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Showing posts from April, 2020

AP Automation in Dynamics 365 Finance and Supply Chain

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As a solution architect, prospects and clients often ask me for ways to improve their Accounts Payable process.  They receive hundreds of invoices every month, and it can take considerable amount of time to enter all of the detail as well as attaching the invoice for reference during the approval process.  This is an area where we have often needed to bring in an ISV solution, but that could cause other issues.  Microsoft has been doing considerable work around automation for AP and they recently released a public preview of their Invoice Capture solution. I have had the opportunity to set this up in one of my test environments so I could get an idea of what they have to offer.  As we would expect, the Invoice Capture solution is built on the Power Platform.  It uses a model driven Power App along with Power Automate Flows to integrate with Dynamics 365 Finance.  However, since it is a solution, they have done much of the work for you, including creating the standard flows to load the
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Covid Dashboard - Article 6 - Creating a group for a slicer

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I will continue to build on my earlier posts about my  COVID19 Dashboard .    In this series, I have written a few different blog articles describing not only how I built it, but some things I learned in the process. The current list of topics include: Working with growing CSV files Using index and merge when transforming data Using time functions to calculate the daily change Reporting based on the population Working with maps, setting data types correctly Today I am going to look at a question one person asked me.  When they were using the dashboard, they wanted to be able to filter the global results by countries of similar size. After thinking about this for a while, I decided on the following approach.  There are probably different approaches that can be used, and that is one of the things I really like about Power BI.  In this case, I tried to take a very simple approach. Step 1 - I had to decide how I wanted to group the countries.   I took a look at
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COVID 19 Dashboard – Article 5 – Working with maps

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I will continue to build on my earlier posts about my  COVID19 Dashboard .    In this series, I am writing a few different blog articles describing not only how I built it, but some things I learned in the process. The current list of planned topics include: Working with growing CSV files Using index and merge when transforming data Using time functions to calculate the daily change Reporting based on the population Working with maps, setting data types correctly Possibly more to come depending on how long this all goes…. PART 5 – Working with maps I will be the first to admit, I have not used the mapping features in Power BI very often.   I have used them for some simple demos, but being an accountant, I am more often interested in the numbers versus the visual location.   However, when analyzing a global pandemic, I felt a map visual or two might be important. So without going to any custom visuals, there are four map visualizations in Power BI.   Map Fille
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COVID 19 Dashboard – Article 4 – Reporting based on population

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I will continue to build on my earlier posts about my  COVID19 Dashboard .    In this series, I am writing a few different blog articles describing not only how I built it, but some things I learned in the process. The current list of planned topics include: Working with growing CSV files Using index and merge when transforming data Using time functions to calculate the daily change Reporting based on the population Working with maps, setting data types correctly Possibly more to come depending on how long this all goes…. PART 4 - Reporting based on population In looking at the data, I quickly realized simply looking at total numbers or even daily changes was only giving me a limited view of what was happening.     Were the large number of cases in New York simply because they have more people than most other states, or were they growing at a different rate.   Are New York citizens at a higher risk of acquiring the virus than California citizens.   It became evident
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COVID 19 Dashboard - Article 3 - Adding some key measures - unique time functions

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I will continue to build on my earlier posts about my COVID19 Dashboard .   In this series, I am writing a few different blog articles describing not only how I built it, but some things I learned in the process. The current list of planned topics include: Working with growing CSV files Using index and merge when transforming data Using time functions to calculate the daily change Reporting based on the population Working with maps, setting data types correctly Possibly more to come depending on how long this all goes…. PART 3 – Adding some key measures In my earlier posts, I spent time explaining where I acquired the data and some key steps I took in transforming the data into the data model.   In this post, I will explain some of the DAX I used to create some of the key measures for the visualizations.   One of the first steps in design is to identify what questions you are trying to answer or the story you are trying to tell with your visualizations and reports.
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COVID 19 Dashboard – Article 2 - Transforming the Data – Calculating between rows in Power Query

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I will continue to build on my earlier posts about my COVID19 Dashboard .   In this series, I am writing a few different blog articles describing not only how I built it, but some things I learned in the process. The current list of planned topics include:   Working with growing CSV files   Using index and merge when transforming data   Using time functions to calculate the daily change   Reporting based on the population   Working with maps, setting data types correctly   Possibly more to come depending on how long this all goes…. PART 2 – Continuing to transform the data In my first post, I explained how I brought in data from a CSV file and used “Unpivot Columns” to transform the data into a format that works much better for reporting.   Once the data was in this format, I quickly noticed that the values (Cases, Deaths and Recoveries) were cumulative.   In other words, these amounts were ending balances not daily transactions, if I look at it as an accountant.
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