SUMMARIZE Y SUMMARIZECOLUMNS en DAX: Conoce las PRINCIPALES FUNCIONES DE AGRUPAMIENTO en POWER BI.

Functions for Grouping Data in DAX

Introduction to SUMMARIZE and SUMMARIZECOLUMNS

• The video introduces the key functions for grouping data in DAX: SUMMARIZE and SUMMARIZECOLUMNS, noting that both can potentially return the same results based on user preference.

Importance of Contextual Understanding

• It is recommended, though not mandatory, to understand row context, filter context, and context transition before diving into advanced DAX functions like CALCULATE and complex filters.

Overview of Function Definitions

• The presenter outlines the structure of the video: starting with definitions and syntax of SUMMARIZE, followed by simple examples such as calculating last month's sales.

Examples of Measures

• A simple measure example will be provided first, then an intermediate example calculating daily average amounts per customer, illustrating how learned contexts interact.

Advanced Use Cases

• The discussion will culminate in more complex measures, such as identifying the best daily customer. Additionally, there will be a focus on SUMMARIZECOLUMNS as a potential replacement for SUMMARIZE.

Comparing Group By Functionality

Performance Considerations

• The speaker mentions another grouping function called GROUP BY, which is generally slower than SUMMARIZE and SUMMARIZECOLUMNS. However, it may have special use cases worth exploring in a dedicated video.

Understanding SUMMARIZE Syntax

Basic Structure of SUMMARIZE

• The syntax for using SUMMARIZE is introduced: it requires a table as its first argument followed by columns used for grouping. New columns can also be created but should be approached cautiously.

Operations Performed by SUMMARIZE

• Key operations performed by SUMMARIZE:

• Grouping tables based on specified columns.

• Creating new columns.

• Generating subtotals (though this last operation is discouraged).

Practical Example Using Power BI

Creating Distinct Brand Table

• An example demonstrates creating a calculated table from product dimensions to show distinct brands using the functionality within Power BI.

Comparison with VALUES Function

• The presenter compares using VALUES versus SUMMARIZE, noting that while they may return similar values in some cases, only SUMMARIZE allows grouping across multiple columns (e.g., brand and color).

Advanced Features of SUMMARIZE

Adding Additional Columns Safely

• Users can add additional columns with specific measures (like sales amount), but caution is advised due to potential unexpected results when dealing with complex measures.

Complexity of Context Handling

• Emphasizes that SUMMARIZE generates both filter context and row context simultaneously. This complexity necessitates careful handling when adding extra columns or performing calculations.

Best Practices When Using DAX Functions

Recommended Approach for Adding Columns

Understanding Sales Data Analysis

Calculating the Last Sales Period

• The speaker emphasizes that using a specific method will yield consistent and reliable results, ensuring the desired output is returned.

• The speaker discusses identifying the last available sales data, noting that the most recent order date is November 4, 2019, indicating that November is the last month of sales.

• A calculated column for "order month" can be created to generate indicators; however, leveraging existing calendar dimensions is recommended for efficiency.

Utilizing Calendar Dimensions

• The use of the Max function with calendar periods returns a value (2019-12), but discrepancies arise since actual sales only occurred until November.

• It’s common for calendar dimensions to include more dates than those with recorded sales. This applies similarly to product dimensions where products may exist without being sold yet.

Creating Efficient Measures

• Instead of creating inefficient calculated columns in fact tables, measures can be developed to determine the last sales period effectively.

• By utilizing functions like Maxx and SUMMARIZE from fact tables while grouping by periods, accurate values for the latest sales period can be obtained.

Understanding Table Relationships

• The importance of understanding table relationships is highlighted; when using SUMMARIZE with fact tables, it incorporates all related dimension information as well.

• When creating calculated tables based on periods from either calendar or fact tables, different outcomes are produced depending on which table serves as the primary grouping source.

Advanced Grouping Techniques

• Using a calendar dimension allows for comprehensive grouping across all periods available in that dimension compared to just those with recorded sales in fact tables.

• The concept of an expanded table is introduced: it includes both original data and additional columns from related dimensions when performing calculations like SUMMARIZE.

Finalizing Sales Period Calculation

• With an understanding of how expanded tables work within calculations, groups can be generated based solely on actual sale periods rather than all possible dates.

• By applying MAXX with previously defined measures and incorporating them into reports, users can dynamically update their reporting based on current data trends.

Exploring Average Daily Sales Metrics

Understanding Daily Sales Metrics

Calculating Average Daily Sales per Customer

• The average daily sales amount is 107.3 soles, which can be calculated by dividing total sales by the number of customers.

