I have a very large data set that has 15,000 rows with a few descriptive columns and then the data itself is stored as monthly sales in 200 columns, one for each month. There are a couple of other data sets I need to connect this to so I want to be able to use Power Pivot to build the relationships.
How do I go about harnessing Power Pivot to build a dashboard for this data? Specifically is there a way to link all those date columns to a DimDate table so I can connect it to other data sets without reformatting the whole data set?
Related
I am trying to provide on of our FHF fundraising campaign managers with a tool they can use to help them pick postcodes to send a campaign to.
Spoecifcally, I'm seeking help writing an Excel PowerPivot Measure for a cumulative total that counts only the visible cells in a Pivot Table and wherein the source data is OLAP (i.e. an Excel Data Model - which seems to carry some constraints to possible solutions)
The linked worksheet is a simplified example of the excel worksheet tool I want to give to the Campaign Manager in a Fundraising setting https://www.dropbox.com/scl/fi/3pfc8ix2ekduoocsa90zs/minReprodExample_share1.xlsx?dl=0&rlkey=m6gqf5zbjxhl6dzkjq653it3d
We have two predictive models both giving different predicted response rates per postcode
The 'data' tab contains the raw data
The 'modelA_pivot' tab contains a pivot table ranking the raw data according to the predicted response rates from ModelA
'model_pivotB' does the identical pivot for ModelB
Focussing just on the 'modelA_pivot' tab for now
you can see a slicer that allows the campaign manager to exclude postcodes with only 3,000 addresses
(or some other threshold level of their choosing)
The slicer's exclusion of a single postcode with 3,000 addresses in this example is why you see the rank column in the pivot run; 1, 3, 4, 5
(i.e. missing rank 2 postcode with 3,000 addresses)
In the pivot, the last column - 'addressCount_postcodeCumulative_modelA' - is based on an excel Power Pivot 'Measure'
And the current formula of the measure is
=VAR rankCurrent = MAX([rank_modelA])
RETURN
CALCULATE(
SUM([addressCount_postcodePer]),
FILTER(
ALL(data),
[rank_modelA] <= rankCurrent
)
)
You can see in the 'modelA_pivot' tab the 'addressCount_postcodeCumulative_modelA' column
doesn't work or make sense. As it includes the cumulative total of ALL addresses (including the 3,000 addresses that are excluded from the pivot)
Can anyone help me with a 'Measure' formula that sums only the addresses of the postcodes that are visible and included in the pivot table
FYI and in case anyone is wondering, why a 'Measure'; I am using excel Power Query and Power Pivot so if/when the data upstream changes, the data team will be able to refresh this worksheet with a single click (more or less), and the campaign manager still gets to use excel (the tool they know and like)
But, the use of excel Power Query and Power Pivot and setting this up as an excel Data Model (which uses OLAP structured data)
is also injecting constraints which I'm hoping to fit the answer to this puzzle inside
Constraints such as;
I can't seem to put calculated fields onto the pivot table, and
I don't want to just add conventional excel columns on the side of the pivot table as the size of the pivot table is dynamic depending on choices of the Campaign Manager (like their choice of threshold # addresses in the slicer)
I have 4 tables reporting monthly sales of a product line; each table displays sales from a sdepcific channel (B2B, Ecommerce, Monobrand, Customized) and I'd like to stack these 4 tables one after the other.
Each table has the same exact columns, I can't do copy and paste because I need to update the 4 tables monthly.
I've tried to create a unique database and gather the information from the system throug a sequence of IF, IFS and SUMIFS but even though I get to the result, this takes forever in calculation
Is there a way to do it? I don't know, maybe with Powerquery or creating a Power Pivot?
Thank you
Load the tables into Power Query and append them in the Combine section of the Home tab.
I am trying to create a summary calculation on a set of tables which I have added to an Excel data model. I have 5 tables with the following columns:
Datetime (UTC)
Measured Data 1
Simulated Data 1
Measured Data 2
Simulated Data 2
etc.
I have created a master Date-time table which links these 5 tables together on their UTC date-time column.
My query is how to optimally create calculated fields on each of these tables without needing to explicitly specify the calculations on each individual table, as is the case with PivotTables (I need to select the specific measured and simulated data columns from one individual table in the data model). Instead I would like to be able to map all measured fields to one column and all simulated fields to another, and then use filters to select out the fields (or groups of fields) I want to compare.
I started by creating a summary table which lists all my tables in my data model by name along with the names of measured and simulated columns within each. However, I'm unsure what the next step should be. This seems like a pretty straightforward problem, but it has me stumped this morning. I'd appreciate any help. Let me know if I haven't fully explained anything.
I have a pivot table that displays agencies in rows, products in columns and sales units as values. I have to make up a report in this format:
Data is coming from an SQL Server Analysis Services and the "Estimate 2015" measure is a different measure (but uses the same dimensions and granularity so it is in fact possible to display the values side by side).
I could add a separate pivot table for the estimates, but then filtering or sorting the two tables will make them loose sync.
Is it possible to somehow align or combine pivot tables with different measures nicely?
if you are using Excel 2013, then you can use the data-model feature with standard pivot tables.
You need on your workbook:
3 dimension tables for agencies, products and year. All rows must contain distinct values (Agency A-C, Product A-B, Year 2013-2015)
2 fact tables: the one with historical data (from SSAS), the second one with your forecast datas
then you just create a Pivot table from any of the Dimension table. At this step, check the box "Add this data to the Data Model".
You then pull the Dimension field from the three dimension table on the Pivot column and rows. Choose both value fields from the 2 fact table (Quantity).
Excel detect missing relationships. You have to build them manually between the two Facts tables and the three dimensions (total 6 relationships)
In this Excel-file you can see an example with your sample datas.
You can find on the web some step-by-step explanations like How to Build PivotTables Using Excel's Data Model Feature
I am having a huge database of records and I'm finding it to be a nightmare getting to analyse the data.
Objective:
Group my data by Country of Purchase (rows), by Years/Months (rows), by Product (columns) with the Sum of Paid amount being the value.
Let me explain:
Below is a sample excerpt from my table.
And here is the result that I am looking for that I was able to achieve using an Excel Pivot table:
Why use MS Access:
My table has over 3 million records stored across many workbooks, and Excel has a limit of 1m in each sheet. Also Excel crashes more often than not when loading >500k of data.
I installed an older version of MS Access (2010) which has pivot tables option but it was very slow and did not allow me to group correctly. I then tried using a combination of queries and reports to arrive to my result to no avail.
Any help will be very welcome :)
How about doing the aggregation in Access and then the pivot in Excel?
SELECT country, year, month, product, sum(paid)
FROM myTable
GROUP BY country, year, month, product
(year and month based on access functions for date manipulation... alternatively, you could use is as a date to keep date functionality in the pivot - just make it the first of the relevant month)
Then use this as the source of the pivot table. The pivot table then basically just does the formatting - which it can hopefully do quickly enough