Social Responsibility for Retailers and Distributors is No Longer an Option is the title of a new blog post by Paul Sirface on the Stibo Systems Datafusion blog.
Herein Paul writes:
“While many companies know that they have to respond to consumers’ demands, those with an active Master Data Management strategy have the best chance of responding effectively. Multi-domain Master Data Management (MDM) is the perfect place to begin organizing and collecting the data on product related information within the supply chain, including supplier compliance..”
Know Your Customer (KYC) is a well established term within data management and linked to fraud protection and anti money laundering.
Know Your Supplier (KYS) is indeed an equally important side of party master data management.
While customer master data management is on the way of evolving from handling mostly domestic customer data quality issues to also handling international customer data quality issues, supplier master data management has always been about international data quality challenges for most businesses.
As with customer master data having supplier master data that is well aligned with the real world and that can be maintained to reflect changes in the real world is indeed the starting point.
Data Stewardship is performed by data stewards.
What is a Data Steward?
A steward may in a general sense be:
- One employed in a large household or estate to manage domestic concerns – typically an old role.
- An employee on a ship, airplane, bus, or train who attends passengers needs – typically a new role.
My guess is that data stewardship also will tend to be going from the first kind of role related to data to the latter kind role related to data.
The current data steward role is predominately seen as the oversight of the house-holding related to the internal enterprise data assets. It’s about keeping everything there clean and tidy. It involves having routines and rules that ensure that things with data are done properly according to the traditions and culture in the enterprise.
Big Data Stewardship
In the future enterprises will rely much more on external data. Exploiting third party reference data and open government data and digging into big data sources as social data and sensor data will shift the focus from looking mostly into keeping the internal data fit for purposes.
As such you as a data steward will become more like the steward on a ship, airplane, bus or train. Data will come and go. After a nice welcoming smile you will have to carefully explain about the safety procedures. Some data will be fairly easy to handle – mostly just spending the time sleeping. Other data will be demanding asking for this and that and changing its mind shortly after. Some data will be a frequent traveler and some data will be there for the first time.
So, are you ready to attend the next batch of travelling data on board your enterprise?
Systems supporting faster and more accurate registration of addresses are becoming more and more common along with that they are becoming better and better.
I have noticed a structured and an unstructured approach to rapid addressing – and hybrids of course.
The general concept is that you target in on the address like this:
- First you choose a country from a country list (unless it’s always the same country).
- Then you select a state or province if a state or province is a mandatory part of an address in that country like it is in the United States, Canada, Australia and India
- Then you type a postal code if the country has a postal code system. It may be suggested as you write.
- Then you type a street if the country has thoroughfare based addressing. It may be suggested as you write. For some countries, like the United Kingdom, or part of a country the street is unique by the postal code.
- Then you type a building number. May be suggested if present in reference data.
- Then you type a unit or other section of building where applicable. May be suggested if present in reference data.
You type in the sequence in a single string as it suites you and the system figures out along the way what matches and makes suggestions.
This approach may better fit the way the address is known to you, but does on the other hand sometimes require you to start again and thereby the rapidness disappears a bit.
A common hybrid solution as that you select the country before going unstructured. That cures the worst system glitches.
What’s Your Approach?
What are your experiences as a user? Maybe you are developing rapid addressing and have had your considerations. Where do you stand?
When calling people in order to have a long distance conversation there are three main ways today:
- The landline phone, which have been around since the 19th century and penetrated most homes and businesses in the last century
- The mobile phone, which came around in the 70’s and spread rapidly in the 90’s
- Skype, a voice over internet service that grew in the 00’s
Using these services involves and identifier which may be stored in customer tables and other party master data repositories with some implications for data management and identity resolution:
The Landline Phone Number
The landline phone number is a very common attribute in databases around and is often used as the main identifier of a customer in ERP and CRM solutions around.
Using a landline phone number for identity resolution has some challenges, including:
- As with most attributes they may change. Depending on the country in question they may change during relocation and most phone number systems gets and upgrade over the years.
- In business-to-business (B2B) a company typically has more than one phone number.
- In business-to-consumer (B2C) the landline phone number merely belongs to a household rather than a single individual. That may be good or not good depending on purpose of use.
