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Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing Ken W. Collier
Publisher: Addison-Wesley

It's that time of year again, so get your award . Aug 17, 2013 - In your choice for a BI and Data warehouse applications and a comparison of vendors it is important to know what the goal is for your BI Data Warehouse. While I found it interesting, there's a lack of rigor in the analysis that is a common problem when using surveys at many companies- misinterpreting trends from surveys based on small sample sizes. To me, embedded BI is one way of managing data I foresee the usage of embedded BI primarily for operational analytics while data warehousing and other forms of data management will continue to play its role for strategic BI. Mar 7, 2011 - Big, old, traditional BI companies are good at producing technologies that enhance the infrastructure of business intelligence—more and faster—but not the actual use of data in ways that lead to greater intelligence. Dec 18, 2013 - Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. Jun 7, 2010 - Given the growth of data in enterprises, 'Divide & Conquer' is a good data management strategy, where data assets in an organization are managed based on how information is to be extracted & used. The sessions delivered in this event introduce the framework and methodologies for modernizing data warehouses into data platforms that combine the latest data integration and analytic databases. My clients routinely tell me stories about building dozens of BI reports based on customer spreadsheets or requests, only to discover that most of these are rarely or never used. With over twenty-five years of experience delivering value through data warehousing and business intelligence programs, John O'Brien's unique perspective comes from the combination of his roles as a practitioner, consultant, and vendor CTO in the BI industry. Nov 26, 2013 - Their pitch centered on three areas – data warehousing, big data analytics and integrated marketing – that to some degree reflect Teradata's core market and acquisitions in the last few years of companies like Aprimo who provides integrated I want to note that the premise behind UDA is that the complexities of the different big data approaches is abstracted from the user which may access the data through tools such as Aster or other BI or visualization tool. There is first the difference in Both propose Star Schema Datamarts, business driven incremental development involving prototyping iterations (Agile) and report/query delivery from relational databases directly or via OLAP data repositories such as n-dimensional cubes. Dec 30, 2011 - I am currently reading a book by Ken Collier, called “Agile Analytics, A Value-Driven Approach to Business Intelligence and Data Warehousing”. VG- brief comment- agile vs adaptive. Feb 5, 2014 - Once a year, TDWI collects award applications, judges them, and gives awards in eleven categories in data warehousing, business intelligence, and data management. Dec 29, 2013 - In my opinion, it points back to visual analytics, dedicated teams of business analysts with clear missions and good data management and warehouse practices with an agile approach, so that major misses can be caught, reviewed and corrected mid-course. Nov 12, 2013 - As a proponent of agile data warehousing and business intelligence, I am constantly looking for new techniques for delivering value to customers faster, adapting to their feedback, and evolving toward the right business solutions It isn't about data warehousing, BI, or analytics … or is it? Sep 11, 2013 - In Forrester's Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012 survey of 634 BI users and planners, we found that social and mobile data is actually a pretty low priority: We wrote about big data hubs in our report, Deliver On Big Data Potential With A Hub-And-Spoke Architecture, and indeed we think most firms will shift from a data-warehouse-centric approach to one that favors low-cost data hubs. It also often requires setting realistic goals, rolling out functionality in a phased approach, and evangelizing and selling the organization on the value of BI, DW, DM, and analytics at every opportunity.