Lancet Data Sciences and Bluestem Brands · It can feel like team members are speaking a language that can be tricky to understand ... Bluestem Brands. Lancet Data Sciences • In

Lancet Data Sciences and Bluestem Brands Man vs. Analytics

Neil GunnBluestem Brands Inc.BI [email protected]

Jason ToddLancet Data SciencesPractice Leader & [email protected]

The Challenging Part of Analytics…

It can feel like team members are speaking a language that can be tricky to understand

via mathsyear7.wikispaces.comVia http://en.wikiquote.org/wiki/Talk:Computers

Agenda

• About Bluestem• About Lancet• A business centric approach

• Define• Organize• Match• Maintain

• Matchmaking• Advance analytical capabilities• Future

Consumable Analytics

Lancet Data Sciences

BI is in our DNA• Became a MSTR partner in

1997• Multiple Practices• Managed Services• 2013 MSTR Solutions

Provider of the Year• 2013 (#34)-Fastest growing

private companies in the Twin Cities

• 2013 Inc. 500|5000 list of the fastest-growing private companies in America


• Headquartered in suburban Minneapolis

• Parent company to three fast-growing, dynamic, multichannel retail brands.

• We provide a unique mix of retail and payment options for a diverse range of customers with a wide range of financial needs.

• Three campuses and more than 1000 employees

Bluestem Brands


• In 2010, Bluestem and Lancet collaborated on a BI Roadmap to transform our BI environment

• After implementation of a new Enterprise Data Warehouse, MicroStrategy was the final piece in the puzzle

Bluestem and Lancet – The Journey

• MicroStrategy Implemented in April 2012

100 Users less than a month after install

Utilized visual insight to gain momentum

Platform developed and administered by Lancet

Today there are now 300 users of MicroStrategyin several different departments across Bluestem

Consumable Analytics: A business focused style

Not just pushing buttons and configuring technical BI options

Define: Use case(s) & storyline(s)

Organize: Business questions into desired flows

Match: Business questions & Analytic methods

Maintain: The approach

Tunnel Vision A Data Science Approach

A business centric language

Consumable Analytics: Solving business problems

Type Question Examples

Historic What has happened?Sum, Avg, Rank, Min, Max, Counts

PredictiveWhat will happen if these trends continue?

Linear & Exponential Regression, Time Series

RelationAre these entitiesassociated?

Affinity

GroupingHow should I assemblethese items?

Clustering

ClassifyWhat’s the chance of this outcome?

Decision Tree, Logistic Regression

Putting analytics in a business focused language that engages users




From Whiteboard to Dashboard!

What questions am I trying to answer?

What actions will be taken from this dashboard?

Who is the audience?

What is the purpose of this dashboard?

How do I navigate here?



From Whiteboard to Dashboard: Prototyping

Lancet Mock-up Dashboard



From Whiteboard to Dashboard: The Final Product

Confidential – for live presentation only

Final Dashboard Presentation

Co

mp

etit

ive

Ad

van

tage

What’s the best that can happen?

What will happen next?

What if these trends continue?

Why is this happening?

What actions are needed?

Where exactly is the problem?

How many, how often, where?

What happened?

Sophistication of Intelligence

Optimization

Predictive Modeling

Forecasting/extrapolation

Statistical analysis

Business Intelligence

Competing on Analytics: The New Science of Winning, by Thomas H. Davenport and Jeanne G. Harris (Harvard Business School Press, March 2007).

Alerts

Query/drill down

Ad hoc reports

Standard reports


Consumable Analytics: Most have seen…

Consumable Analytics: Organizing business questions

Historic Questions• Year to Date?• Quarter to Date?• Month To Date?• Today?

What are my current sales?

Predictive Questions• Forecast this month?• Forecast this quarter?• Forecast this year?

• Solution with match the desired information flow

• Builds an information app vs unsynchronized dashboard

• Reduces dashboard build time

• Engages the user community


Analysis/Method

Logistic Regression

Time Series

Association

Decision Tree

Clustering

Rank

Moving Average

Regression (Linear & Exp.)

Which stores have a similar product sales pattern?

What is my sales forecast?

What are my cross-sell recommendations?

What’s my optimal investment choice?

What is my average defect rate every 10 days by location?

