Using Business Analytics and Program Data to Drive Success...Using Business Analytics and Program...

transcript

Workshop

Tuesday | 4:45 pm | Veranda DE

Jason Ezratty, Managing PartnerBrightfield Strategies LLC

Using Business Analytics and Program Data to Drive Success

“I’ve Been Avoiding Math My Whole Life, Why Start Now?”

Casual Methods Quantitative Methods

Observation “headcount is higher than usual” “headcount is up 28% compared to this month last year,”

Understanding “those guys charge so much more” “those guys charge 46% more than competitors for like job titles”

Inference “we seem to pay more in TX than NY”

“there is no statistical difference between TX and NY pay rates”

Prediction “we are hoping for industry average levels of savings, ~10%”

“measures of rate variance suggest competitive bidding can save 16%”

Common Misuses of Statistics Paint Confusing Pictures

• “Lies, damn lies, & statistics”

• Round number syndrome

• Forests vs Trees

• Under interpretation

• Over interpretation

Understanding & Implementing Analytical Methods is Worth the Journey

I’m able to measure the benefits of my

decisions.

What’s my headcount?

Start at the Beginning, Most Mistakes Happen in Least Sophisticated Data Analysis Steps

• Only deal in trusted data sources

• Data must be comprehensive

• Data must be representative, current

• Know your data type~ Discrete (aka categorical)

~ Continuous

• Condition your data

• Understand how your data distributes across key variables

Sample Output Quality Depends on Quality of Sample

What’s the best way to sample this data?

Directed Plus Random Sampling

ConsultingJob Title

Popularity

Top 15% of managerswith greatest headcount inmost popular job titles

Additional randomsampling of 15%of managers

Manager’s Consultant Headcount

Data Types of Common CW Data Elements

• Number of rejected invoices: __________

• Payroller Pay Rates: __________

• Nonexempt Headcount: __________

• Mark-Up %: __________

• VMS Bug Count, by Severity: __________

• Avg Tenure by Location: __________

• Satisfaction Rating (1-5): __________

• Number of rejected invoices: Discrete

• Payroller Pay Rates: __________

• Mark-Up %: __________

• Payroller Pay Rates: Continuous

• Mark-Up %: __________

• Nonexempt Headcount: Discrete

• Mark-Up %: __________

• Mark-Up %: Continuous

• VMS Bug Count, by Severity: Discrete

• Avg Tenure by Location: Continuous

• Satisfaction Rating (1-5): Discrete

Satisfaction Survey Data - Discrete

1 2 3 4 5 1 2 3 4 5

The PivotTable: Microsoft’s Gift for Discrete Data Analysis

• Fast & simple way to analyze discrete data with “count” field summary type

• PivotCharts aid in rapid visualization of sub-groups

• (Great for continuous data, too)

When Dealing with Continuous Data, Remember: AVERAGES LIE.

AveragedRaw Data

Client-identified vs Recruited Average Bill Rate Per Job Title & Level

RECRUITED

even-value line

regression line

Scatter Plot Also Effective with Conditioned Data

Visualizing Central Tendency & Range of Distributions

Standard Deviation: Tastes Better than it Looks

Idealized Relationship Between Mean & Standard Deviation

Kurtosis is the Measure of “Spikiness”

Variance is Like the Bread of a Sandwich, Too Much and You Can’t Taste the Meat

Thick Doughy Mess Thin & Crispy

Relationship Between Average & VarianceGROUP A GROUP B GROUP C GROUP D GROUP E

1 2 3 4 52 3 4 5 53 4 5 5 54 5 5 5 55 5 5 5 56 6 6 6 67 6 6 6 68 7 6 6 69 8 7 6 6

10 9 8 7 6

mean 5.5 5.5 5.5 5.5 5.5

standard deviation 3.0 2.2 1.4 0.8 0.5standard deviation (%) 55% 40% 26% 15% 10%

min 1.0 2.0 3.0 4.0 5.0median 5.5 5.5 5.5 5.5 5.5

max 10.0 9.0 8.0 7.0 6.0

Bill Rate Data - Continuous

GROUP A1 GROUP B1 GROUP A2 GROUP B241$ 39$ 52$ 48$ 48$ 42$ 53$ 48$ 53$ 44$ 53$ 49$ 53$ 47$ 54$ 49$ 56$ 48$ 54$ 49$ 58$ 50$ 55$ 49$ 58$ 51$ 55$ 49$ 59$ 53$ 55$ 50$ 59$ 57$ 56$ 50$ 59$ 60$ 57$ 50$

mean 54.40$ 49.10$ 54.40$ 49.10$ standard deviation 5.93$ 6.54$ 1.51$ 0.74$

standard deviation (%) 11% 13% 3% 2%min 41.0 39.0 52.0 48.0

median 57.0 49.0 54.5 49.0max 59.0 60.0 57.0 50.0

NOISY DATA TIGHT DATA

Per Job Title: Bill Rate StDev vs Avg Bill Rate

COLOR CORRESPONDS TO SUPPLIERSCOLOR CORRESPONDS TO SUPPLIERS

Using Querying to Identify Misclassified SOW Workers with a Venn Diagram

greatest likelihoodof SOW misclassification

Beware of False Metrics

• Average Bill Rate per Location~ Actually saw this in a top VMS’s

• Total Number of Suppliers~ Fine as a point in fact but not great

as indicator of performance~ Better to look at supplier sufficiency

per skill per location

• Know your Headcount from your Spend~ Different situations call for either or

Any Other Questions?

…feel free to call/email me afterward to speak confidentially: jezratty@BrightfieldStrategies.com

212.448.1843

Using Business Analytics and Program Data to Drive Success...Using Business Analytics and Program...

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