Less than 1% of data could cost enterprises 4% of annual turnover in May 2018 Here at Silwood Technology, we’ve conducted research into five of the largest and most widely used application packages to understand the scale of the challenge encountered by our customers when locating personal data for GDPR compliance.
What comes before data extraction? In any data intensive project be it a data warehouse, data migration, master data there is a data discovery phase which has to be completed before any movement of data can be performed.
Top 3 Data Integration mistakes I saw a fascinating post by David Linthicum on the Informatica Blog recently in which he discussed the Top 3 Data Integration mistakes which companies make and how to avoid them.
data modeL Of all the blogs we’ve written here at Silwood, by far the most popular are the ones that are about specific Data Models for SAP, Salesforce and Oracle packages (JD Edwards, E-Business Suite, Siebel and PeopleSoft).
This is the latest in an occasional series of data models which will give some insight into those common business areas in SAP and ORACLE applications for which data models are often required for Information Management projects.
We’ve been surprised (pleasantly I might add) at the response we have received to the introduction of Safyr’s capability to reverse engineer metadata from Salesforce systems.
Well…er…Yes, and No. We get this comment lots of times. It’s sometimes hard to explain the difference. Strictly speaking, we do ‘metadata discovery’. I like analogies.
We would like to congratulate IBM, Informatica, SAP and Oracle on their continued strong showing in the latest Gartner Magic Quadrant for Master Data Management (MDM) of Customer Data Solutions.
I have been reading quite a lot recently about the applicability of Agile methodologies for Data Warehousing amongst other initiatives and it appears to me that often the topic of source data analysis is under-represented in the literature and blogs so I thought I would discuss the subject of application metadata in an Agile data […]
“Garbage out, garbage in” I know, I know I have got that the wrong way round. Well, actually I did it on purpose to illustrate a problem with BI/Data Warehousing projects and incidentally many other Information Management initiatives. That problem is the challenge of source data analysis – particularly with SAP, Oracle and Salesforce packages.