## Data Analysis for Financial Management using DAX, Excel and Power BI

##### Description

Explore the powerful tools and techniques to sort, analyze and visualize your financial data. The learners will be introduced to DAX and Big Data formulas with hands-on exercises. They’ll also delve deep into analyzing and predicting the earnings and expenses using Excel and Power BI. The data analysis skills learnt here not only will make the learners proficient in financial data processing, but also arm them with the knowledge to make data-driven decisions. The original prompt: I need a project calculation monthly earnings and expenses in xls table

# Foundation of Excel for Financial Data Analysis

## Introduction

The core idea of this practical implementation revolves around managing, calculating, and predicting monthly earnings and expenses using Excel – a commonly used tool for financial analysis, Data Analysis Expressions (DAX), Power BI, and Big Data formulas.

This implementation can serve as a foundation for data-driven decision making in your financial dealings, and will be wholly tied with the direct application in Excel; more complex programmatic approaches will be presented in later parts of the project.

## First Steps: Structuring Your Data in Excel

We will start by organizing the data for easier analysis. Suppose you have two Excel sheets: one for Earnings and one for Expenses.

On the Earnings sheet: Create columns for

`Date`

,`Earnings Type`

(e.g., paycheck, additional income), and`Amount`

.An entry may look something like this:

`"01-12-2021", "Paycheck", 5000`

.On the Expenses sheet: Do the same as Earnings but instead use

`Expense Type`

(e.g., groceries, rent), and`Amount`

.An entry would look something like:

`"02-12-2021", "Rent", 1000`

.

## Monthly Earnings and Expenses Calculation

After we populate good amount of data, we'd want to have some calculation on monthly basis. We can achieve this using Excel's inbuilt `SUMIFS`

function.

### Monthly Earnings:

On a new sheet, `Monthly Earnings`

, create a table with `Month`

and `Earnings`

columns.

You would populate

`Month`

column with every month of the year.For the

`Earnings`

column, use the`SUMIFS`

formula in Excel to sum the earnings for each corresponding month.

The formula would look something like: `SUMIFS(Earnings!Amount, Earnings!Date, ">="&MonthStart, Earnings!Date, "<="&MonthEnd)`

where 'MonthStart' is the first day of the month and 'MonthEnd' is the last day.

### Monthly Expenses:

The process is similar to what we did for Earnings.

Create another sheet `Monthly Expenses`

and do the same process with `Expenses`

data.

The `SUMIFS`

formula would be something like: `SUMIFS(Expenses!Amount, Expenses!Date, ">="&MonthStart, Expenses!Date, "<="&MonthEnd)`

## Future Predictions

To predict future earnings or expenses, we can use Excel's `FORECAST`

function, which predicts future values using linear regression.

On a new sheet, `Predictions`

, you can forecast earnings as:

`FORECAST(TargetDate, MonthlyEarnings!Earnings, MonthlyEarnings!Date)`

And similarly for`Expenses`

.

With these formulas, you can start to manage, calculate, and predict your monthly earnings and expenses on Excel using a data-driven approach. You can scale this model by adding more types of earnings and expenses and by including more parameters in your predictions.

This, in turn, forms a strong foundation for falling back upon as we move ahead with DAX, Power BI, and Big Data formulas which will allow us to further elaborate upon our analysis.

## Conclusion

Excel provides us with a robust set of features for performing financial data analysis. By understanding and making effective use of these features, we are able to lay a solid foundation to handle more complex scenarios using tools like DAX and Power BI.

# Introduction to DAX for Financial Calculations

DAX (Data Analysis Expressions) is a powerful formula language for Power BI, Analysis Services, and Power Pivot in Excel. It's a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values.

## Getting Started with DAX

We can initiate our financial calculations with DAX by creating calculated columns and measures. These are the base upon which we will perform our financial calculations.

` Revenue_Column = Table[Units_Sold] * Table[Unit_Price]`

In the above statement, we created a new column `Revenue_Column`

that multiplies each row’s `Units_Sold`

by its `Unit_Price`

. This way, the column now holds the revenue for each specific sale.

## Advanced DAX: Time Intelligence Functions

Time Intelligence is one of the most powerful concepts in DAX. For doing financial calculations, these functions prove to be extremely helpful. Let's take an example of calculating the Month-To-Date (MTD) Sales.

` MTD_Sales = CALCULATE(SUM(Table[Sales]), MTD(Table[Date]))`

In this example, SUM is a commonly used aggregation function and CALCULATE is a key DAX function that alters the context under which its expression is evaluated. It takes in a measure or an expression and one or more filters. The MTD is being used to evaluate the total sum of sales until the current month.

## Using DAX to Predict Monthly Earnings and Expenses

DAX has an in-built function named `FORECAST.LINEAR`

which uses a linear regression to predict a future value on the basis of existing values. It can be used to predict earnings or expenses for future months.

