14
Analytics Maturity Model John A. De Goes @jdegoes, [email protected]

Analytics Maturity Model

Embed Size (px)

DESCRIPTION

Every company is at a different stage in leveraging analytics to improve their operational efficiency and product offerings. In this presentation, you will learn an analytics maturity model that companies can use to determine how far they are from the most successful analytical companies.

Citation preview

Page 1: Analytics Maturity Model

Analytics Maturity ModelJohn A. De Goes

@jdegoes, [email protected]

Page 2: Analytics Maturity Model

Agenda

• Preamble

• Dimension 1: Analytical Sophistication

• Dimension 2: Analytical Productization

• Dimension 3: Data Management

• The Analytics Maturity Model

• Limitations of the Model

• Survey

• Summary

Page 3: Analytics Maturity Model

Analytics Maturity Model Systematize analytics maturity

Preamble

Page 4: Analytics Maturity Model

Analytical Sophistication

•A0: Entity Analytics•A1: Descriptive Analytics•A2: Predictive Analytics•A3: Prescriptive Analytics

Page 5: Analytics Maturity Model

Entit

y An

alyt

ics

Des

crip

tive

Anal

ytic

s

Pred

ictiv

e An

alyt

ics

Insight Intelligence{ {

“How many customers do I

have?”

“How many widgets did I sell

last month?”

“Who is going to buy which widget,

and when?”

Pres

crip

tive

Anal

ytic

s

“How can I make prospects buy

widgets?”

Business Intelligence Business Analytics

Page 6: Analytics Maturity Model

Analytical Productization

•P0: Standard Interactive Tools•P1: Internal Analytical Apps•P2: External Analytical Apps•P3: Data Products

Page 7: Analytics Maturity Model

Stan

dard

Ana

lytic

al T

ools

Inte

rnal

Ana

lytic

al A

pps

Exte

rnal

Ana

lytic

al A

pps

Consumption Productization{ {

• MicroStrategy• Tableau• Cognos• SAS• R

• Field / POS apps• Interactive

dashboards

• Embedded analytics

• Embedded reporting

Dat

a Pr

oduc

ts

• Recommendations• Personalizations• Automated

decision-making

Enablement{

Page 8: Analytics Maturity Model

Data Management

•D0: Few Structured Sources•D1: Many Structured Sources•D2: Multi-Structured Sources•D3: Multi-Structured Sources + Metadata

Page 9: Analytics Maturity Model

Few

Str

uctu

red

Sour

ces

Man

y St

ruct

ured

Sou

rces

Mul

ti-St

ruct

ured

Sou

rces

Silos Data Management{ {

• Financial• Sales• Marketing

• CRM• Support• Multi-channel• IT• ERP

• Log files• Twitter• Emails• Apps• Clickstream

Mul

ti-St

ruct

ured

Sou

rces

+

Met

adat

a

• Provenance• Quality• Authorship• Ownership• Confidence

Data Integration{

Page 10: Analytics Maturity Model

AnalyticalSophistication

AnalyticalProductization

A0

A1

A2

A3

P0 P1 P2 P3

D0 D1 D2 D2

Leaders

Followers

Data Management

Page 11: Analytics Maturity Model

Where you should be?

Where you are

Limitations of the Model

Page 12: Analytics Maturity Model

Please take 5 - 10 minutes to fill out the following survey:

http://bit.ly/YHs9wm

Survey

Page 13: Analytics Maturity Model

Summary• Much written on analytics maturity but few attempts to systematize

results

• Analytics maturity is complicated but 3 dimensions stand out:• Analytical sophistication

• Analytical productization

• Data management

• Model successfully identifies well-recognized analytical leaders as being more mature

• Limitation: model is descriptive, not predictive or prescriptive

• Need to collect much more data to build predictive / prescriptive model