The below saying has become a popular share around in social media:
“Big data is like teenage sex. Everybody talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”
Indeed, there is quite a lot of hype around big data as for example told in The Big MDM Trend.
The teenage sex joke isn’t new at all. It has been used about a lot of new trends. I remember when the e-Business hype started, the joke was used here as well as you still can find some evidence about if googling the saying and getting this and that.
Today e-Business has matured and maybe a few brick and mortar bookstores have stopped laughing about the e-Business and teenage-sex joke now.
Also, maybe the joke says more about parents’ knowledge about teenage-sex.
Analyst firms have a lot of fun in making different surveys and rankings of vendors in different markets using their own special visualizing method. For the Master Data Management (MDM) market we have this year had the:
Lazy as I am I haven’t made my own survey but simply taken the brand names from the rankings mentioned above and filled in the name either 1, 2 or 3 times from each report depending on how well the brand was positioned.
So the size of the letters tells something about market positioning according to analyst reports. The size of the words also tells something about the length of the brand name. The placement is according to the wordle principle of course totally random.
And of course I now expect a load of tweets from vendor marketing departments saying that their company is positioned very randomly in the MDM Market Wordle 🙂
I’m sorry if this blog is turning into a travel blog. But here’s a third Paris story.
Boulevard Haussmann is one of the city’s great thoroughfares (to use the right meta-data term) and is known to be where we can find the headquarters of SPECTRE.
While visiting SPECTRE today I learned a lot about how SPECTRE is exploiting big data as an important way of keeping up with the tough competition in its industry sector today. But all that is of course a secret.
When asking about if they still has trouble with Bond the answer was:
“Bond? – Jimmy Bond? – The sexy data scientist who is working for NSA?”
“Oh no, I replied. James Bond.”
“Oh, yes” the SPECTRE chief data manipulator replied. “He was with British Intelligence. But he has been moved to the EU Data Protection Service. He just got his license to fine. Now 2% and soon 5% of our global turnover each time. Very dangerous man. Very dangerous”.
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?
LinkedIn is a great social service for professionals. I often read descriptions of LinkedIn with the sentiment that LinkedIn is a recruitment platform. However, in my opinion LinkedIn is much more than that. To me LinkedIn is more about networking, knowledge sharing, social marketing and social selling.
But that said, recruiters are certainly very active on LinkedIn. I guess it happens to me every week that I’m contacted on LinkedIn by a recruiter with a MDM (Master Data Management) job.
The opening is practically always like this:
“We are looking for a candidate with experience with <brand>….”, where <brand> is Informatica, Oracle, IBM, SAP and other well known brands in the MDM sphere.
As I don’t guess the recruiters make up the top requirement themselves, this number one requirement probably comes from the hiring organization. So to users of MDM, MDM is all about the software brand. Never mind people and processes. That’s easy. Technology is the hard part, not at least mastering the master data technology that was bought after a thorough selection process.
In here Jim ponders how working with Big Data must be build on a lot of other disciplines including Data Quality and the title of the blog post is nicely composed from the title of the fantastic Pink Floyd song called Another Brick in the Wall.
In this song there is an unpleasant voice of an angry stupid old teacher yelling:
“If you don’t eat yer meat, you can’t have any pudding. How can you have any pudding if you don’t eat yer meat?”
I’m afraid I also have to raise an equally unpleasant voice of saying:
“If you don’t eat yer data quality, you can’t have any big data. How can you have any big data if you don’t eat yer data quality?”