The Mobile Phone Number
Mobile phone numbers also piles up in databases around. In relation to identity resolution there are issues with mobile phone numbers, namely:
- They change a lot.
- It’s not always clear to who a number actually belongs:
- A company paid phone may be used for both business and pleasure and may be transferred to another individual
- In a household a person may be registered for a range of mobile phones used by individual members of the household including children
The Skype ID
I seldom see databases with Skype ID’s. In my experience Skype ID aren’t used a lot in internal master data. They reside in Skype and social network profiles like for example LinkedIn.
A final rant
Today I hardly ever use a landline phone, I use my mobile once in a while and I use Skype a lot. Not because it’s convenient, but because the telecom companies has decided to charge international mobile calls in ways so greedy that it make Somali sea pirates look like honest business men.
The Information Difference is an analyst firm that every year publishes a free online paper ranking the data quality tool vendors. The 2013 data quality tool landscape is out now.
An interesting trend is the shifts in who is in the main picture. Here are the 2012 and 2013 participants:
The number of x’s is a rough measure of market strength.
While X88 is a new vendor in the landscape there are four vendors that have dropped from the main picture to the list of other vendors.
I have earlier compared the Gartner Data Quality Tool Magic Quadrant and The Information Difference Landscape in the post The Data Quality Tool Vendor Difference and put the spot light on Experian QAS as a vendor appearing differently by not being in the Gartner Quadrant as reported here. This year Experian QAS also have dropped from The Information Difference Landscape main picture. Not the way to go I guess considering the many efforts of Experian QAS to be a leading data quality tool vendor.
Other vendors have dropped from their position in the picture. DQ Global is one. Oracle as well. And then Talend. Both Oracle and Talend are doing much more than data quality and probably some focus has shifted to other things. Talend for example has emphasized a lot on big data recently.
It’s going to be exciting to see what happens on another source of truth, being the Gartner Data Quality Tool Magic Quadrant, this year.
Some of the established vendors in the Master Data Management (MDM) realm may be working on integrating social data and some apparently don’t. Either way as with many other new technologies we will probably see the big movements coming from entrepreneurs.
I have noticed some new startups. Two is not surprisingly coming from the San Francisco Bay area and one is maybe surprisingly coming from the Saint Petersburg that is the original one in Russia.
Reltio is working with multi-channel, including the social channel, data integration. Their raison d’être is:
“As a business user in Sales, Marketing or Compliance you always work with information from multiple sources of data, then why is it that most of your existing applications cannot handle data from multiple sources (internal, third party or social) or channels of interaction to provide you with the benefit of insights from this related information. Reltio is working to fill this gap….”.
Fliptop is doing the matching between your current party master data records and the same real world entities in the social sphere:
“Fliptop’s Customer Intelligence platform provides companies with an on-demand data scientist for their leads and contacts. Using publicly available information including social data to score and enrich leads, companies can prioritize their pipeline, better target their audience and know more about their customers.”
Actualog is into Social PIM (Product Information Management):
“Actualog is an innovative cloud-based social Product Information Management platform that brings together the expertise and knowledge of the manufacturers and most competent customers around the world. Actualog helps companies to share information about products, materials and technologies focusing on complex technical products using the ideas of social interaction.”
Have you noticed some Social MDM and related startups? – or are you actually one?
The ”First Time Right” principle is a good principle for data quality and indeed getting data right the first time is a fundamental concept in the instant Data Quality service I’m working with these days.
However, some blog posts in the data quality realm this week has pointed out that there is a life, and sometime an end of life, after data has hopefully been captured right the first time.
In the post From Cable to Grave by Guy Mucklow on the Postcode Anywhere blog the bad consequences of a case of chasing debt from a customer not among us anymore is examined.
Asset in, Garbage Out: Measuring data degradation is the title of a post by Rob Karel on Informatica Perspectives. Herein Rob goes through all the dangers data may encounter after being entered right the first time.
Some years ago I touched the subject in the post Ongoing Data Maintenance. As told here I’m convinced, after having seeing it work, that a good approach to also getting it right the last time is to capture data in a way that makes data maintainable.
Some techniques for doing this are:
- Where possible collect external identifiers
- Atomize data instead of squeezing several different elements into one attribute
- Make the data model reflect the real world
And oh, it’s not the first time, neither the last time, I will touch this subject. It needs constant attention.