Data Visualization for Cognitive Effects
In todays business world, there are various tools in getting business insights from data visualization. No longer are the days where Microsoft Excel are effective to present data charts, graphs & histograms whenever there are dynamic changes to the data source.
Hence, i embark my journey onto Tableau among various tools such as IBM BI Cognos, Microsoft Power BI, R Studio using R language, Spyder or PyCharm using Python language for data ingestion to present data for visualization of outliers, abnormalities, trends & patterns. The choice of Tableau was simply the ease without programming of code to visualize business outlook in the fastest possible time.
Here below i have attempted to achieve Data Visualization for Cognitive effect to present the business outlook in terms of product demand outlook changes (ie: fluctuation & ramping in volume) across several workweeks which is necessary to check if any BOM assemblies supplies & forecast are met as the challenges is supplier delivery lead-time is dynamic.
Otherwise without a context filter, the following query is run:
To gain insights on the TOP 10 product name(s) & product categories, SQL query can also be applied to present data in tabular form.
If my data set is heavily indexed, context filters may not provide performance improvement and may actually cause slower query performance. Context filters can adversely affect any query performance improvements when using the Include joined tables only when referenced option in the Tables dialog box.
To improve performance of context filters especially on large data sources, it is best to follow these basic rules of Using a single context filter that significantly reduces the size of the data set that is much better than applying many context filters. In fact, if filter does not reduce the size of the data set by one-tenth or more, it is actually worse to add it to the context filter because of the performance cost of computing the context.
Instead of setting other additional filters, single context filter can be used for monitoring wip inventories volume where OI ingots = “Y”
Not only is it important to have an effective Tableau dashboard in terms of its presentation and data to show the wip profile for respective product nickname(s) or workcell(s) , but there’s another variable to consider: loading speed performance to refresh data
