analytics for the midmarket and smaller companies
DESCRIPTION
How to get started with analytics.TRANSCRIPT
A Guide for Small and Midsize Companies
Metric-X, LLC 2692 American Drive, Troy, Michigan 48083
www.metricx.com
Five Steps for Beating Your Competition With Analytics
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Contents
Encouragement
All You Need Is …
Levels of Analytical Capability
The Five Steps
How to Get Started
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Encouragement
Competing on analytics is not only for large enterprises
You can get started with a zero budget: – All you need is a data dump and Microsoft Excel
Much can be achieved with little knowledge of statistics or computer programming
The analytics capability is built gradually by testing theories and gaining knowledge about your data
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All You Need Is …
Three components are needed to compete on analytics:
– Skills – Tools – Data
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Levels of Analytics Capability
We define four levels of capability
Moving to next level involves investment in skills, tools and data organization
Each level involves exploration until a ceiling is reached
Level 3:
Sweet
Spot
Level 2:
Basic
Level 4:
Formidable
Level 1:
Starting
Point
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Levels of Analytics Capability
Level Characteristics
Level 1: Starting Point
• Excel and an extract from the accounting or other system • Use Excel pivot tables to understand data • Ceiling is reached when data from multiple sources needs to be combined
Level 2: Basic • Data from multiple sources is combined by using Excel formulas or simple SQL • Pivot tables or a visualization tool is used • Analytical capability is concentrated among few power users • Ceiling reached when preparing data for analysis becomes complex or time-consuming
Level 3: Sweet Spot • Data is managed in a database system, and is well-designed for analysis • Fresh data is added daily or more frequently • Data is cleaned and transformed to be usable by non-programmers • Visualization tools are widely used, and reports and dashboards can be published to
other users • Ceiling is reached when the team lacks the statistical knowledge or tools to build models
Level 4: Formidable • Proprietary models are developed that enable continuous improvement and prediction • Highly skilled “data scientists” have access to r
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Levels of Analytics Capability
Level Characteristics Ceiling
Level 1: Starting Point
• Excel and an extract from the accounting or other system • Use Excel pivot tables to understand data
• Ceiling is reached when data from multiple sources needs to be combined
Level 2: Basic • Data from multiple sources is combined by using Excel formulas or simple SQL
• Pivot tables or a visualization tool is used • Analytical capability is concentrated among few power users
• Ceiling reached when preparing data for analysis becomes complex or time-consuming
Level 3: Sweet Spot
• Data is managed in a database system, and is well-designed for analysis
• Fresh data is added daily or more frequently • Data is cleaned and transformed to be usable by non-programmers • Visualization tools are widely used, and reports and dashboards can
be published to other users
• Ceiling is reached when the team lacks the statistical knowledge or tools to build models
Level 4: Formidable
• Proprietary models are developed that enable continuous improvement and prediction
• Highly skilled “data scientists” have access to r
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Five Steps
Iterate at each level using these five steps
Eventually, limitations of data, tools or skills will be realized
Invest in getting to the next level when ceiling is reached
2. List Your
Questions
3. Gather the Data
1. Select a Topic
5. Review, Validate the Discoveries
4. Answer
Questions With Data
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Topic: Marketing Questions: • Which regions are we most profitable in? • How does customer satisfaction correlate with
profitability? • What is the ROI on our marketing campaigns? • Which market segment is the most loyal?
Topic: Sales Questions: • Is there a correlation between commissions earned
and profit generated? • Can we predict which customers will be
uncollectable? • What factors impact the length of the sales cycle? • What
Examples of Topics and Questions
Topic: Production Questions: • Which plants / shifts have the highest scrap rates? • What is the variance in quality by time of day? • How long does it take us to set up for a new order? • Do we order too much /too little raw material for
each production run?
Topic: Finance Questions: • What was our work in process (WIP) over the last
twelve months?
• Are we paying too much for express shipping
production parts?
• What is the cost of sales for product X?
• How do discounts affect our profitability?
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Selecting a Topic
Decide which processes will give you a competitive advantage
Examples: – Customer Service – Job Costing? – Technician Productivity – Flexible Pricing – Advertising and Promotions
Identify the metrics that matter
Ensure data is available for analysis
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Excel Pivot Tables
Visualization Tools
Statistical Analysis Tools
Modeling, Simulation Tools
High
Complexity
Low
Complexity
Less Specialized
Analytical Skillset
(“Anybody”)
Highly Specialized
Analytical Skillset
(“Data Scientist”)
Skillset
To
ol C
om
ple
xit
y
Analytical Tools Complexity vs. Skillset
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Data Dump Into Excel
Multiple, Linked Excel Files or Queries
Departmental Data Warehouse
(Database tables with auto-refresh)
Enterprise Data Warehouse
(Multi-department, automated, high volume, “single source of truth”)
High
Invest-
ment
Low
Invest-
ment Less
Sophisticated
(manual, single
user)
Highly Sophisticated
(Automated, clean,
multi-user)
Data Organization
Tim
e &
Mo
ne
y
Data Organization Investment vs. Sophistication
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Modeling,
Simulation
Tools
Excel
Pivot
Tables
Data Dump
Into Excel
Enterprise Data
Warehouse
Data Organization
To
ols
Analytics Maturity
Companies Advance to Next Level After Hitting Ceiling
Statistical
Analysis
Tools
Visualization
Tools
Linked Excel
Files
Departmental
Data Warehouse
Level 1:
Starting
Point
Level 2:
Basic
Capability
Level 4:
Formidable
Analytical
Capability
Level 3:
Sweet Spot
(for Most Companies)
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Step 2: Understand Your Data
Start analyzing your data – Pull data into Excel and create pivot tables, cross tabs, summaries
Identify unknowns – What questions are you unable to answer? – Which metrics cannot be computed?
Define requirements – What targets should be set? – What information does your team need to monitor actual vs target? – What knowledge will give you an edge in your industry?
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Step 3: Organize, Clean Your Data “Flatten” the data
– Create common codes, descriptions regardless of system of origin: • Customer = Cust = Client = Account = Company • General Motors = GM = General Motors Corp • Ohio is in Midwest Territory? Central Region? USA? Americas?
– Make data easy to fetch, avoiding lookups
Clean the Data – Deal with missing values, data entry errors, outliers
Create a Common Repository – Pull the data from multiple systems into a common database – Regularly refresh the data
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Step 4: Learn to Use Analytical Tools
Try several tools – Data exploration, visualization, drill-down, statistical etc.
Get trained, develop expertise in using one or more tools
Create analysis models – Reports, workbooks, models
Publish your findings, invite conversations from co-workers
Promote an analytical culture
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Step 5: Test Your Theories Put your theories on the table. Examples:
– Sales of part X spike in the winter – Our profit margins on service type Y is low – We lose jobs because our estimates are too high – Technician utilization on small jobs is too low and cuts into margins
Validate the theories with historical data
Identify missing information
Update business processes so that new data can be collected to test theories
