|
|
|
|
Top Articles
By Doug Camplejohn on May 06, 2015 11:37 am
 Big data is a big deal. Unfortunately, due to the sheer volume and velocity at which its created, many organizations still struggle to make sense of their data. Furthermore, because data is dispersed across so many disparate platforms (e.g. marketing automation, CRMs, Google Analytics, etc.), it’s become increasingly difficult to merge and analyze this data in meaningful ways. In recent years, however, technology has made it easier to bridge the gaps between siloed data sources, and provide a unified view of data across platforms. Historical data can then be analyzed to identify patterns in past performance, and predict future outcomes. Paired with the right technology, big data has ushered in a new era of data-driven sales and marketing—the age of the Predictive Enterprise. While predictive technologies may appear simple on...
Read in browser »
 
By Nenshad Bardoliwalla on May 04, 2015 11:22 am
 The collision between people and big data has caused an explosion of machine learning innovations, with one natural home being in modern data preparation – the steps of understanding, cleaning, shaping, and correlating data prior to it being ready for analytics. For thirty years, there have really only been two data preparation processes: first, the human-led, coding and scripting, trial-and-error approach, which can’t scale when datasets are constantly changing and being generated regularly from new disparate sources. The other: the rigid path of ETL (Extract, Transform, Load), where a schema and set of mappings was built and could not be changed without an act of Congress. Neither of these options allows for people to process, analyze or derive insight from the volumes of data they are collecting, as rapidly as...
Read in browser »
Recent Articles:
|
|
Top News
It looks like you have a lot of content. To preview your campaign, send a test email.
|
|
Dataconomy Job Board
It looks like you have a lot of content. To preview your campaign, send a test email.
|
|
|
|
|
|
|
