Nearly every client Edgewater Ranzal partners with uses statistical averages in their analytic and reporting solutions. As far as statistical functions go, it is probably the easiest to understand, however; the limitation of using the average is that it can be difficult to determine how to rate the individual performance of contributors to that average. Consider the following examples:
Recently, Ranzal has been working with a client in the healthcare space implementing Oracle Business Intelligence (OBI), and a requirement surfaced to translate a scorecard report into an OBI dashboard. One of the data elements was simply captioned “Trend” and colored red, yellow, and green. It was discovered that this Trend was the slope of a linear regression plot (more on what that means in a moment) and the color was based on an arbitrarily chosen number. This immediately raised some concerns from the Ranzal team who then made some suggestions for more pertinent statistical analysis.
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Ranzal was recently invited to participate in a number of chalk talks for the Healthcare Industry User Group (HIUG) in San Antonio, TX. One of these chalk talks covered how an organization should prepare for and execute an upgrade to Oracle Business Intelligence (OBI) 12c. Since the technical steps are already covered in numerous blog posts as well as Oracle documentation, our conversation focused on a strategic approach to the upgrade. Our conversation essentially came down to four topics:
The federation of EPM and relational data sources through Oracle Business Intelligence (OBI) seems straightforward: import the cube, federate and rename, expose it all, and create dashboards and analysis. Due to the technical simplicity of EPM and relational federation, many organizations underestimate the amount of effort needed to implement an OBI solution that properly leverages and extends the capabilities of the EPM and relational data sources. The OBI implementation process should not be an afterthought, especially if OBI is to be the primary method by which users consume organizational data. We have assembled ten “Dos and Don’ts” that cover the full lifecycle implementation to help organizations get the most out of their OBI solution.
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More and more Oracle customers are finding value in federating their EPM cubes with existing relational data stores such as data marts and data warehouses (for brevity, data warehouse will refer to all relational data stores). This post explains the concept of federation, explores the consequences of allowing hierarchical structures to get out of synchronization, and shares options to enable this synchronization.
Coupling disparate data sets into meaningful "mashups" is a powerful way to test new hypotheses and ask new questions of your organization's data. However, more often than not, the most valuable data in your organization has already been transformed and warehoused by IT in order to support the analytics needed to run the business. Tools that neglect these IT-managed silos don't allow your organization to tell the most accurate story possible when pursuing their discovery initiatives. Data discovery should not focus only on the new varieties of data that exist outside your data warehouse. The value from social media data and machine generated data cannot be fully realized until it can be paired with the transactional data your organization already stockpiles.