Sell more. Reduce costs.

Business outcome is the end goal of any data management activity may that be data governance, data quality management, Master Data Management (MDM) and Product Information Management (PIM).

Business outcome comes from selling more and reducing costs.

At Product Data Lake we have a simple scheme for achieving business outcome through selling more goods and reducing costs of sharing product information between trading partners in business ecosystems:

Sell more Reduce costs

Interested? Get in touch:

I am afraid that Gartner does not help

“The average financial impact of poor data quality on organizations is $9.7 million per year.” This is a quote from Gartner, the analyst firm, used by them to promote their services in building a business case for data quality.

AverageWhile this quote rightfully emphasizes on that a lot of money is at stake, the quote itself holds a full load of data and information quality issues.

On the pedantic side, the use of the $ sign in international communication is problematic. The $ sign represents a lot of different currencies as CAD, AUD, HKD and of course also USD.

Then it is unclear on what basis this average is measured. Is it among the +200 million organizations in the Dun & Bradstreet Worldbase? Is it among organizations on a certain fortune list? In what year?

Even if you knew that this is an average in a given year for the likes of your organization, such an average would not help you justify allocation of resources for a data quality improvement quest in your organization.

I know the methodology provided by Gartner actually is designed to help you with specific return on investment for your organization. I also know from being involved in several business cases for data quality (as well as Master Data Management and data governance) that accurately stating how any one element of your data may affect your business is fiendishly difficult.

I am afraid that there is no magic around as told in the post Miracle Food for Thought.

Cultured Freshwater Pearls of Wisdom

One of my current engagements is within jewelry – or is it jewellery? The use of these two respectively US English and British English words is a constant data quality issue, when we try to standardize – or is it standardise? – to a common set of reference data and a business glossary within an international organization – or is it organisation?

Looking for international standards often does not solve the case. For example, a shop that sells this kind of bijouterie, may be classified with a SIC code being “Jewelry store” or a NACE code being “Retail sale of watches and jewellery in specialised stores”.

shiny thingsA pearl is a popular gemstone. Natural pearls, meaning they have occurred spontaneously in the wild, are very rare. Instead, most are farmed in fresh water and therefore by regulation used in many countries must be referred to as cultured freshwater pearls.

My pearls of wisdom respectively cultured freshwater pearls of wisdom for building a business glossary and finding the common accepted wording for reference data to be used within your company will be:

  • Start looking at international standards and pick what makes sense for your organization. If you can live with only that, you are lucky.
  • If not, grow the rest of the content for your business glossary and reference data by imitating the international or national standards for your industry, and use your own better wording and additions that makes the most sense across your company.

And oh, I know that pearls of wisdom are often used to imply the opposite of wisdom 🙂

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Takeaways from MDM Summit Europe 2016

Yesterday I popped in at the combined Master Data Management Summit Europe 2016 and Data Governance Conference Europe 2016.

This event takes place Monday to Thursday, but unfortunately I only had time and money for the Tuesday this year. Therefore, my report will only be takeaways from Tuesday’s events. On a side note the difficulties in doing something pan-European must have troubled the organisers of this London event as avoiding the UK May bank holidays has ended in starting on a Monday where most of the rest of Europe had a day off due to being Pentecost Monday.

MDM

Tuesday morning’s highlight for me was Henry Peyret of Forrester shocking the audience in his Data Governance keynote by busting the myth about the good old excuse for doing nothing, being the imperative of top-level management support, is not true.

Back in 2013 I wondered if graph databases will become common in MDM. Certainly graph databases has become the talk of the town and it was good to learn from Andreas Weber how the Germany based figurine manufacturer Schleich has made a home grown PIM / Product MDM solution based on graph database technology.

Ivo-Paul Tummers of Jibes presented the MDM (and beyond) roadmap for the Dutch food company Sligro. I liked the alley of embracing multi-channel, then omnichannel with self-service at the end of the road and how connect will overtake collect during this journey. This is exactly the reason of being for the Product Data Lake venture I am working on right now.

