La Lune met autant de temps pour faire une rotation sur elle-même que pour effectuer une orbite complète autour de la Terre. En astronomie on appelle ça une rotation synchrone.
Vu du dessus, la Terre tourne sur elle même dans le sens contraire des auguilles d’une montre et la Lune orbite dans le même sens
…plus de 13 000 applications iPhone sont téléchargées, plus de 370 000 minutes de conversations sont utilisées via Skype, plus de 6600 clichés sont publiés sur flickr, et 168 millions d’emails (vous avez bien lu !) sont envoyés !
Comment sait-on tout ça ? Et bien, grâce à la dernière infographie en date offerte par Shanghai Web Designers. CQFD. Notez tout de même que certains, pour ne pas dire la plupart, de ces chiffres, demeurent TRES impressionnants, sur bien des points !
The First Law of Data Quality explained the importance of understanding your Data Usage, which is essential to the proper preparation required before launching your data quality initiative.
The Second Law of Data Quality explained the need for maintaining your Data Quality Inertia, which means a successful data quality initiative requires a program – and not a one-time project.
The Third Law of Data Quality explained a fundamental root case of data defects is assuming data quality is someone else’s responsibility, which is why Data Quality is Everyone’s Responsibility.
The Fourth Law of Data Quality explained that Data Quality Standards must include establishing standards for objective data quality and subjective information quality.
The Fifth Law of Data Quality explained that a solid Data Quality Foundation enables all enterprise information initiatives to deliver data-driven solutions to business problems.
The Sixth Law of Data Quality
“Data quality metrics must be aligned with business insight.”
Business-relevant metrics align data quality with business objectives and measurable outcomes.
There are many data quality metrics, which are alternatively referred to as data quality dimensions. In her great book Executing Data Quality Projects, Danette McGilvray provides a comprehensive list of data quality metrics, which include the following:
- Timeliness and Availability – A measure of the degree to which data are current and available for use as specified and in the time frame in which they are expected.
- Data Coverage – A measure of the availability and comprehensiveness of data compared to the total data universe or population of interest.
- Duplication – A measure of unwanted duplication existing within or across systems for a particular field, record, or data set.
- Presentation Quality – A measure of how information is presented to and collected from those who utilize it. Format and appearance support appropriate use of the information.
- Perception, Relevance, and Trust – A measure of the perception of and confidence in the quality of data, i.e., the importance, value, and relevance of the data to business needs.
Although there are many additional data quality metrics (as well as alternative definitions for them), perhaps the two most common data quality metrics are Completeness and Accuracy.
Completeness is generally a measure of the presence of an actual data value within a field, excluding NULL values and any non-NULL values indicating missing data (e.g., character spaces). Completeness can also be used as a measure of the absence of some of the sub-values that would make a field complete (e.g., a telephone number in the United States missing the area code). Either way, completeness is not a measure of the validity or accuracy of the values present within a field.
There is a subtle, but important, distinction between the related notions of validity and accuracy.
Validity is the correctness of a data value within a limited context such as verification by an authoritative reference. Accuracy is the correctness of a valid data value within an extensive context including other data as well as business processes.
Validity focuses on measuring the real-world alignment of data in isolation of use. Accuracy focuses on the combination of the real-world alignment of data and its fitness for the purpose of use.
A common mistake made by those advocating that data needs to be viewed as a corporate asset is measuring data quality independent of its business use and business relevance, which is why most data quality metrics do a poor job in relaying the business value of data quality. Without data quality metrics that meaningfully represent tangible business relevance, you should neither expect anyone to feel accountable for providing high quality data, nor expect anyone to view data as a corporate asset.
Therefore, every data quality metric you create must be able to answer two questions :
- How does this data quality metric relate to a specific business context?
- How does this data quality metric provide business insight?
Libellés : Data, Data Management, DataFlux, Données, Qualité des données
Deezer propose une nouvelle expérience musicale en ligne avec un site 5 fois plus rapide, incitateur à la découverte et au partage de musique. Avec 1,2 million d’abonnés payants à l’offre Premium, Deezer accélère la conversion des utilisateurs les plus actifs vers des offres payantes.
Via Doc News
Libellés : Data, Data Management, Informatica, MDM, Qualité, Qualité des données
Have you ever received an offer from a company for a product you already own? How about two identical offers for the same product… or even three offers? It’s not uncommon. With all the great applications for campaign management and customer relationship management (CRM) that companies have implemented, how do these costly mistakes keep happening?
The problem isn’t the applications—it’s the customer data they use. In many organizations, customer data is contradictory and incomplete across multiple systems. One reason is that when you made a purchase, you might have entered the name “John” for shipping information, and “Jonathan” for billing.
Customers change their personal and professional addresses, phone numbers, email addresses and other identifying data that companies use to make cross-sell and up-sell offers. Customer data inconsistency can become a nightmare for the company, and most organizations lack the processes and technology needed to empower customer-facing teams such as marketing, sales and customer service with a complete and trusted customer view, which is an essential ingredient to attracting and retaining customers.
This lack of customer data integration means that marketing doesn’t have an accurate customer list for segmentation. They’re unable to send the right offer to the right person at the right time because they can’t be sure which customers have purchased which products and services. They end up wasting time, budget and opportunities.
There’s another cost that’s much harder to measure. If you receive two identical offers for a product or service you already purchased, you probably think the company doesn’t have its act together. You might be tempted to look at an alternative provider. A company that doesn’t have its customer data in order risks damage to its brand reputation and credibility (especially with the increased use of social media to share bad experiences).
Add in the high costs to sales and customer service, and bad customer data amounts to a huge impediment that hinders a company’s ability to attract and retain customers and puts a drag on the top and bottom line.
L’analyse de l'évolution du trafic sur les sites médias/d'actualités mesurés par Médiamétrie-eStat - aux mois de mars, avril et mai 2011 - met en évidence l’impact de l’actualité très intense du mois de mai.
Le Tsunami au Japon, le décès de Ben Laden, l'affaire DSK, le Mariage princier ont constitué vérirtablement des "boosters" d'audience.
Voir le classement des sites (nombre de visites en mai 2011)
"As part of my work I deal with data from different countries. In the below figure I have put in some examples of different presentations of the same data from some of the countries I meet the most being Denmark (DK), Germany (DE), France (FR), United States (US) and United Kingdom (GB):
I have some more information on the issues regarding the different attributes :
Alors ça, si ce n’est pas une rumeur capable de vous tenir en haleine à quelques minutes de l’arrivée de votre week-end (peut-être) prolongé : à en croire le très influent Eldar Murtazin, Microsoft aura mis en place une offre visant à acquérir Nokia contre la somme de 19 milliards de dollars. Rien que ça. Une histoire de boucle bientôt bouclée ? Sans doute, dans le sens où d’une part, l’actuel PDG de Nokia, Stephen Elop, a gagné la plupart de ses galons au sein de la firme de Redmond et que Nokia a d’ores et déjà misé son futur sur la percée de Windows Phone 7…
Via le JournalduGeek.com