• A new measure called "average daily sales per customer" will be created using the SUMMARIZE function to analyze this metric effectively.

• The average daily sales per customer is determined to be 49.5 soles; understanding how this value is derived involves examining the data model and its filters.

Importance of Filter Context in Data Analysis

• The filter context significantly impacts the data model; for instance, if filtered by January, only relevant records are considered despite having all historical data available.

• It's crucial to differentiate between having a complete dataset and an active filter context; even with all records present, an applied filter alters calculations.

Evaluating DAX Functions

• DAX functions evaluate from the inside out, meaning that nested functions like SUMMARIZE first process their inner components before returning results.

• The SUMMARIZE function generates a virtual table containing distinct combinations of customers and purchase dates based on existing relationships in the data model.

Understanding Row Context and Measures

• The ADDCOLUMNS function operates within a row context, allowing for virtual column creation during calculations.

• Each measure in DAX has an underlying calculation; when evaluating measures like "sales amount," it applies filters specific to each row being processed.

Aggregation Techniques in DAX

• After calculating individual values for each row, aggregation functions (like AVERAGEX or MAXX) summarize these results into final metrics such as averages or maximum values.

• An additional measure can be created to identify the maximum daily sales per customer using MAXX instead of ADDCOLUMNS.

Identifying Top Customers Using Variables

• To find out who achieved the highest single-day sale (84 soles), one can expand the matrix but also create a more efficient measure using variables in DAX.

• By defining variables within measures, such as storing maximum sale amounts and corresponding customer names, code readability and performance improve significantly.

Filtering Customers Based on Daily Purchase Amount

Creating a Filter for Top Customers

• The process begins by filtering customers based on their highest daily purchase amount, which is set to a predefined variable.

• The filter aims to identify customers who have made purchases equal to the specified amount (84), allowing for the identification of ties among top purchasers.

Returning Customer Names

• The technique allows for returning multiple customer names in case of ties, concatenating them with commas for clarity.

• This method can be applied broadly to determine various metrics such as best products, clients, stores, and categories within the top 15%.

Understanding Contextual Filters

• The model operates with two dimensions and a fact table; it applies filters contextually without altering the underlying data structure.

• Evaluating how variables are calculated starts from the innermost function outward, beginning with identifying all customers who made purchases.

Iterative Filtering Process

• The filtering process iterates through each row while evaluating whether each customer's maximum daily purchase meets the defined criteria (equal to 84).

• For example, Julio's maximum purchase does not meet this threshold; thus he is excluded from being labeled as the best customer.

Finalizing Results and Handling Ties

• After filtering through all customers, only Christopher meets the condition of having purchased exactly 84 on his best day.

• A final variable concatenates results into a single output. If there were multiple qualifying customers, they would be listed together.

Identifying Overall Best Customer

Distinguishing Between Daily and Monthly Performance

• Although Christopher was identified as the best customer for one day, overall monthly performance shows that Julio Mendoza had higher total sales across all days.

Simplifying Measures for Better Insights

• To find out who was the best customer overall, a new measure can be created that calculates total sales per client rather than focusing solely on daily peaks.

Modifying Existing Measures

Best Client Analysis

Overview of Client Performance

• The speaker discusses the performance of clients, noting that Julio Mendoza has surpassed Cristofer as the best client overall.

• A modified measure is introduced to analyze why Julio Mendoza ranks highest in purchases compared to others.

Calculation Methodology

• The new calculation method returns a total of 216 for the best client, differing from previous measures which returned 84.

• A virtual table is created to evaluate each client's purchase amount, identifying Julio Mendoza as the only one meeting the criteria for maximum sales.

Introduction to SUMMARIZECOLUMNS Function

Transition from SUMMARIZE

• The speaker introduces SUMMARIZECOLUMNS as a potential replacement for SUMMARIZE, highlighting its syntax differences.

• Unlike SUMMARIZE, where the first argument is a table, SUMMARIZECOLUMNS requires columns for grouping first, followed by a filter table.

Advantages of SUMMARIZECOLUMNS

• This function allows for creating new columns with expressions and subtotals without risk, making it more efficient than SUMMARIZE.

• It is noted that while SUMMARIZECOLUMNS is optimized and faster in calculations, it currently cannot be used in measures.

Limitations and Recommendations

Current Limitations of SUMMARIZECOLUMNS

• The speaker demonstrates how to modify an expression using SUMMARIZECOLUMNS, emphasizing its limitations when transitioning context.

• An error occurs when trying to use this function in certain contexts due to its inability to support context transition effectively.

Best Practices Moving Forward

• For now, it's recommended to continue using SUMMARIZE for measures until further optimizations are made on SUMMARIZECOLUMNS.