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Aloha Facebook, Where am I Today?

Henrik Gabs Liliendahl2 Comments

Facebook is set to fight fake news by using artificial intelligence. A good way to practice may be by playing a bit more around with their geolocation intelligence.

Today I, as far as I know, are on the Canary Islands. This is a part of Spain, though a little bit away from the motherland down the Atlantic Ocean off the North African coast. A main town on the islands is called Las Palmas.

However, according to Facebook I seem to be in a place called Las Palmas Subdivision on Hawaii in the Pacific Ocean on the other side of the globe with Hawaii being a bit away from where it were last time I looked on a map.

Facebook Geolocation Hickup

 

A System of Engagement for Business Ecosystems

Henrik Gabs LiliendahlLeave a comment

Master Data Management (MDM) is increasingly being about supporting systems of engagement in addition to the traditional role of supporting systems of record. This topic was first examined on this blog back in 2012 in the post called Social MDM and Systems of Engagement.

The best known systems of engagement are social networks where the leaders are Facebook for engagement with persons in the private sphere and LinkedIn for engagement with people working in or for one or several companies.

But what about engagement between companies? Though you can argue that all (soft) engagement is neither business-to-consumer (B2C) nor business-to-business (B2B) but human-to-human (H2H), there are some hard engagement going on between companies.

pdl-whyOne of the most important ones is exchange of product information between manufacturers, distributors, resellers and large end users of product information. And that is not going very well today. Either it is based on fluffy emailing of spreadsheets or using rigid data pools and portals. So there are definitely room for improvement here.

At Product Data Lake we have introduced a system of engagement for companies when it comes to the crucial task of exchanging product information between trading partners. Read more about that in the post What a PIM-2-PIM Solution Looks Like.

Don’t Mess (Up) with Jensen

Henrik Gabs LiliendahlLeave a comment

Jensens fiskA big talk in the media in Denmark this weekend is the story about that a little harbor restaurant specializing in serving fish has been denied continuing using the name Jensens Fiskerestaurant (Jensen’s Fish Restaurant in English). A lower court has earlier disallowed the name Jensens Fiskehus (Jensen’s Fish House in English).

Jensen BeefThe opponent is a large restaurant chain called Jensen’s Bøfhus (Jensen’s Beef House in English).

This has brought a so called shitstorm over the restaurant chain in social media, not at least on Facebook. Jensen is the most common surname in Denmark. A bit more than a quarter of a million people, which is 5 percent of the population, are called Jensen. So how can a big chain be the only one allowed to use the name Jensen for a restaurant?

PS: I remember this nasty restaurant chain name from when I coded name parsing routines in the old days. “Jensen’s Bøfhus” initially came out as “S. Bøfhus Jensen”. Some of the remedy was to apply external reference data to name parsing as checking if a business entity with a similar name exists on the address.

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Winning by Sharing Data

Henrik Gabs Liliendahl1 Comment

When I changed my laptop a few months ago, it was the easiest migration to a new computer ever.

Basically I just had to connect to all the services in the cloud I had been using before and for many services the path was to get connected to Google+, Twitter and FaceBook and then connect to many other services via these connections.

ShareThis was a personal win.

Most of the teams I am working with are sharing their data with me in the cloud. As in the bad old days I do not have to call and ask for progress on this and that. I can check the status myself and even get notifications on my phablet when a colleague completes a task.

ShareThis is a shared win.

Within my profession being data quality improvement and Master Data Management (MDM) sharing data is going to be a winning path too as told in the post Sharing is the Future of MDM.

There are several ways of sharing master data like using commercial third party data, digging into open government data, having your own data locker and relying on social collaboration. These options are examined in the post Ways of Sharing Master Data.

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Identity Resolution and Social Data

Henrik Gabs LiliendahlLeave a comment
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Identity Resolution

Identity resolution is a hot potato when we look into how we can exploit big data and within that frame not at least social data.

Some of the most frequent mentioned use cases for big data analytics revolves around listening to social data streams and combine that with traditional sources within customer intelligence. In order to do that we need to know about who is talking out there and that must be done by using identity resolution features encompassing social networks.

The first challenge is what we are able to do. How we technically can expand our data matching capabilities to use profile data and other clues from social media. This subject was discussed in a recent post on DataQualityPro called How to Exploit Big Data and Maintain Data Quality, interview with Dave Borean of InfoTrellis. In here InfoTrellis “contextual entity resolution” approach was mentioned by David.

The second challenge is what we are allowed to do. Social networks have a natural interest in protecting member’s privacy besides they also have a commercial interest in doing so. The degree of privacy protection varies between social networks. Twitter is quite open but on the other hand holds very little usable stuff for identity resolution as well as sense making from the streams is an issue. Networks as Facebook and LinkedIn are, for good reasons, not so easy to exploit due to the (chancing) game rules applied.

As said in my interview on DataQualityPro called What are the Benefits of Social MDM: It is a kind of a goldmine in a minefield.

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From B2B and B2C to H2H

Henrik Gabs Liliendahl1 Comment

I stumbled upon an article from yesterday by Bryan Kramer called There is no more B2B or B2C: It’s Human to Human, H2H.

H2H

The article is about the implications for marketing caused by the rise of social media which now finally seems to eliminate what we have known as business-to-business (B2B) and more or less merges B2B and business-to-consumer (B2C).

