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.
Using natural language, she asks aloud to her mobile Day by Day application about expected variance in operating profit by region.
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.
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!
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.
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.
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.
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.
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.
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.
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.
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.
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.
For T&E, expenses have started to exceed both budget and previous year’s numbers, starting around July/August.
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.
As for the “Salary & Wages” issue, starting in August we’ve exceeded both the budget and last year’s numbers.
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?
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.
Drilling into payroll data, overtime hours match the same July/August spike, where we see a trend up in the call center.
Simultaneously, the call center had a huge staff turnover in July and has had a hard time filling the open job positions.
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?
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.
Mission accomplished!
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:

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