MDM Tools Revealed

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Every organization needs Master Data Management (MDM). But does every organization need a MDM tool?

In many ways the MDM tools we see on the market resembles common database tools. But there are some things the MDM tools do better than a common database management tool. The post called The Database versus the Hub outlines three such features being:

  • Controlling hierarchical completeness
  • Achieving a Single Business Partner View
  • Exploiting Real World Awareness

Controlling hierarchical completeness and achieving a single business partner view is closely related to the two things data quality tools do better than common database systems as explained in the post Data Quality Tools Revealed. These two features are:

  • Data profiling and
  • Data matching

Specialized data profiling tools are very good at providing out-of-the-box functionality for statistical summaries and frequency distributions for the unique values and formats found within the fields of your data sources in order to measure data quality and find critical areas that may harm your business. These capabilities are often better and easier to use than what you find inside a MDM tool. However, in order to measure the improvement in a business context and fix the problems not just in a one-off you need a solid MDM environment.

When it comes to data matching we also still see specialized solutions that are more effective and easier to use than what is typically delivered inside MDM solutions. Besides that, we also see business scenarios where it is better to do the data matching outside the MDM platform as examined in the post The Place for Data Matching in and around MDM.

Looking at the single MDM domains we also see alternatives. Customer Relation Management (CRM) systems are popular as a choice for managing customer master data.  But as explained in the post CRM systems and Customer MDM: CRM systems are said to deliver a Single Customer View but usually they don’t. The way CRM systems are built, used and integrated is a certain track to create duplicates. Some remedies for that are touched in the post The Good, Better and Best Way of Avoiding Duplicates.

integriertWith product master data we also have Product Information Management (PIM) solutions. From what I have seen PIM solutions has one key capability that is essentially different from a common database solution and how many MDM solutions, that are built with party master data in mind, has. That is a flexible and super user angled way of building hierarchies and assigning attributes to entities – in this case particularly products. If you offer customer self-service, like in eCommerce, with products that have varying attributes you need PIM functionality. If you want to do this smart, you need a collaboration environment for supplier self-service as well as pondered in the post Chinese Whispers and Data Quality.

All in all the necessary components and combinations for a suitable MDM toolbox are plentiful and can be obtained by one-stop-shopping or by putting some best-of-breed solutions together.

Happy Old New Year in Reference Data Management

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Today the 14th January in our times calendar used to be the first day in the new year when the Julian calendar was used before different countries at different times shifted to the Gregorian calendar.

Firework

Such shifts in what we generally refer to as reference data is a well-known pain in data management as exemplified in the post called The Country List. Within data warehouse management, we refer to this as Slowly Changing Dimensions.

Master Data Management (MDM) and Reference Data Management (RDM) are two closely related disciplines and often we may use the terms synonymously and indeed sometimes working with the same real world entity is MDM in one context but RDM in another context.

I have worked in industries, as public transit, where the calendar and related data must be treated as master data. But surely, in many other industries this will be an overkill. However, I have seen other entities treated as a simple List of Values (LoV) where it should be handled as master data or at least more complex reference data. Latest example is plants within a global company, where the highest ambition is proposed to be a mark for active or inactive, which hardly reflect the complexity in starting or buying a plant and closing or selling the same and the data management rules according to the changing states.

So happy 14th of January even if this is not New Year to you – but hey, at least it is my birthday.

The Evolution of MDM

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Master Data Management (MDM) is a bit more than 10 years old as told in the post from last year called Happy 10 Years Birthday MDM Solutions. MDM has developed from the two disciplines called Customer Data Integration (CDI) and Product Information Management (PIM). 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.

You 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.

MDM

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.

If you happen to be around Copenhagen in the late January I can offer you the full story at a late afternoon event taking place in the trendy meatpacking district and arranged by the local IT frontrunner company ChangeGroup. The event is called Master Data Management: Before, now and in the future.

My 2016 MDM Clairvoyance

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Bowl
Magic glass bowl

Now is the time of the year where you can try predicting what will happen in the next year within a certain field of interest to you.

