Analytics

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Use Power BI Decomposition Tree for Root Cause Analysis

Power BI Decomposition Tree Breaking Down Your Data Use the Decomposition Tree Visual in Power BI to explore your data and gain insights into how parts contribute to the whole.  Drill into hierarchies to see how each part contributes to the total.  Enable faster analysis for your team and gain data driven insights.  Using this visual enables you to break down a metric by the dimensions of your data to see the impact of each.  This can be used for root cause analysis such as failure of parts, operational processes or determining where you highest sales are coming from. While in Preview mode, you will need to enable the feature in the Power BI options and then restart Power BI.  The screenshot below shows the proper setup. Analyzing Data with a Decomposition Tree I am using a sample dataset of retail sales.  The sample dataset can be found here - [...]

By |2019-11-14T08:07:20-06:00November 13th, 2019|Power BI|0 Comments

Primed-AP (Analytic Process)

What is the Primed-AP Methodology? CDO Advisors has launched Primed Analytics Process Homepage to publish content related to our newly published Analytic Process.  The Primed-AP © process was created based on years of experience working with business users to implement predictive analytics.  Existing methodologies focus on the data science tasks but do not include the required tasks for business users.  In order to have a successful project everyone involved needs to understand the roles and responsibilities of the entire process.  Some phases of the process require collaboration between the business and data scientist.  While other phases require more time from the data scientist.  The following section outlines the phases and highlights the responsibilities for the project team.   Full details will be available on the website that describe each step in the process along with typical tasks for business users and data scientists.  This process will enable you to get more value [...]

By |2019-08-14T11:51:27-05:00April 1st, 2018|Data Mining|0 Comments