I know PowerPivot is not programming, but I wanted to see if I could get help or a recommendation on how to get the total rows to correctly calculate the sum of Absolute Values at a higher aggregation level that the detailed data in a PowerPivot data model.
Here is my example: This is detailed data format. There are 2 hierarchies:
1. Product Group\Product Family\**Material** '3 columns'
2. Region\**Plant**\SalesMgr\Employee\Customer '5 columns'
Hierarchy represents 8 columns in my data model
All data in the data model is at the lowest level (i.e. Material/Customer). However I want to calculate Forecast Accuracy at Material/Plant Level (a higher aggregation level).
DataModel Column9: Sales 'Sales at the detailed Material/Customer'
DataModel Column10: Forecast 'Forecast at the detailed Material/Customer'
Calculated Column11: AbsoluteError = ABS(Forecast - Sales) 'calced at Mat/Cust'
PowerPivot DAX formulas/measures, shortened for simplicity
TotalSales:=SUM(Sales)
TotalAbsError:=SUM(AbsoluteError)
MAPE:= TotalAbsError/TotalSales
ForecastAccuracy:= 1 - MAPE (Mean Absolute Percentage Error)
This calculates the data correctly at the Material/Plant level when I create my Pivot Table. However the Total values in the Pivot Table are incorrect.
For example, the Total of AbsError is the Sum of 20 rows of Material/Customer Combinations. I need the Total of only the 5 Material/Plant Combinations. The 20 rows of detailed Material/Customer data aggregates up to only 5 rows of Material/Plant Combinations.
I have a sample spreadsheet I can share. Thanks Dimitry for updating the question!
I would like to post the spreadsheet / powerpivot so that the StackOverflow community can see it. I assume I have to post a link. I will start trying to figure out how.
Thanks
David
Related
I am trying to come up with some arithmetic calculations for some survey data. I want to do these calculations for a number of segments and want to figure out how to do it without writing numerous SELECT statements.
This is what I have so far:
FACT table. This tables holds survey data at a respondent level - for example, if a survey had 10 questions, this table will have 11 columns: a column to identify the respondent_ID and 10 other columns to identify the responses to those questions.
DIMENSION table. This table segments we want to view the survey data by at a respondent level - for example, if we want to view survey responses by membership_status and age_bracket, this table will have 3 columns: a column to identify the respondent_ID, and two columns to identify membership_status and age_bracket.
OUTPUT.
I want to get aggregate calculations to summarizes the responses to the survey overall and to each question. I also want to be able to get this information for all possible segments that exist in the DIMENSIONS table.
I can do the query below, however I'll need to do this for every segment:
SELECT
COUNT(DISTINCT(CASE WHEN f.QUESTION_1 IN ('8', '9', '10') THEN f.RESPONDENT_ID END))*1.0 / COUNT(DISTINCT(CASE WHEN f.QUESTION_1 IS NOT NULL THEN f.RESPONDENT_ID END))*1.0 AS CSAT_1
FROM FACT f
JOIN DIMENSION d ON f.RESPONDENT_ID = d.RESPONDENT_ID
WHERE d.MEMBERSHIP_STATUS = 'ACTIVE'
The calculation above gives us something called a top 3 box. That is just one calculation, I will need to do many of them. Additionally, ever calculation will need to be done for each segment. In order to get a calculation for nonactive members, I would need to run another query and set d.MEMBERSHIP_STATUS = 'INACTIVE' and I would need to run another query with no filter, to get the overall calculation.
Is there a way I could store all my arithmetic calculations needed in my output as a function (maybe in a temp table or something) - my thought is that it'll be better to set the functions somewhere, and then when I need to calculate the output, I would some how call the function to do all the calculations I need, and give me all the calculations for every segment I have?
I can't fully envision how to get there, or if this is even a good solution, so guidance and detailed SQL code would be extremely helpful.Examples please!
I am stuck with this report pattern can anyone help me out how to deal with this situation? Here is what want to accomplish on SSRS
I have table
Units distribution
High 10 (10/30) -33%
low 20 (20/30) -66%
total 30
how can we use the total value of high, low rows to calculate distribution?
Sample Pic with formulas table and Data table
and here I want to achieve data table on right on the picture, so I want to implement the formulas table pattern on Data table using SSRS, I have already pulled the data.
Thank you
create a dataset that will return those rows as columns.. then display the table vertically (columns to rows) since the structure is fixed.
I am editing my question which I want exactly.