When we talk about predictions within data management, we usually mean something based on analysing historical data with emphasis on seeing some recent trends.

My precognitions for the Master Data Management (MDM) market I have to admit is of the more traditional kind. Gut feelings. Qualified guessing if you like.

So, here are three foreseeings:

  • Gartner, the analyst firm, will finally stop publishing two magic quadrants for MDM (one for customer and product MDM) and, using some suitable data from their surveys, admit that there now is only one true multidomain market for larger MDM vendors. They might however introduce a new quadrant for what was more or less known as Product Information Management (PIM). But under a new term and with focus on eCommerce capabilities.
  • There will be more acquisitions in the market than seen since five years ago. At least one of the larger former product MDM specialists will buy a customer MDM specialist first and foremost in order to gain reference clients. Also MDM vendors will be looking for buying land in the new big data world.
  • The numbers and scopes of MDM projects will increase and therefore there will be a shortage of people with MDM experience. This trend will pave the way for more agile approaches to MDM including implementing less complex MDM solutions and services whereof most, in contradiction to the multidomain trend, will be domain (customer/party, product, location) niche players.

Did You Mean Potato or Potahto?

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As told in the post Where the Streets have Two Names one aspect of address validation is the fact, that in some parts of the world, a given postal address can be presented in more than one language.

I experienced that today when using Google Maps for directions to a Master Data Management (MDM) conference in Helsinki, Finland. When typing in the address I got this message:

Helsinki

The case is that the two addresses proposed by Google Maps are exactly the same address, just spelled in Swedish and Finnish, the two official languages used in this region.

I think Google Maps is an example of a splendid world-wide service. But even the best world-wide services sometimes don’t match local tailored services. This is in my experience the case when it comes to address management solutions as address validation and assistance whether they come as an integrated part of a Master Data Management (MDM) solution, a stand-alone data quality tool or a general service as Google Maps.

Big Data Quality, Santa Style

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Previous years close to Christmas posts on this blog has been about Multi-Domain MDM, Santa Style and Data Governance, Santa Style.

julemandenSo this year it may be the time to have a closer look at big data quality, Santa style, meaning how we can imagine Santa Claus is joining the raise of big data while observing that exploiting data, big or small, is only going to add real value if you believe in data quality. Ho ho ho.

At the Santa Claus organization they have figured out, that there is a close connection between excellence in working with big data and excellence in multi-domain Master Data Management (MDM) and data governance.

Here are some of the findings in the big data paper that the Chief Data Elf just signed off:

  • The feasibility of the new algorithms for naughty or nice marking using social media listening combined with our historical records is heavily dependent on unique, accurate and timely boys and girls master data. The party data governance elf gathering will be accountable for any nasty and noisy issues.
  • Implementation of the automated present buying service based on fuzzy matching between our supplier self-service based multi-lingual product catalogue and the wish list data lake must be done in a phased schedule. The product data governance elf committee are responsible for avoiding any false positives (wrong present incidents) and decreasing the number of false negatives (someone not getting what could be purchaed within the budget).
  • Last year we had and an 12.25 % overspend on reindeers due to incorrect and missing chimney positions. This year the reliance on crowdsourced positions will be better balanced with utilizing open government property data where possible. The location data governance elves will consult with the elves living on the roof at each head of state in order make them release more and better quality of any such data (the Gangnam Project).

Tear Down These Walls

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Over at The Data Roundtable there is some good thinking going on. Recently Dylan Jones blogged: Want to improve data quality? Start by re-imagining your data boundaries.

In his blog post Dylan explains how data journeys are costly and risky. There are huge opportunities, not at least for data quality, in simplifying the sharing of data by breaking down the data boundaries.

Berlinermauer
The Berlin Wall. Fortunately it is not there anymore.

Data boundaries exists within organisations and between organisations. As the way of doing business today involves businesses working together, we see more and more data being sent between businesses. Unfortunately often using spreadsheets as told in post Excellence vs Excel.

We definitely need better ways to share data within organisations and between organisations. Furthermore, as Dylan points out, the data exchange needs to go in both directions. The ability to share data in an intelligent way is based on that data is identified and described by commonly shared reference and master data.