` Projected_Earnings = FORECAST.LINEAR(NEXTMONTH(Table[Date]), Table[Sales], Table[Date])`

In the statement above, we’re using `FORECAST.LINEAR`

to predict sales for the next month on the basis of the date and current sales.

Remember, a common use case for DAX is financial metrics such as Year to Date (YTD), Quarter to Date (QTD), Month to Date (MTD), and so on. But with the same fundamentals, you can forecast other financial metrics.

` Projected_Expenses = FORECAST.LINEAR(NEXTMONTH(Table[Date]), Table[Expenses], Table[Date])`

Here we’re predicting the upcoming expenses with the help of the previous ones.

# Summary

DAX provides a comprehensive toolset for performing financial calculations with relative ease. With its powerful Time Intelligence functions and inbuilt statistical functions such as FORECAST.LINEAR, it becomes highly convenient to manage, calculate, and even predict financial occurrences. A sound understanding of DAX can prove to be a very valuable skill in performing financial analysis leveraging Power BI, Excel, or any other tool that supports DAX.

Please remember that the field names and table names in the examples I used are simply placeholders. You should replace them with your own field names and table names.

Note that while DAX is a powerful tool, it has its limitations and might not always be the ideal tool for heavy financial forecast modeling. Instead, it is best utilized for analyzing, aggregating, comparing, and classifying data that's already known or reasonably estimated.

# Practical Implementation of Big Data Formulas in Financial Data Analysis

Here, we will discuss the practical application of big data formulas to manage, calculate and predict monthly earnings and expenses. We will focus on the usage of Data Analysis Expressions (DAX) formulas, Excel, Power BI and Big Data mathematical functions and algorithms.

## Using DAX for Advanced Financial Calculations

To start with, let's look at some advanced calculation using DAX:

**Yearly Profit Calculation:**DAX offers the`SUMX`

function to iterate over a table and sum the expression or data specified. To check overall yearly profits, the logic formula should look something like this:`Yearly Profit := SUMX( FILTER(Earnings, YEAR(Earnings[Date]) == YEAR(TODAY())), Earnings[Amount] - Expenses[Amount] )`

Here, we iterate over entries in 'Earnings' where the year of the transaction is this year. For each such entry, the profit is calculated, and then all these profits are summed.

**Monthly Expense Prediction:**Use the`FORECAST`

function to predict annual expenses based off of existing data, using linear regression.`Monthly Expense Prediction := FORECAST( MONTH(TODAY()), FILTER(Expenses, YEAR(Expenses[Date]) == YEAR(TODAY())), Expenses[Amount] )`

In this scenario, we are predicting the expenses for the current month. 'Expenses' where the year of the transaction is this year are being used to make the forecast.

## Excel Formulas for Financial Data Analysis

Excel also offers a wide variety of financial functions:

**CUMPRINC function for Principal Payment over time:**This function is used to calculate the cumulative principal paid on a loan between start and end period.`=CUMPRINC(rate, nper, pv, start_period, end_period, type)`

Where:

- rate - Interest rate
- nper - total number of payment periods
- pv - present value or total amount that a series of future payments is worth now
- start_period - the first period in the calculation
- end_period - the last period in the calculation
- type - when payments are due (0 = end of period, 1 = start of period).

**CUMIPMT function for Interest Payment over time:**This function returns the cumulative interest paid on a loan between start and end period.`=CUMIPMT(rate, nper, pv, start_period, end_period, type)`

**Note:** These formulas assume that the period of the loan is divided equally into payment periods and that payments are due at period ends.

## Big Data formulas in Power BI for Analysis

**Rolling Average Calculation:**This expression gives a rolling average over a specific period. This can be used to smooth out short-term fluctuations and highlight long-term trends.`Rolling Average:= AVERAGEX ( DATESINPERIOD ( Calendar[Date], MAX ( Calendar[Date] ), -3, MONTH), CALCULATE ( SUM ( Sales[Revenue] ) ) )`

**Year-to-Date Calculation:**YTD calculations are often used in finance and economics to compare data from different time periods. YTD in DAX can be calculated as follows:`YTD Sales := CALCULATE ( SUM ( Sales[Revenue] ), DATESYTD ( 'Calendar'[Date] ) )`

Here, the DatesYTD function uses the 'Calendar'[Date] column and generates a list of dates from the beginning of the year to the last date available in the data.

**Note:** The above are just samples of how Big Data formulas can be used in Financial Data Analysis. The actual implementation can get complex depending on the specific requirements of your data and financial analysis.

# Visualizing Financial Data using Power BI

Having completed the foundation of Excel for Financial Data Analysis, Introduction to DAX for Financial Calculations, and application of Big Data formulas in Financial Data, the next focus will be on visualizing this data in a meaningful way using Power BI. Let's assume that we have a financial dataset with a list of transactions which includes columns like date, amount, category, etc.

