Video on demand has become a popular way to watch television series, films and other entertainment and Netflix is probably the most known brand for delivering that.
The great thing about watching video on demand is that you do not have to enjoy the service at the exact same time as everyone else, as it was the case back in the days when watching TV or going to the movies were the options available.
At Product Data Lake we will bring that convenience to business ecosystems, as the situation today with broadcasting product information in supply chains very much resembles the situation we had before video on demand came around in the TV/Movie world.
As a provider of product information (being a manufacturer or upstream distributor), you will push your product information into Product Data lake, when you have the information available. Moreover, you will only do that once for each product and piece of information. No more coming to each theatre near your audience and extensive reruns of old stuff.
As a receiver of product information (being a downstream distributor, reseller or large end user), you will pull product information when you need it. That will be when you take a new product into your range or do a special product sale as well as when you start to deal with a new piece of information. No more having to be home at a certain time when your supplier does the show or waiting in ages for a rerun when you missed it.
Learn more about how Product Data Lake makes your life in Product Information Management (PIM) easier by following us here on LinkedIn.
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:
Interested? Get in touch:
“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.
While 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.
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”.
A 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 🙂
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.
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.
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.
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.
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.
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.
Doing 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.
An 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
Multi-Domain MDM, Santa Style, is the title of a post on this blog made a couple of years ago. This post was about how a multi-domain Master Data Management solution could look like in an organization doing business as we think Santa Claus do.
Many organizations around the world who has recently embraced Master Data Management (MDM) has added Data Governance as an imperative parallel or integrated initiative. I guess Santa could have followed that path too.
Below are some thoughts about data governance considerations at the Santa Claus place based on a concept from The Data Governance Institute mentioned in the post Data Governance: Day 2:
What does it mean to be naughty or nice? I guess that must be a key question faced by Santa and his team every day. Santa is probably in no better position here than your are in many real world organizations. It is challenging to document key principles that everyone refer to every day as it turns really hard when you try to put them in a common shared business glossary.
Is Santa able to implement data governance based on the roles that the elves and the reindeers have had in daily operations around Christmas or does he have to ask Rudolph to head up a Data Governance Office or even become Chief Data Officer?
Reactive Issue Resolution
What should Santa do when the logistic elves insist on chimney positions in UTM and the reindeers can only use WGS84 coordinates? If Santa’s data governance programme does not solve this one you better watch out when Santa Claus is coming to Town.
I guess this is the time for blog posts about big things that is going to happen in 2015. But you see, we could also take a route away from the motorways and highways and see how the traditional way of life is still unfolding the data quality landscape.
While the innovators and early adopters are fighting with big data quality the late majority are still trying get the heads around how to manage small data. And that is a good thing, because you cannot utilize big data without solving small data quality problems not at least around master data as told in the post How important is big data quality?
Solving data quality problems is not just about fixing data. It is very much also about fixing the structures around data as explained in a post, featuring the pope, called When Bad Data Quality isn’t Bad Data.
A common roadblock on the way to solving data quality issues is that things that what are everybody’s problem tends to be no ones problem. Implementing a data governance programme is evolving as the answer to that conundrum. As many things in life data governance is about to think big and start small as told in the post Business Glossary to Full-Blown Metadata Management or Vice Versa.
Data governance revolves a lot around peoples roles and there are also some specific roles within data governance. Data owners have been known for a long time, data stewards have been around some time and now we also see Chief Data Officers emerge as examined in the post The Good, the Bad, and the Ugly Data Governance Role.
As experienced recently, somewhere in the countryside, while discussing how to get going with a big and shiny data governance programme there is however indeed still a lot to do with trivial data quality issues as fields being too short to capture the real world as reported in the post Everyday Year 2000 Problems.