As discussed here on the blog several times starting way back in 2009 in the post Echoes in the Database a problem with B2B indeed is that while business transactions takes place between legal entities a lot of business processes are done between employees related to the selling and buying entities. You may call that employee-to-employee (E2E), people-to-people (P2P) or indeed human-to-human (H2H).

Related to databases, data quality and Master Data Management (MDM) this means we need real world alignment with two kinds of parties:

While B2B and B2C may melt together in the way we do messaging the distinction between B2B and B2C will be there in many other aspects. Even in social media we see it as for example two of the most used social networks being FaceBook and LinkedIn clearly belongs mainly to B2C and B2B respectively for marketing and social selling purposes.

The different possibilities with B2B and B2C in the H2H world was touched in an interview on DataQualityPro last year: What are the Benefits of Social MDM?

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Third-Party Data and MDM

Henrik Gabs Liliendahl1 Comment

A recent blog post called Top 14 Master Data Management Misconceptions by William McKnight has as the last misconception this one:

“14. Third-party data is inappropriate for MDM

Third-party data is largely about extending the profile of important subject areas, which are mastered in MDM.  Taking third-party data into organizations has actually kicked off many MDM programs.”

business partnersIndeed, using third-party data, which also could be called big external reference data, is in my eyes a very good solution for a lot of use cases. Some of the most popular exploitations today are:

  • Using a business directory as big reference data for B2B party master data in customer data integration (CDI) and supplier master data management.
  • Using address directories as big reference data for location master data management also related to party master data management for B2C customer data.
  • Using product data directories such as the Global data Synchronization Network (GDSN®) services, the UNSPSC® directory and heaps of industry specific product directories.

The next wave of exploiting external data, which is just kicking off as Social MDM, is digging into social media for sharing data, including:

  • Using professional social networks as LinkedIn in B2B environments where you often find the most timely reference data not at least about contact data related to your business partners’ accounts.
  • Using consumer oriented social networks as Facebook for getting to know your B2C customers better.
  • Using social collaboration as a way to achieve better product master data as told in the post Social PIM.

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Introducing the Famous Person Quote Checker

Henrik Gabs Liliendahl5 Comments

quoteAs reported in the post Crap, Damned Crap, and Big Data there are data quality issues with big data.

The mentioned issue is about the use of quotes in social data: A famous person apparently said something apparently clever and the one who makes an update with the quote gets an unusual large amount of likes, retweets, +1s and other forms of recognition.

But many quotes weren’t actually said by that famous person. Maybe it was said by someone else and in many cases there is no evidence that the famous person said it. Some quotes, like the Einstein quote in the Crap post, actually contradicts what they apparently also has said.

As I have worked a lot with data entry functionality checking for data quality around if a certain address actually exist, if a typed in phone number is valid or an eMail address will bounce I think it’s time to make a quote checker to be plugged in on LinkedIn, Twitter, Facebook, Google Plus and other social networks.

So anyone else out there who wants to join the project – or has it already been said by someone else?

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Social Data vs Sensor Data

Henrik Gabs Liliendahl5 Comments

Social data sensor data big dataThe two predominant kinds of big data are:

  • Social data and
  • Sensor data

Social data are data born in the social media realm such as facebook likes, linkedin updates, tweets and whatever the data entry we as humans do in the social sphere is called.

Sensor data are data captured by devices of many kinds such as radar, sonar, GPS unit, CCTV Camera, card reader and many more.

There’s a good term called “same same but different” and this term does also in my experience very well describe the two kinds of big data: The social data coming directly from a human hand and the sensor data born by a machine.

Of course there are humans involved with sensor data as well. It is humans who set up the devices and sometimes a human makes a mistake when doing so. Raw sensor data are often manipulated, filtered and censored by humans.

There is indeed data quality issues associated with both kinds of big data, but in slightly different ways. And you surely need to apply master data management (MDM) in order to make some sense of both social data and sensor data as examined in the post Big Data and Multi-Domain Master Data Management.

What is your experience: Is social data and sensor data just big data regardless of source? Is it same same but different? Or are social data and sensor data two separated data worlds just both being big?

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Defining Social MDM

Henrik Gabs Liliendahl10 Comments

Social MDM2Social Master Data Management (Social MDM) has been a recurring subject on this blog for a couple of years. But what is Social MDM? What do others say it is?

Here is what Techopedia, Gartner and The MDM Institute thinks:

Techopedia has this definition of Social MDM:

Definition – What does Social Master Data Management (Social MDM) mean?

Social master data management (Social MDM) refers to the processes, policies and concepts used to gather and compile social media data sources – like Facebook, LinkedIn and Twitter – into one master file.

Gartner (the analyst firm) hasn’t to my knowledge used the term Social MDM. In 2011 they though said that this will be one of three main trends in MDM:

Increasing Links Between MDM and Social Networks

By 2015, 15 percent of organizations will have added social media data about their customers to the customer master data attributes they manage in their MDM systems….

In 2012, as reported in the post The Big MDM Trend, Gartner, The Hype Cycle firm, changed the point to be about MDM and Big Data.

The MDM Institute has a Field Report from April 2012 where Aaron Zornes (who is the MDM institute) writes:

Social MDM (Cloud-enablement, Architecture & Integration)

During 2012, cloud-enabled MDM will attract small- and mid-sized businesses as a means to engage in MDM without committing to long-term project and major expense….

By 2014-15, cloud-innate services for data quality and Data Governance will be more prevalent than full Social MDM…

So the MDM institute thinks Social MDM is MDM in the cloud.

What do you think?

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