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

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. On the other hand, when the business users control their own data and BI, they can be much more agile and thus able to glean more value from their data, faster. Figure 1 Business Intelligence & Analytic Data Components. Trend #1: BI and analytics enable better response to dynamic and diverse user needs. Apr 17, 2012 - Agile provides a streamlined framework for building business intelligence/data warehousing (BIDW) applications that regularly delivers faster results using just a quarter of the developer hours of a traditional waterfall approach. Jan 16, 2014 - Both the specialty vendors and the heavy weights — are trying to find the balance between agile yet trusted data — user driven but IT controlled. We've seen Agile cut project costs in half Organisations will embrace the Agile approach, utilising new tools and technologies to decrease delivery times and demonstrate substantial business value. Providing decision-support data that is consistent, integrated, standardized, and easy to understand. Nov 5, 2013 - How to size the Big Data opportunity within the context of their organization; How to get Big Data technologies to work with cumulative years of investments in traditional business intelligence / data-warehouse technologies; How to execute on the Big Data effort without falling into the trap of approaching it as . Teradata has long provided an agile analytics data warehouse with an integrated data lab that provides a self-provisioning, self-service environment for swift prototyping and analysis on new, external and uncleansed data. Transition from fee-for-service to a value-based “continuum” approach. Apr 30, 2014 - As a result, there is confusion around their use in business intelligence and analytics communities with assumptions that NOSQL (Not Only SQL), NewSQL databases are a replacement for the relational databases in BI and analytics. BI teams that have taken a hands-off approach to visual data discovery tools need to support data provisioning and best practices; conversely, users who have relied on IT for every last sort, filter, and customization will need to Tip: Just because users aren't asking for mobile BI doesn't mean there is no business value. Nov 5, 2013 - The need for detailed requirements gathering, data quality and cleansing work is paramount in launching any business intelligence project. Centers for Medicare and Medicaid Ser- vices (CMS), payers and providers are taking steps to move realizing value from them. Oct 30, 2012 - Recently I read the book “Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing” by Ken Collier. This calls for an Agile-based execution approach as well as a cohesive project team that includes both business and IT members under a single team structure. See the book The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics (ISBN 978-1935504207) for details. Jul 2, 2013 - Based upon my experience, I can name at least nine data components that need testing and validation in a business intelligence project. 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. Guided primarily by changes in reimbursement policies by the U.S.