are the foundation of data quality and information quality methodologies:

  • Quantify the costs and lost opportunities of bad information quality
  • Always look for the root cause of bad information quality
  • Observe the plan-do-check-act circle when solving the information quality issues

Let us roll up our sleeves and continue what Larry started.

There is No Single Customer 360 View

The terms Single Customer View (SCV) and 360 View of Customer have been touted about within Master Data Management (MDM) since it all started with the first Customer Data Integration (CDI) solutions.

The theory is that a customer MDM solution can provide golden records that uniquely identifies any person or organization who is a customer to your organization and consistently build a complete description of those persons and organizations which then will be the single source of truth.

In practice this is very hard as compiling a concept for a view that suite all scenarios in all business units often is too daunting, and the challenges then will kill the customer MDM implementation before completion. This is sad, because it is also hard to succeed in digital transformation and launching new digital services with scattered unconnected customer views across the application landscape within your organization.

Therefore, building context-aware customer views is a useful concept for ensuring the success of customer MDM implementations and succeeding in digitalization.     

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

Why are Analyst Rankings Behind the MDM Market Dynamics?

From time to time, analyst firms publish market reports that include their opinion and ranking of the vendors/solution providers in a specific market, such as Master Data Management (MDM).

Reading such reports, it strikes me that the rankings often do not seem to be in line with what is going in the market, especially when you consider market positioning, demand and technological developments.

One example was touched on in my post “The Latest Constellation Research MDM Shortlist” where the analyst firm in question seemed to take a long time to understand that Oracle had left the MDM market.

Gartner’s Magic Quadrant reports are generally the most popular; their rankings often appear in corporate PowerPoint decks when businesses want to evaluate and select the right MDM solution that fits their needs.  And yet, I would argue that Gartner is more conservative in its approach.  For example, it took Gartner a long time to abandon the notion that there was a separate customer MDM and product MDM enterprise-level market, as I examined in my post “Who will become Future Leaders in the Gartner Multidomain MDM Magic Quadrant?

You’ll notice that the magic quadrant from 2017 had a very limited number of market players on it; it excluded several vendors who offer MDM via cloud subscription models who are now recognised as key players and who, in hindsight, should have been included on many shortlists back then.

So, when the next Gartner Magic Quadrant for MDM is published (currently scheduled for the end of November 2020, though I hear it may be pushed to January 2021), I would always recommend you take a look at who is not included as well as those who are, and ask yourself what information has led Gartner to rank the vendors the way they have.

In that sense, Gartner’s thoroughness can often work against them as a lot of the data used in the upcoming report will be from 2019.  Also, you should be aware that customer feedback is given by those who made the decision to implement a specific solution; I often hear a number of differing opinions from people across a business when they evaluate MDM solutions.

It’s also interesting to note how analyst firms differ between them.  Examples from the world of MDM include a dysfunctional relationship between Forrester and Informatica as well as between Gartner and IBM, and how Forrester, opposed to Gartner, has a much more favourable assessment of a new kind of MDM provider like Reltio.

Disclosure: I recently worked with Reltio on a webinar and a white paper.

Forrester’s Latest View on the MDM Market

Generic rankings of vendors on a market, like the Master Data Management (MDM) market, are in my eyes not very useful.

So, it is relieving that the recent Now Tech report format from Forrester does not do that.

The Now Tech: Master Data Management, Q4 2020 from Forrester was published last month. In here the vendors are grouped by size and some key selection criteria are stated for each vendor. This include:

  • Deployment mode which in Forrester lingo are functional segments covering public cloud, multi-cloud, on-premise and SaaS.
  • Geographic segments with percentage of revenue for North America, Latin America, EMEA (Europe/ Middle East/Africa) and APAC (Asia/Pacific).
  • Vertical market focus and sample customers.

You can access the report if you are a Forrester client or buy it from Forrester. Alternatively, you can have a free copy at the Prospecta site, as the MDO (Master Data Online) solution from Prospecta is included in the report. Link here.

How to Use Connected Master Data to Enable New Revenue Models

In today’s experience economy and in the age of digital transformation, there are two distinct ways you can use modern master data management to drive positive business outcomes:

  • Automating existing business processing so you can increase operational effectiveness, cut costs, reduce risk and drive incremental growth.
  • Developing new data-driven revenue streams that result in new substantial growth opportunities.

As customer centricity is vital to any digitisation project, so it must follow that improving the customer experience (CX) is a key objective of any digital transformation project.

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.

As the diagram above shows, a connected and extended master data landscape (aka data hub) will give you the essential capabilities you need in order to understand your customers. Knowing your customers better allows you to develop better products, drive new streams of revenue, and deliver the best customer 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.