If you are a merchant (retailer or a B2B dealer) of tangible goods a huge challenge in today’s data driven world is the get complete product information from your suppliers being the importers, brand owners and/or manufacturers of the products.
There are plenty of bad ways of trying to do that:
- Send them a spreadsheet to be filled in
- Build a supplier portal where they can do the work
- Get the data from a data pool
- Outsource the collection process to someone far away
Sean Sinclair sums this up nicely in the LinkedIn article called Feeding the Monster – Product Data Onboarding for ‘Hundreds and Thousands’…
Coincidentally Sean and I at the same time worked with these challenges at two different major competing UK distributors/dealers of building materials up in the West Midlands.
The only solution will be to create a win-win situation for both manufacturers and merchants as explained in the post Merchants vs Manufacturers in the Information Age.
Every time there is a survey about what causes poor data quality the most ticked answer is human error. This is also the case in the Profisee 2019 State of Data Management Report where 58% of the respondents said that human error is among the most prevalent causes of poor data quality within their organization.
This topic was also examined some years ago in the post called The Internet of Things and the Fat-Finger Syndrome.
Even the Romans knew this as Seneca the Younger said that “errare humanum est” which translates to “to err is human”. He also added “but to persist in error is diabolical”.
So, how can we not persist in having human errors in data then? Here are three main approaches:
- Better humans: There is a whip called Data Governance. In a data governance regime you define data policies and data standards. You build an organizational structure with a data governance council (or any better name), have data stewards and data custodians (or any better title). You set up a business glossary. And then you carry on with a data governance framework.
- Machines: Robotic Processing Automation (RPA) has, besides operational efficiency, the advantage of that machines, unlike humans, do not make mistakes when they are tired and bored.
- Data Sharing: Human errors typically occur when typing in data. However, most data are already typed in somewhere. Instead of retyping data, and thereby potentially introduce your misspelling or other mistake, you can connect to data that is already digitalized and validated. This is especially doable for master data as examined in the article about Master Data Share.
The Master Data Management (MDM) discipline is something that belongs in the backbone of digitalization and enterprise architecture and therefore new ways of doing things always have a hard time in this realm. Fore sure there have been talk about big data and MDM for years, but actual implementations are few compared to ongoing traditional system of record implementations. The same will be the case with Artificial Intelligence (AI) and MDM. We will still see a lot of clerking around MDM for years.
So, I am stretching it far when working with yet a new must do thing for MDM (besides working with MDM, big data and AI).
But I have no doubt about that shareconomy (or sharing economy) will affect the way we work with MDM in the future. A few others are on the same path as for example the Swiss consultancy CDQ as presented on their page about Shareconomy for Customer and Supplier Data and The Corporate Data League (CDL).
Doing Master Data Management (MDM) enterprise wide is hard enough. The ability to control master data across your organization is essential to enable digitalization initiatives and ensure the competitiveness of your organization in the future.
But it does not stop there. Increasingly every organization will be an integrated part of a business ecosystem where collaboration with business partners and through market places will be a part of digitalization and thus, we will have a need for working on the same foundation around master data.
This new aspect of MDM is also called multienterprise MDM. It will take years to be widespread. But you better start thinking about how this will be a part of your MDM strategy. Because in the long run you must Share or be left out of business.