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From datadoodle,
So many people who should know better seem to miss the point when they mention Tableau. Why? I asked BI veteran Stephen McDaniel for his thoughts — which he gave, but then went on to suggest an almost unheard of challenge: a data analysis face-off among vendors
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I recently pondered the value of a great analyst to a mid-sized public company (revenues around $1-2 billion.) Based on my experience at multiple companies, I estimate the productivity, typical project benefits, and costs of an average, good and great analyst. The differences in the return to a company are staggering with great analysts yielding double the ROI of a good analyst and triple the ROI of an average analyst!
My conservative estimates of analyst project results (annual value to the firm) are $460k for average analysts, $1.2m for good analysts and $3.1m for great analysts!
There are two primary drivers behind the much higher value produced by great analysts (illustrated in the following graph):
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My Background in Data Warehousing
I have been involved in the creation, development, and maintenance of seven data warehouses through the years- one of them before I even knew about the term “data warehousing”! I have built them with SAS and Oracle, SAS alone, Informatica and Oracle, Oracle alone, and SQL Server.
I have also visited many companies as an adviser, consultant and user of their data warehouse. In these many visits, I have seen some successes and many failures. Often, the failures could have been prevented with some key guiding principles.
Data Warehousing or Enterprise Data Integration?
Data warehousing is now known by a new buzz word, Enterprise Data Integration. In fact, SAS recently renamed SAS ETL Studio as SAS Data Integration Studio (they also added some new features around the EDI area, one new feature was around continual data acquisition so that near real time data feeds are available in the data warehouse.) Another great part of SAS EDI is SAS Data Quality, this should be a consideration throughout the entire process, but I won’t directly comment about data quality in this post. Since most people still use the term data warehousing, so I will keep the popular terminology over the analysts and even SAS.
What Does it Take to Build a Successful Data Warehouse? Read the rest of this entry »
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