## Loading Data into Power BI

Power BI supports a wide range of data sources including Excel, SQL Server, and Big Data. To load data, use the "Get Data" button in Power BI, from the navigation pane, select the correct source and import your dataset into Power BI.

`Click "Get Data" -> Choose DataSourceType -> Click "Connect" -> Navigate to FilePath -> Load Data`

## Data modeling

The raw data may not always be in a form ready to analyze, so you will need to prepare and transform the data. You can do this in Power Query Editor.

`Click "Edit Queries" -> Select Column -> Make Needed Transformations`

## Create Measures

Use the Data Analysis Expressions (DAX) to create new measures based on your existing data. For instance, you might want to have a calculated column to represent earnings and another column to represent expenses. Here's pseudocode to create the measures:

`Right Click On Table -> Click "New Measure" -> Input DAX Formula -> Press Enter `

Example of DAX formula for calculating Total Earning might be:

`Total Earnings = SUM('table'[Earnings])`

## Data Visualizations

After preparing the data and adding measures, it's time to start creating visualizations. Power BI provides numerous built-in visualizations like bar charts, scatter plots, or line graphs. Select the visualization that suits your needs and select the data fields accordingly.

`Click on viz type (e.g. bar chart) -> Drag and Drop Data Fields into Values and Axes`

For example, you could visualize a comparison of expenses and earnings per month. For this, you could select clustered column chart visualization. Drag 'Date' column in Axis and drag 'Earnings', 'Expenses' in values.

## Dashboard Interaction and Setup

Dashboards provide a consolidated view of data and visualizations. One powerful feature of Power BI is the ability to create interactions between different visualizations on the same report.

`Click "Format" -> Click "Edit Interactions" -> Set desired interaction`

You can also manage the visual impact by adjusting color, size, and other aesthetics.

## Publishing

Once the dashboard is ready, you can publish it. Following is the pseudocode for publishing.

`Click "File" -> Click "Publish" -> Select "Destination" -> Click "Publish"`

That's an overview of how you can prepare and visualize your financial data using Power BI. You can further optimize the analysis by adding drill-down features or by creating dashboard hierarchies to have an in-depth view of a particular aspect.

# Comprehending Financial Dynamics using DAX, Excel, and Power BI

## Getting Started

Let’s suppose you have a set of raw financial data, such as:

- Product sales, categorized by product type and region.
- Operational expenses, distributed per department.

These could be available as CSV files, or may come directly from a web API.

Having laid the foundation for Excel, DAX, Big Data formulas and Power BI, it is time to integrate all these skills in a single project.

## Pre-processing the data with Excel

Given that the data is raw, it may require pre-processing before loading it into Power BI, and Excel is a perfect tool for this.

**Load the data**into an Excel workbook.**Clean up**the data. Remove any unnecessary columns, check for missing values, or outliers, and handle them appropriately considering the dataset and project needs.

`=IF(ISBLANK(Cell),"N/A", Cell)`

**Normalize**the data. For instance, you may have product sales in different currencies, which would need to be converted to a common currency.

`=VLOOKUP(currency, conversionTable, 2, FALSE) * amount`

**Export**the cleaned datasets to CSV or Excel files for the next steps.

## Analyzing the Data with DAX

Now you are ready for some serious data analysis.

- Import the cleaned data into
**Power BI**. - Verify the
**relationships**between your tables. - Begin writing
**DAX formulas**.

DAX, with its powerful in-memory engine, is capable of handling large quantities of data and complex calculations efficiently.

Suppose we want to create a measure that calculates the total net revenue for each product, considering both the product sales and operational expenses.

`Total Net Revenue = SUM('Product Sales'[Revenue]) - SUM('Operational Expenses'[Expenses])`

## Advanced Formulas and Predictions

With Big Data formulas, it's possible to perform intricate computations. Here is an example in Excel of the linear regression formula that could be used to forecast future revenue:

`FORECAST.LINEAR(target_date, historical_data, timeline)`

## Data Visualization with Power BI

After generating the data and creating a formula for predictions, the final step is visualizing it. Import your updated data into Power BI and create various types of insightful visualizations.

Suppose you may want to view monthly net revenue trends over time:

- Drag 'Date' into the 'Axis' field.
- Drag 'Total Net Revenue' into the 'Values' field.
- Select 'Line Chart' from the Visualizations pane.

Or perhaps you wish to see the breakdown of product sales by region:

- Drag 'Region' into the 'Axis' field.
- Drag 'Product Sales' into the 'Values' field.
- Select 'Bar Chart' from the Visualizations pane.

Ultimately creating a **Dashboard** containing your most relevant data.

Employing DAX, Excel and Power BI for your financial data analysis needs will provide insightful visuals while maintaining flexibility and efficiency. Ultimately, assisting in comprehending financial dynamics and making data-driven decisions.