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IT SPEND

THE CHALLENGE

You are a member of the Audit team and have been tasked to investigate how the company's IT department is performing relative to plan.

You have been given a dataset (to run the analysis, type "MS IT spend analysis" in the app,) that details spending across different dimensions.

Gartner Predicts IT Spending Will Plummet By $300 Billion In 2020 As CIOs  Slash Budgets

THE DEPARTMENT

Cost variance analysis

See aggregated cost variance or split variance between its components (volume, price, mix).

The IT department is significantly over budget.

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Marimekko Plot

See how a metric, here revenue, interacts along a pair of dimensions, here cost element group and cost element sub group.

About 15% of actual spend went into Shared Services, mostly for Outbound Allocations....

marimekko (11)

Multi-tier bar chart

See how a dimension - here Cost Element Group - plays out in the two periods on a metric - here amount.

...items barely mentioned in the plan.

multi_tier_bar (5)

multi_tier_bar (6)

Shared Services spending is mainly tied to Infrastructure,...

marimekko (12)

BY IT AREA DUMBBELL PLOT

Dumbbell Plot

See how a dimension - here cost element group - plays out in the two periods on a set of metrics - here amount.

...that went significantly over budget.

visualization (14)

The 139M Shared Services spend was tied to two VP and to Core Infrastructure spending.

marimekko (13)

The item was booked as an Adjustment....

marimekko (14)

...also significantly over budget.

multi_tier_bar (7)

Plan vs Actual timeline

See when Actuals beat Plan and vice versa.

The cost overruns vs plan seem to have occurred in April and from September onwards...

ac_vs_py (3)

...with actual spending on the item while none planned....

ac_vs_py

Waterfall chart format

Whenever possible, charts are built in accordance to the IBCS standard. Green is "good", red is "bad", white is "plan", grey is "previous period" and black is "actual".

..since the difference is due mainly to "new" purchasing combinations: combinations that exists as actual, but do not exist in the plan.

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WHAT CHANGED

Variable dimension variance - Sales

Variable dimension variance helps see the interaction between the change in sales across the different dimensions.

FIRST RESULT COMBINATION

1️⃣ Overall, Infrastructure spend grew by 160M, 64% Y-o-Y.

2️⃣ Internal, Regular, Labor Costs decreased by 35M...

3️⃣ ...while other Labor Costs increased by the same amount.

4️⃣ Other Miscellaneous Expenses fell by 28M.

5️⃣ Fixed Asset Depreciation also fell by 24M.

*️⃣ This leaves 18M balance tied to other issues.

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The 158M cost increase of row 1️⃣ is mainly made up of Core Infrastructure spend...

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...in Shared Services, Outbond allocations, tied to Sara Berg.

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The first row result of the first waterfall picks up the large 160M overspend in Infrastructure.

The other result rows pick up less significant facts such as a swap between a 34.6M fall of internal labor costs and an equivalent increase of other labor costs.

waterfall (17)

SECOND RESULT COMBINATION

See another angle

This second waterfall shows also the geographical dimension.

1️⃣ Overall, Infrastructure spend grew by 160M, 64% Y-o-Y.

2️⃣ Excluding Infrastructure, USA spend fell 41M.

3️⃣ Excluding Infrastructure & USA, Regular Salaries fell 10M.

4️⃣ Excluding Infrastructure & Regular Salaries, Europe spend increased by 14M.

5️⃣ Excluding Infrastructure, Latin America Labor costs increased by 2M.

*️⃣ This leaves zero balance tied to other issues.

image-20210427193419956

The first row result of the second waterfall also picks up the same large 160M overspend in Infrastructure.

Show small multiple plots

After setting the appropriate filters set the "Run" widget to "Variable dimension bridge". Set the "Choose dimension for small multiples" widget to brand. Hit the 🚀 Submit button.

The other result rows pick up interesting but less significant facts such as a 41M fall of non infrastructure costs in the USA.

waterfall (16)

THIRD RESULT COMBINATION

There has also been a 125M (result row 1️⃣) increase of Shared Services Cost, in the USA, tied to "long tail" items. 34M (result row 3️⃣) of the 158M infrastructure spend increase is due to Telephone expenses.

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Every dataset is different

This use case is based on a fake dataset. Test run the app with your data to confirm the advantages of the variable dimension variance approach in your specific use case.

The first row result of the third waterfall picks up a subset of the 160M infrastructure overspend, 128M in the USA for shared services outbound allocation.

The other result rows pick up interesting but less significant facts such as a 34M, 2000%, increase of other group infrastructure telephone costs.

waterfall (18)

FORTH RESULT COMBINATION

Finding patterns in data

'Mparanza uses variable dimension variance to help find patterns in the data that could be missed with a traditional slice and dice approach.

In the USA a 155M increase (result row 1️⃣) in Infrastructure spend was partially offset, as already noted, by 41M (result row 2️⃣ ) of other cost reductions. In Europe, a 13M decrease in Salaries (result row 3️⃣) was offset by a 20M increase (result row 4️⃣ ) in other expenses.

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Consistency of results

Alternative result sets are always consistent. The different sets of results combinations show pretty much the same elements from slightly different angles, potentially helping to unlock insights.

The forth waterfall picks up another subset of the 160M infrastructure overspend, the 155M allocated to the USA.

The other result rows pick up interesting but less significant facts such as a reduction of labor costs in Europe which is more than offset by an increase in other costs.

waterfall (19)

ANNEX - VARIANCE BY DIMENSION

The dataset contains nine dimension columns, of which six are hierarchical.

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BY REGION

North America is the largest Region and has a high percentage variance.

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In all Regions, New Volume is the driver of increased spending.

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North America cost overruns happened in April and from September onwards.

ac_vs_py (5)

BY COUNTRY

USA is the largest area, with costs 15% over Plan.

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In all Countries, New Volume is the driver of increased spending.

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USA cost overruns happened in April and from September onwards.

ac_vs_py (6)

BY IT AREA

Infrastructure IT Area is over Plan.

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In all IT Areas, new volume is the driver of increased spending.

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Infrastructure cost overruns happened especially in April and from September onwards.

ac_vs_py (8)

BY IT SUB AREA

Core Infrastructure Sub Area is well over plan.

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In all IT Sub Areas, new volume is the driver of increased spending.

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Core Infrastructure cost overruns happened in April and from September onwards.

ac_vs_py (9)

BY COST ELEMENT GROUP

Shared Services Cost Element Group is way over plan.

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In all Cost Element Groups, new volume is the driver of increased spending.

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Shared Services cost overruns happened in April and from September onwards.

ac_vs_py (10)

BY COST ELEMENT SUB GROUP

Outbound Allocations Cost Element Sub Group is way over plan.waterfall

In all Cost Element Groups, new volume is the driver of increased spending.

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Outbound Allocations cost overruns happened in April and from September onwards.

ac_vs_py (12)

BY COST ELEMENT NAME

"Aggregated lower value items" (the sum of the long tails) Cost Elements have the largest overspend vs plan and a very high percentage variance.

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In all Cost Elements, new volume is the driver of increased spending.

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Peak in Adjustments in April and from September.

ac_vs_py (14)

BY BUSINESS AREA

The Infrastructure Business Area is the largest source of variance vs plan.

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In all Business Areas, new volume is the driver of increased spending.

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Peak in Infrastructure in April and from September.

ac_vs_py (15)

BY VP

Most of the overspend vs plan is tied to one VP.

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For all VPs, new volume is the driver of increased spending.

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And Sara Berg it is.

ac_vs_py (17)