Welcome finance pro!

Here’s the scenario: While traveling, the CFO gets an alert on her mobile highlighting an issue with operating profit for the year.

Next

Using natural language, she asks aloud to her mobile Day by Day application about expected variance in operating profit by region.

Next
Show me the profit variance by regions

The UK clearly stands out as the source of the negative profitability variance.

With the troubling negative profitability forecasts for the year in hands, the CFO tasks you, the lead finance analyst, to investigate and present a detailed action plan to tackle the problem at next week’s board meeting.

Start Quick Tour

Using Oracle Autonomous Data Warehouse and Oracle Analytics you will complete 4 objectives:

  • Discover how the CFO came to her conclusions
  • Load your Autonomous Database and prepare data for analysis
  • Analyze UK data to understand the situation
  • Dig deeper to discover the why behind the issues

You are now Autonomous. Let’s go!

Start Objective 1

Pulling out her tablet, the CFO quickly studies financial intelligence dashboards powered by Autonomous Data Warehouse using Oracle Analytics Cloud. Let’s look at revenue first.

Click Revenue widget

Key Insight
Revenue KPIs show the business will end the year below plan.

Next

Operating expenses also look like they’re trending up. Let’s zoom in.

Click Operating Expenses widget

Key Insight
Definitely looks like this is a big factor in the negative impact on profitability.

Next

The system recommends reviewing the end of year sales forecast to see if the business can close some of the upside deals and thereby meet the revenue target.

Next

Let’s take a look.

Click Sales Forecast tab

Key Insight
Analytics based on sales forecast data loaded into the finance datamart, powered by Autonomous Data Warehouse, reveal that the current Q4 revenue forecast will not meet FY budget. We’ll make a note that we need to discuss the reliability of the forecast and the possibility to close additional deals with sales management.

Next

Objective 1 Complete

Nice work!

Now that you understand the high level insights, let’s prepare all the data we need for further insights by combining multiple data sources in one place for fast and easy analysis.

Start Objective 2

Let’s start by loading all appropriate data spreadsheets that might help in our analysis.

Click Create button

Click Data Flow

First, let’s load financial data.

Click Financial data set

Click the + symbol to begin adding T&E and payroll data to the mix.

Click Add Data

Select both spreadsheets

Finally, click the + symbol to save this new combined data set.

Click Save Data

The new combined data set has now been saved.

Next

Objective 2 Complete

Nice work!

You’ve successfully uploaded a new dataset to Autonomous Data Warehouse and joined it with two others to create a new multi-dimensional dataset for your analysis.

Next, let’s analyze British data to understand the situation.

Start Objective 3

The UK finance dashboard reveals rising troublesome trends in “OPEX by Account Group”. Let’s examine this trend further.

Click graph

The graph reveals “Salary & Wages” and “Travel and Expense (T&E)” as key culprits.

Next

Let’s investigate if “OPEX Planning” reveals further details.

Click OPEX Planning

Across the board, we plainly see a negative trend in the last few months.

Next

For T&E, expenses have started to exceed both budget and previous year’s numbers, starting around July/August.

Next

Investigating “Out of Policy T&E” , hotel expenses spike around July/August and it continues to be a problem. Better make a note to alert the sales managers.

Next

As for the “Salary & Wages” issue, starting in August we’ve exceeded both the budget and last year’s numbers.

Next

From external payroll data, we find overtime pay started to increase from July at the same time as base salaries show a drop in August. This seems like too much of a coincidence. What could be the cause behind all this?

Next

Objective 3 Complete

Nice work!

Now that you have a better understanding of the situation, let’s dive deeper to discover the why behind the issues.

Start Objective 4

Drilling into payroll data, overtime hours match the same July/August spike, where we see a trend up in the call center.

Next

Simultaneously, the call center had a huge staff turnover in July and has had a hard time filling the open job positions.

Next

Combined data from various sources in the HCM system reveals many young, low salary employees have left the business around that same timeframe. But why are they leaving?

Next

A word cloud, using data collected from an employee survey, sheds light on why employees may be leaving.

Better share with the hiring managers and HR, who can brainstorm ideas to address retention and hiring issues.

Next

Mission accomplished!

Finance

Well done! Oracle Analytics Cloud has enabled us to understand the problem with operating profits from all angles. By loading and combining multiple data sources, you’ve diagnosed the root cause and you’re well prepared for a successful board room meeting to share a powerful storyboard of your analysis.

Next step:

This simulator is only the beginning. Uncover the full potential of Oracle Autonomous Data Warehouse by signing up for a free trial.

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