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.
As 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?
In a recent interview with yours truly on the Fliptop blog I had the chance to answer a question about how Social MDM is different from traditional MDM (Master Data Management). Check out the interview here.
As said in the interview I think that:
“The main difference between MDM as it has been practiced until now and Social MDM is that traditional MDM has been around handling internal master data and Social MDM will be more around exploiting external reference data and sharing those data.”
This is in line with a take away from the MDM Summit Europe 2013 as reported in the post Adding 180 Degrees to MDM.
But, as asked by a member of the Social MDM group on LinkedIn:
“What is the industry or analysts’ consensus on the meaning of Social MDM? Is it just gathering Master Data from social sources? Not really MDM – where is the Management part?”
You may follow the discussion here.
I definitely think that the management part is there, but it is different. Management is different in the social sphere in general. Data governance is different when it comes to social data (and other big data for that matter). Relying on social collaboration when maintaining master data is different from implementing “a data steward regime”.
In my eyes the management part is about balancing the use of internal master and the use of external reference data. Every organization should very carefully assess if they are good at maintaining different aspects of their internal master data (Hint: Many aren’t). Getting help from traditional data collectors and the new social sources and using social collaboration may very well be an important part of the solution.
Do we need a LinkedIn group for this and that? It’s always a question. There are already a lot of LinkedIn groups for Big Data and a lot of LinkedIn groups for Data Quality.
However I think we do see targeted discussions and engagement in the niche groups on LinkedIn, so therefore I created a new group about the intersection of Big Data and Data Quality yesterday. The group is called Big Data Quality.
It’s good to see a stampede of people joining (well, 39 within first 24 hours) and see discussions and comments starting.
So, if you haven’t joined already, please do so here.
And why not take part in the fun, maybe just by voting on the question: How important is data quality for big data compared to data quality for small data?
The 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?
In the past years social networks has emerged as a new source of external reference data for Master Data Management (MDM). But surely, there are challenges with the data quality related to this source.
Let’s look at a few examples from inside the data quality tool vendor space.
Who is head of Informatica in the social sphere?
There is a twitter account owned by Sohaib Abbasi:
Informatica is one of the leading data quality tool vendors and the CEO there is Sohaib Abbasi.
So, is this the real world individual behind the twitter handle @sabbasi the head of Informatica?
A social graph should indicate so: There’s a bunch of Informatica accounts and people following the handle (though that’s not worth the trouble as there is no tweets coming from there).
What about the one behind Data Ladder?
Data Ladder is another data quality tool provider, thought with a fraction of revenue compared to Informatica.
In a recent post I stumbled upon a strange situation around this company. In the social sphere the company for the last seven years has been represented by a guy called Simon as seen here on LinkedIn:
But I have reasons to believe that his real world identity is Nathan as explored in the comments to this post.
Data Quality tool vendors: It’s time to get real.
Let’s say LinkedIn opened a bank. Would you put money into the LinkedIn bank?
I don’t think I would if they used the same technology for accounting as they use for counting members in the LinkedIn groups.
The other day I made a happy tweet telling that the Social MDM LinkedIn group just got 400 members. And now today LinkedIn told me we are only 385 members. First thought: Jesus, 15 members left in a few days. Boring subject. Missing the hype before it even got inflated.
But when I went to the statistics page we were now 400 again:
Going back to the member list and refreshing it several times showed these results:
Well, I guess we are around 400 members. And oh, there’s room for more. Join here.
As told on DataQualityPro recently in an interview post about the Benefits of Social MDM, doing social MDM (Master Data Management) may still be outside the radar of most MDM implementations. But there are plenty of things happening with connecting CRM (Customer Relationship Management) and social engagement.
While a lot of the talk is about the biggest social networks as FaceBook and LinkedIn, there are also things going around with more local social networks like the German alternative to LinkedIn called Xing.
Last week I followed a webinar by Dirk Steuernagel of MRM24. It was about connecting your SalesForce.com contact data with Xing.
As said in the MRM24 blog post called Social CRM – Integration von Business Netzwerken in Salesforce.com:
“Our business contacts are usually found in various internal and external systems and on non-synchronized platforms. It requires a lot of effort and nerves to maintain all of our business contacts at the different locations and keep the relevant information up to date.”
(Translated to English by Google and me).
We see a lot of connectors between CRM systems and social networks.
In due time we will also see a lot of connectors between MDM and social networks, which is a natural consequence of the spread of social CRM. This trend was also strongly emphasized on the Gartner (the analyst firm) tweet chat today:
As reported in the post Fighting Identity Fraud with Identity Fraud and experienced with the post 255 Reasons for Data Quality Diversity I have seen several sloppy attempts of link building from SEO agencies working for data quality tool vendors.
The other day it happened again, this time on LinkedIn.
There was a comment in the Master Data Management Interest group:
The comment is now deleted by the author and I do understand why.
I guess a SEO guy was working for Simon at DataLadder and Nathan from somewhere else at the same time and given access to their LinkedIn accounts. However he/she posted a comment to be meant being from Simon logged in as Nathan (who is not working with MDM and data quality).
So, data quality tool and service vendors: You can’t fight identity fraud with identity fraud and you can’t advocate for a single view of customer with a messy view of you as a vendor. Be authentic.
These days LinkedIn are celebrating passing 200 million profiles.
This is done by sending us members a mail telling about our part in the success.
The mail message is easily sharable on LinkedIn, Twitter and Facebook. What I’ve seen is that you can be among the 1 % most viewed (including yours truly), the 5 % most viewed, the 10 % most viewed and among the first 500,000 members in a given country.
The latter incident includes for example being among the first 500,000 members in Malta.
I guess that will include every member in Malta as Malta has a population around 450,000, unless of course the Maltese are world champions in creating duplicate profiles.