Getting Started With Data Analytics

A Primer on Transforming Data into Action

MAY 25, 2017

What you will/will not get from today

The Cadence Group : Data Analytics

Won’t Get:

• PreCrime from “Minority Report”

• Magic ability to “sprinkle some of that big data on it” and get

“the right answers”

Will Get:

• A roadmap to developing an analytics program

• Three, brief case studies to learn from & build on

• CPE, Donuts, Bagels!

Discussion Topics

The Cadence Group : Data Analytics

• Big Data, Data Science, Analytics, Business Intelligence, Data Visualization –

what are we talking about?

• Challenges in building an analytics program

• The Cadence Analytical Framework – How we approach analytic projects

• Three case studies: gross margin analysis, mileage reimbursement, &

inventory holding costs

• Closing comments & questions

Here’s What We’re Talking About

The Cadence Group : Data Analytics

• Big Data – This is EVERYTHING (e.g. Facebook likes, Amazon

clicks, phone calls, credit card purchases, etc.)

• Data Science – Disaggregated data used in an exploratory way

• Analytics/Business Intelligence – interchangeable terms; take

raw, structured data and draw conclusions from it

• Data Visualization – taking data and telling an insightful story

Challenges Faced In Building an Analytics Program

The Cadence Group : Data Analytics

Bloomberg Businessweek Research Report – “The Current State of Business Analytics: Where Do We Go From Here?”

The Cadence Analytical Framework

Our Approach to Analytics

• Identify the Problem – What is the

question we’re trying to answer? Issue at

hand?

• Data Exploration – What can we get our

hands on? What shape is it in?

• Build the Model – What do we want to

measure? What are the

calculations/assumptions behind it?

• Evaluate & Present Results – How is it

to be presented? What is the

visualization? What happened and why?

The Cadence Group : Data Analytics

Identify the Problem

Data Exploration

Build the Model

Evaluate & Present Results

Case Study #1Gross Margin Analysis

• Identify the Problem – Company XYZ

approached with the question “We have an

accessories business, have no clue what’s

selling, what our margin is, can you help?”

• Data Exploration – Able to get our hands on

three data sets; sales, cost of goods sold, and

a child/parent hierarchy. Built out an Extract,

Transform, Load (ETL) process to merge into

a single data set.

• Build the Model – Developed an automated

process to calculate gross margins within the

source data.

• Evaluate and Present Results – Visualization

of how each accessory line’s margin

fluctuated quarter of quarter.

The Cadence Group : Data Analytics

Case Study #2Mileage Reimbursement

• Identify the Problem – Company ABC

approached with the question “It feels that

our mileage reimbursement requests have

been high, what do you think?”

• Data Exploration – Data point around

employees who had been assigned a car and

data point around employees who had

submitted a mileage reimbursement request.

• Build the Model – Who are the employees

who have been assigned a company car AND

submitted a mileage reimbursement request?

• Evaluate and Present Results – More text

than a visualization. Question behind the

data, why did these individuals submit a

request? Was their company car in the shop?

The Cadence Group : Data Analytics

Case Study #3Inventory Holding Costs

• Identify the Problem – Company XYZ

approached with the question “We feel like

there’s slow moving inventory which drives

up our holding costs, what are the outliers?”

• Data Exploration – Could not get inventory

levels/costs but were able to receive sales

numbers by SKU then filtered by color.

• Build the Model – Initially, the structure was

less defined but after data were presented,

we were able to draw conclusions based on

outlay.

• Evaluate and Present Results – Three

distinct tiers of product movement.

Suggestion was to have >40 on hand at all

times, 20 – 40 on two week notice, and <20

on an “an order” basis. Reduce the slow

moving colors in the warehouse, thereby

reduce holding costs.

The Cadence Group : Data Analytics

Closing Comments & Questions

The Cadence Group : Data Analytics

• Start with a high priority, high return project

• Find an ally on the business side, build the culture

• Have a good handle on the data

• Understand the story behind the numbers

• You’re opening the kimono, exercise restraint and due diligence

www.theCadenceGroup.com

http://www.linkedin.com/company/the-cadence-group

503.936.3276

kc@theCadenceGroup.com