In my experience, the ability to collaborate between businesses by sharing reference and master data, and utilize available public sources, will be crucial in the quest for re-imagining data boundaries. This is indeed the future of data quality and The Future of Master Data Management.

What does Twitter Know?

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We all know the pain of receiving e-mails with offers that is totally beside what you need.

Now Twitter has joined this spamming habit, which is a bit surprising, because with all the talk about big data and what it can do for prospect and customer insight, you should think that Twitter knows something about you.

Well, apparently not.

I operate two Twitter accounts. One named @hlsdk used for my general interaction with the data management community and one named @ProductDataLake used for a start-up service called Product Data Lake.

For both accounts, I am flooded with e-mails from Twitter about increasing my Holiday sales by using their ad services.

Twitter

Strange, because:

  • My businesses is not Business-to-Consumer (B2C) being about selling stuff to consumers, where the coming season is a high peak in the Western World. My business is Business-to-Business (B2B) where the coming season when it comes to sales is a stand still in the Western World.
  • In my part of the Western World we don’t use the term Holidays for the coming season. We (still) call it Christmas as told in the post Is the Holiday Season called Christmas Time or Yuletide?
  • In my home country, Denmark, you are not allowed to e-mail businesses with offers in e-mails unless you have actually asked for it. Not sure if Twitter is on the right side of the law here.

Excellence vs Excel

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We all use Excel though we know it is bad. It is a user friendly and powerful tool, but there are plenty of stories out there where Excel has caused so much trouble like this one from Computerworld in 2008 when the credit crunch struck.

I guess all people who works in data management curses Excel. Data kept in Excel is a pain  – you know where – as it is hard to share, you never know if you have the latest version, nice informative colouring disappears when transforming, narrow columns turns into rubbish, different formatting usually makes it practically impossible to combine two sheets and heaps of other not so nice behaviours.

Even so, Excel is still the most used tool for many crucial data management purposes as for example reported in the post The True Leader in Product MDM.

Excel is still a very frequent used option when it comes to exchanging data as touched by Monica McDonnell of Informatica in a recent blog post on Four Technology Approaches for IDMP Data Management.

Probably, the use of Excel as a mean to exchange data between organizations is the field where it will be most difficult to eliminate the dangerous use of Excel. The problem is that the alternative usually is far too rigid. The task of achieving consensus between many organizations on naming, formatting and all the other tedious stuff makes us turn to Excel.

Excellence vs Excel

When working with data quality within data management we may wrongly strive for perfection. We should rather strive for excellence, which is something better than the ordinary. In this case Excel is the ordinary. As Harriet Braiker said: “Striving for excellence motivates you; striving for perfection is demoralizing.”

In order to be excellent, though not perfect, in data sharing, we must develop solutions that are better than Excel without being too rigid. Right now, I am working on a solution for sharing product data being of that kind. The service is called the Product Data Lake.

The Future of Master Data Management

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Back in 2011 Gartner, the analyst firm, predicted that these three things would shape the Master Data Management (MDM) market:

  • Multi-Domain MDM
  • MDM in the Cloud
  • MDM and Social Networks

The third point was in 2012, after the raise of big data, rephrased to MDM and Big Data as reported in the post called The Big MDM Trend.

In my experience all these three themes are still valid with slowly but steadily uptake.

open-doorBut, have any new trends showed up in the past years?

In a 2015 post called “Master Data Management Merger Tardis and The Future of MDM” Ramon Chen of Reltio puts forward some new possibilities to be discussed, among those Machine Learning & Cognitive computing. I agree with Ramon on this theme, though these have been topics around in general for decades without really breaking through. But we need more of this in MDM for sure.

My own favourite MDM trend is a shift from focussing on internally captured master data to collaboration with external business partners as explained in the post MDM 3.0 Musings.

In that quest, I am looking forward to my next speaking session, which will be in Helsinki, Finland on the 8th December. There is an interview on that with yours truly available on the Talentum Master Data Management 2015 site.