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.
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.
One 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.
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.
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.
A frequent update on my LinkedIn home page these days is about the HiPPO principle. The HiPPO principle is used to describe a leadership style based on priority for the leader’s opinion opposite to using data as explained in the Forbes article here.
The hippo (hippopotamus) is one of largest animals on this planet. So is the rhino (rhinoceros). The rhino is critically endangered because it is hunted by humans due to a very little part of its body, being the horn.
I guess anyone who has been in business for some years has met the hippo. Probably you also have experienced a rhino hunt being a project or programme of very big size but aiming at a quite narrow business objective that may have been expressed as a simple slogan by a hippo.
A 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).
The 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.
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 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.
Many moons ago I wondered how my social influence is measured as told in the post Klout Data Quality.
Since then my Klout has dropped a bit from 59 to 57. It does not ruin my day, but I wonder why. A thing that strikes me is from where I get my Klout. It seems Twitter is the place as it counts for 73 % of my Klout. LinkedIn is only 8 %. Personally, I would give them opposite importance.
Recently I noticed I was included in a list called Top 200 Thought Leaders in Bigdata Analytics. Honorable maybe. However, I am afraid it merely is a count of how many #Bigdata tags I have used on Twitter relative to others.
What matters to me in social influence seems to be out of scope for Klout, as it is readers and comments on this blog.
What about you. Do you have the right Klout? Is it measured the right way?
Recently I changed one of my job titles on LinkedIn resulting in a number of likes, congrats and messages. And thanks for that.
Probably I have a record in a number of CRM systems out there where I am registered as a contact for an account with an attached job title. As a guy working at several places at the same time I am a bit complicated, I have to admit, so I guess many of these records aren’t up to date about where I work carrying what title and having what means of contact.
Complicated or not, I have no doubt about that many CRM implementations will benefit from digging into social networks in order to be up to date and complete as told in the post Social MDM and Complex Sales.
As discussed in the post Multi-Facet MDM we may divide master data management into handling these facets:
Within party master data management events may be captured during interacting with your (prospective) customers and other business partners, as an update from a third party reference data provider or in an increasing way by monitoring social networks which are often the first to know certain things, not at least when it’s about contacts in Business-to-business (B2B) activities.
The distinction between IT and business is an often used concept in a lot of disciplines from Enterprise Architecture (EA) over data quality management to Master Data Management (MDM). While it may be a good concept to use when assigning responsibilities and finding out who should be driving what I have personally always kind of disliked the concept. It’s a concept practically only used by the IT side and in my eyes IT is part of the business just as much as marketing, sales, accounting and all the other departments are.
So, now when business is going social, how does that affect the IT and business distinction?
Social business is in large parts done today in the enterprises around without involving the internal IT department. The use of data and functionality in social business done with so called systems of engagement is much more external focused than the internal focused nature of the traditional systems of record.
If you were to build a service that could avoid postings with disputable quotes, what considerations would you have then? Well, I guess pretty much the same considerations as with any other data quality prevention service.
Here are three things to consider:
Getting the reference data right
Finding the right sources for say reference data for world-wide postal addresses was discussed in the post A Universal Challenge.
The same way, so to speak, it will be hard to find a single source of truth about what famous persons actually said. It will be a daunting task to make a registry of confirmed quotes.
1) A good and simple option could be to periodically scan through postings in social media and when a disputable quote is found sending an eMail to the culprit who did the posting. However, it’s probably too late, as even if you for example delete your tweet, the 250 retweets will still be out there. But it’s a reasonable way of starting marking up all the disputable quotes out there.
2) A better option could be a real-time check. You type in a quote on a social media site and the service prompts you: “Hey Dude, that person didn’t say that”. The weak point is that you already did all the typing, and now you have to find a new quote. But it will work when people try to share disputable quotes.
3) The best option would be that you start typing “If you can’t explain it simply… “ and the service prompts a likely quote as: “Everything should be as simple as it can be, but not simpler – Albert Einstein”.