I have two columns Actual Units, Future Units from Fact A and Fact B respectively but at same granular level.I also have Demand Units from Fact B
My requirement is :
1. Projected Units = Coalesce(Actual Units,Future Units)
2. Stock Units = IF(Projected Units > Demand Units,Demand Units,Projected
Units)
3. Stock Rate = (Stock Units/Demand Units)
I cannot join the two facts in the data source view level and do the
calculation there because they are a very huge tables, so I think the
performance would be very slow. If you say that doing the calculations at
the data source view level level is the only way we have, please let me
know.
Did you get this?
When calculating the grand total MDX is summing up A, summing up B, and then comparing them.
If you want the calculation to occur at the row level (checking whether B>A) then edit the Data Source View and add a new calculated column to the table your measure group is based upon. The calculated column should be:
CASE WHEN B>A THEN A ELSE B END
Then create a Sum measure based upon that new column.
This approach will perform much better compared to any completely MDX approach to calculating this at a very detailed grain. If your fact tables had 500,000 rows or less and you had a degenerate Dimension which was the same grain as the grain you need to calculate at, we could possibly do it in MDX. But since you are concerned with SQL query performance I am assuming the tables are big. Just remember that SQL is done once at processing time. MDX is calculated in every query at query time. So do expensive things in SQL when you can.
DAX 2013 standalone power pivot.
I have a sales table with Product and Brand columns, and Sales measure which explicitly sums up sales column.
Task in hand: I need to create 1 measure RANK which would ...
if Product is filtered expressly, then return count of Products that have higher or equal sales amount, divided by total count of products.
If it's a subtotal brand level, show the same but for brands.
My current approach is using RANK and then MAXX of rank which seems working but a no-go - slow nightmare. Excel runs out of memory.
Research: it's been a week. This is the most relevant post i found anywhere, this question here , but it's in MDX.
In my example picture, I'm showing Excel formulas with which I can get to the result. Ideally there shouldn't be any helpers, 1 formula for all.
I.E.
RANK:=IF( HASONEFILTER(PRODUCTS[PRODUCT], HELPER_PROD, HELPER_BRAND)
where HELPER_PROD part would be something like this - need to find a way to refer to "current" result in pivot table like Excel does using [#[...:
HELPER_PROD:=COUNTX(ALL(PRODUCTS), [SALES]>=[#[SALES]]) / COUNTX(ALL(PRODUCTS))
HELPER_BRAND:=COUNTX(
DISTINCT(ALL(PRODUCTS[BRAND])),
[SALES]>=[#[SALES]]) /
COUNT(DISTINCT(ALL(PRODUCTS[BRAND]))
You can use the "Earlier" function to compare with the current record.
ProductsWithHigherSales:=CALCULATE(countrows(sales),
FILTER(all(Sales),
countrows(filter(Sales,Sales[Sales]<=EARLIER(Sales[Sales])))
))
Using Earlier function in measures: can-earlier-be-used-in-dax-measures
Used workbook: Excel File
I am trying to create a pivot table with data from a SQL database. The pivot table is basically a process capability report that requires certain information. I have created an Excel file that sort of does what we are trying to accomplish, but I don't think my calculations are quite accurate. I know after doing some research that there are Pivot Tables within SQL but I don't know how to get them to work.
The table that my data is stored in has thousands of records. Each record has the following information: DATE, TIME, PRODUCT_NO, SEQ, DATECODE, DATECODE_IDH, PRODUCT, LINE, SHIFT, SIZE, OPERATOR, SAMPLE_SIZE, WEIGHT (1-12), WEIGHTXR, LSL_WT, TAR_WT, USL_WT, LABEL, MAV, LINE_SIZE
For the report, I need to group the data based on product and by line. Since the product isn't consistent, each product can be described by TAR_WT. So the grouping will be a combination of TAR_WT and LINE_SIZE. I need to count how many instances of that product were measured which will be the number of measurements (each individual weight which is 12 weights per record). I also need to find the minimum, maximum, and average of all of the weights per product (again 12 weights for every record). After those values are obtained, I have to calculate the Standard Deviation, Cp, and Tz of the values (staistical calculations) and report all the information.
Date Time Product No Seq DateCode Internal DateCode&ProductNo Product Description Line Size Weight1 Weight2 Weight3 Weight4 WeightXR LSL_WT TAR_WT USL_WT LABEL MAV
8/3/11 0:37:54 1234567 23 DateCode Internal DateCode&ProductNo Product Description L-1A 50 1575 1566 1569.5 1575.5 1573.4 1550.809 1574.623 1598.437 1564.623 1525.507 L-1A_50