Приглашаем всех желающих на открытый LAN турнир Федерации Киберспорта Московской области 3-4 июня 2017 г. ДС "Триумф" Люберцы, Смирновская, 4. CS GO 2x2; FIFA 17 1x1; DOTA 2 1x1. Регистрация и подробная информация ФКСМО.РУС.
Siapa bilang pake maxidress itu ribet?? Maxidress selain bikin kamu lebih ELEGANT & CANTIK tapi juga pssstt bisa bikin kamu look more SLIM & superrr modissshhhh.. makanya Hamidah Maxidress ini gak boleh terlewat dari list kamu yahh
Gak mau kehabisan?? Cuss order gercep gak pake lama ya ke admin LINE & WA
Code: MADINA TUNIC MAXIDRESS MAROON
Size: allsize fit to L (LD: 90 cm, P: 145 cm, Panjang tangan: 57 cm)
Material: Premium Satin Roberto Cavalli
"Material nya oke bangett, halus, lembut, adem pokoknya bikin kamu COMFY deh dipake apalagi di acara yang lama banget.. udah gitu dengan cuttingan SIMPLE & clean bikin kamu tambah ELEGANT tapi tetep STYLISH MUST HAVE ITEMS banget kalau kata mimin
HOW TO ORDER?
If you’re in the business of pretty much anything, you’ve got some important data hanging out at your company. In fact, you probably have a lot of important data in a lot of different places – internal and external. What you might be lacking are the data management best practices that could help you get to all of that data and take a closer look at it. Doing that just might give you a glimmer of insight that could nudge your business into a brand new market, or send profits soaring beyond all expectations.
But what, and where, IS all the data that’s relevant to your business? Can you access it when you want it? Do you know that it’s accurate, current, clean and complete? Can you easily pull all the data together, no matter what format it’s in or how often it changes?
The big question here: Is your data ready to support business analytics? An often-ignored truth is that before you can do really exciting things with analytics, you need to be able to “do” data first. Data management, that is.
Download a white paper about data management best practices
Data management best practices = better analytics
Sure, plenty of companies have done analytics on data that wasn’t really prepared for analytics. Their data might have been incomplete – maybe the company infrastructure couldn’t accommodate some new data format, like unstructured data from text messages. Or maybe they were working from duplicate data, corrupt data or outdated data.