Tableau on the other hand has always invested in the visualisation layer first, because Visualisations have a lot of “sizzle”, and that is what Senior Execs get excited about.
Why? Well in the world of Sales and Marketing, it is common knowledge that you should “sell the sizzle, not the steak”. In fact it was also a brave decision but definitely not the sexy decision. The fact of the matter is that Microsoft decided to build the data modelling capabilities (Power Pivot) and data acquisition capabilities (Power Query) first, before investing heavily in visualisation capabilities. And then the big one – in October 2015 Microsoft announced that anyone can import a custom visual directly into Power BI without any coding at all.In Sept 2015, it held a competition to challenge talented programmers and analysts to build the best visualisations they can think of.At or about the same time, Microsoft announced that it would allow people to contribute their own visualisations so that others can share them.First it announced that it was opening up access to its Power BI visuals tool kit so that anyone with the skills and the desire could build their own visualisations on their own You can read about that here.Back in July 2015, Microsoft started a chain of actions and announcements that will prove to be a game changer (in my opinion) for Power BI moving forward. Ultimately there has to be some prioritisation, and along with the prioritisation comes compromises and ultimately disappointments for some (many?).
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No software company can build every single feature that everyone wants to see all at once.
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The Best Way to Install Power BI Desktop.30 Reasons You Should Be Considering Power BI.Dimensional Modeling (Excel and Power BI).Power BI for the Business Analyst (with live Q&A).Successful orders in 2012 account for 90% of total orders, $269,880 profit and $3,275,543 in sales. This implies perhaps some of the trucks are failing to deliver goods on time. The pie chart can be drilled into to find out which method of shipment can possibly be causing the loss of sales, comparing the orders refunded to orders successful shows that delivery trucks have a higher percentage within the orders refunded category. The data tags show $444,421 in sales was lost during 2012 for refunded orders, as well as $63,401 monthly (last) sales. The pie chart in this example was used to separate refunded orders from successful orders. Further action could be taken from this point on to improve profits in the following quarter. Now the records show for sub category tables, sorting by profit in the table below allows the user to deduct which products are causing the loss of profits. Now it can be noted that bookcases and tables are slowing down profits.ĭrilling down further shows the products, however there are many so right clicking and using ‘show data’ helps in this situation. In order to investigate which products are causing this drilling needs to be performed.ĭrilling down into Furniture category, shows the sub-categories. It can be noted profit for Furniture Category (bottom right chart) is negative. This dashboard also contains custom tool tips, additional information such as number of orders and returns were added to the chart (bottom right). However it should be noted the KPI indicators are set to show end of month, therefore if the time filter is set to a quarter it will still calculate performance using month to month basis, this measure can be changed however. Notice the KPI turned to 3 points instead of 12, indicating 3 month period. Switching to the quarter time filter for the final quarter of 2012 causes the entire dashboard’s metrics to change. The time filter is able to navigate through years, quarters, months, weeks, days. Two custom visuals were added to the dashboard, these include KPI and advanced time filter.
Upon first glance the dashboard shows Prarie being the most profitable region, technology being the most profitable product category, office supplies being the highest selling (volume) category and that month to month sales and profit targets were met.
Kpi indicators for end of month performance.Tags for total orders, profit and revenue.Additional columns were added within Power Bi query editor to complete the data set. It contains approximately 24 columns and a few lookup tables for returns and managers. The data for this dashboard was imported from this superstore sales worksheet.