How do I rank against my peer in the last 30 days?

AlgorithmK-means


Consumable Analytics: Matching



Consumable Analytics: Create Personalized Expressions

• Expressions (rich features)– Formula– Level– Condition– Transformations– Properties

• Use Metrics Anywhere– Dashboards– Consolidations– Filters– Subtotals– Prompts– Custom Groups

• Deployable to – Web– Mobile– Office

• Common Metric Definitions!



Consumable Analytics: Association Use Case

What are my cross-sell recommendations?

Important Considerations!

• What is the time horizon?

• Are there any exclusions?

• What is the dimensionality?

• In a single shopping visit or over customer lifetime?

What products do customers commonly buy together?

Analysis/MethodAssociation



Consumable Analytics: Association Use Case

Lancet Mock-up Dashboard

• “Lots of fish in the sea” for matchmaking…• Can help to improve performance• Dynamic parameters• Lots of opportunity to utilize….

Basic

• Sum

• Count

• Average

• First

• Min

• Max

• Last

Comp. & Operators

• >

• <

• =

• Like

• Between

• +

• -

• ()

Date & Time

• Add Days

• Current Date

• Current Time

• Hour

• Minute

• Month

• Quarter

• Year

Financial

• IRR

• NPV

• PV

• PMT

• Rate

• FV

• MIRR

• Duration

Internal & String

• Apply Simple

• Apply Agg

• Banding

• Case

• Coalesce

• Concat

• Position

• Trim

Logical & Zero

• And

• If

• Not

• Or

• Is not null

• Null

• Null to zero

• Zero to null

Math & Stat.

• Int

• Round

• Slope

• Power

• Tan

• AvgDev

• Forecast

• Growth

• Intercept

OLAP & Rank

• Mov. Avg

• Mov. Count

• Run.Avg

• Run. Max

• Run. Min

• Ntile

• Rank

• Percentile


Consumable Analytics: Comprehensive Library

• “Parameterized” models• Accepted algorithms• View model details• Lots of opportunity to utilize….

Analysis Details Uses

Linear Regression Ordinary Least Squares Forecasting

Exponential Regression Ordinary Least Squares Forecasting

Time Series Exponential Smoothing Forecasting

Logistic Regression Iteratively Re-weighted Least Squares

Predicts Categories

Decision Tree CART algorithm Predicts Categories

Clustering K-means algorithm Grouping

Association Rules Apriori algorithm Affinity


Consumable Analytics: Solid data mining foundation

• Pass-thru functions• PMML• R Plug-In• GEO API’s• Lots of opportunity to utilize….


Consumable Analytics: A business centric approach



Consumable Analytics: Integrating Tools

• Limited to 4GB currently• 64 bit will be coming!



Consumable Analytics: Building Models

• It can be an iterative process…

• Baseline knowledge of data mining algorithms

• Metadata is critical for success

• Many visualization options to display the output

Create Dataset Deploy MaintainBuild

Analytical Lifecycle

Analyze options Analyze variables

Validate ModelCalibrate Model

PresentationReview as-needed

Build

DeployMonitor & Maintain

• Building is only the 1st step…• Monitor & Maintain is

sometimes forgotten• Schedule frequent

business reviews• On predictive metrics,

review your accuracy• Adjust model parameters

as needed to fit the situation in the field

Analytical Lifecycle

A Lasting Relationship


Consumable Analytics: Analytical lifecycle

Lancet Data SciencesConsumable Analytics: Monitor & Maintain

Enterprise Manager has a wealth of data waiting to be explored!

What are my most run reports?

Which reports or cubes run the

longest?

Are users experiencing

errors?

Who are your power users?

Should I look into my cube strategy?

Am I meeting my SLA’s?




Consumable Analytics: A business centric approach

Language: Chat Session gone awry A Data Science ApproachA business centric language

Via http://en.wikiquote.org/wiki/Talk:Computers







Consumable Analytics: Future

• Stats & Entertainment…?• A new kind of capital• Data Quality• Security, Risk, & Compliance• “Localization”• Cross Sell• Logistics

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Lancet Data Sciences and Bluestem Brands · It can feel like team members are speaking a language that can be tricky to understand ... Bluestem Brands. Lancet Data Sciences • In