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CDI, PIM, MDM and Beyond

The TLAs (Three Letter Acronyms) in the title of this blog post stands for:

  • Customer Data Integration
  • Product Information Management
  • Master Data Management

CDI and PIM are commonly seen as predecessors to MDM. For example, the MDM Institute was originally called the The Customer Data Integration Institute and still have this website: http://www.tcdii.com/.

Today Multi-Domain MDM is about managing customer, or rather party, master data together with product master data and other master data domains as visualized in the post A Master Data Mind Map. Some of the most frequent other master domains are location master data and asset master data, where the latter one was explored in the post Where is the Asset? A less frequent master data domain is The Calendar MDM Domain.

QuadrantYou may argue that PIM (Product Information Management) is not the same as Product MDM. This question was examined in the post PIM, Product MDM and Multi-Domain MDM. In my eyes the benefits of keeping PIM as part of Multi-Domain MDM are bigger than the benefits of separating PIM and MDM. It is about expanding MDM across the sell-side and the buy-side of the business eventually by enabling wide use of customer self-service and supplier self-service.

The external self-service theme will in my eyes be at the centre of where MDM is going in the future. In going down that path there will be consequences for how we see data governance as discussed in the post Data Governance in the Self-Service Age. Another aspect of how MDM is going to be seen from the outside and in is the increased use of third party reference data and the link between big data and MDM as touched in the post Adding 180 Degrees to MDM.

Besides Multi-Domain MDM and the links between MDM and big data a much mentioned future trend in MDM is doing MDM in the cloud. The latter is in my eyes a natural consequence of the external self-service themes and increased use of third party reference data which all together with the general benefits of the SaaS (Software as a Service) and DaaS (Data as a Service) concepts will make MDM morph into something like MDaaS (Master Data as a Service) – an at least nearly ten year old idea by the way, as seen in this BeyeNetwork article by Dan E Linstedt.

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Business Agility, Continuous Improvement and MDM

Being able to react to market changes in an agile way is the path to the survival of your business today. As you may not nail it in the first go, the ability to correct with continuous improvement is the path for your business to stay alive.

open-doorDoing business process improvement most often involves master data as examined in the post Master Data and Business Processes. The people side of this is challenging. The technology side isn’t a walkover either.

When looking at Master Data Management (MDM) platforms in sales presentations it seems very easy to configure a new way of orchestrating a business process. You just drag and drop some states and transitions in a visual workflow manager. In reality, even when solely looking at the technical side, it is much more painful.

MDM solutions can be hard to maneuver. You have to consider existing data and the data models where the data sits. Master data is typically used with various interfaces across many business functions and business units. There are usually many system integrations running around the MDM component in an IT landscape.

A successful MDM implementation does not just cure some pain points in business processes. The solution must also be able to be maneuvered to support business agility and continuous improvement. Some of the data quality and data governance aspects of this is explored in the post Be Prepared.

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To-Be Business Rules and MDM

checklistAn important part of implementing Master Data Management (MDM) is to capture the business rules that exists within the implementing organization and build those rules into the solution. In addition, and maybe even more important, is the quest of crafting new business rules that helps making master data being of more value to the implementing organization.

Examples of such new business rules that may come along with MDM implementations are:

  • In order to open a business account you must supply a valid Legal Entity Identifier (like Company Registration Number, VAT number or whatever applies to the business and geography in question)
  • A delivery address must be verified against an address directory (valid for the geography in question)
  • In order to bring a product into business there is a minimum requirement for completeness of product information.

Creating new business rules to be part of the to-be master data regime highlights the interdependency of people, process and technology. New technology can often be the driver for taking on board such new business rules. Building on the above examples such possibilities may be:

  • The ability to support real time pick and check of external identifiers
  • The ability to support real time auto completion and check of postal addresses
  • The ability to support complex completeness checks of a range of data elements

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