Succeeding with Analytics: Mastering People, Process, and Technology

Succeeding with AnalyticsMastering People, Process, and Technology

Wayne W. EckersonPrincipal Consultant, Eckerson Group Dr. Rado KotorovChief Innovation Officer, Information Builders

© Eckerson Group LLC 2

What’s in a Name?Data

Warehousing(Early 1990s)

Business Intelligence(Early 2000s)

Performance Management(Mid 2000s)

Big Data/ Analytics(2010s)


What’s in a Name?

DATA INSIGHTS ACTION

Data Warehousing(Early 1990s)





What’s in a Name?

DATA INSIGHTS ACTION

DATA ACQUISITIONETL, data modeling, data quality, data warehousing

IT ENVIRONMENT

Reports, analysis, dashboards, predictive analytics

DATA DELIVERY

BUSINESS ENVIRONMENT

Data Warehousing(Early 1990s)




© Eckerson Group LLC

• Understand the past– “Which campaigns provided

the best return?”• Optimize the present

– “Which packaging provides the best lift in sales?”

• Predict the future – “Which customers are likely to

respond to this offer?”

Why Analytics?


• Right people• Right tools• Right architecture• Right organization

Analytics Success Factors


Right People

CONSUMERS

• Data Consumer• Data Explorer• Data Analyst• Data Scientist• Statistician

PRODUCERS

• Report Analyst • Data Engineer• Business Developer• App Developer


TOOLSData

ConsumerData

ExplorerReport Analyst

Data Analyst

Data Scientist

Data Enginee

r

KPI dashboards

Ad hoc reports

Report authoring

Ad hoc discovery

Predictive modeling

Data development

Right Tools

• KPI dashboards = Interactive KPI reporting; locked down data• Ad hoc reports = Modifiable reports that expose data model to data explorers or search BI• Report authoring = Create reports from scratch from semantic layer • Visual discovery = Connect to data sources, mash the data, and visualize results• Predictive modeling = Data science tools• Data development = SQL, ETL and other tools

Lines of Support

Top Down

Bottom up

Casual Users Power Users

Expo

se o

n De

man

d


Source Data

(Relational and NoSQL, internal,

external, devices)

On Premise, Cloud, or Hybrid

Data Hub

(Integrated Tables)

MoveStream

FlattenLink

Business Views

(Subject-specific, physical or

virtual models)

Landing Areas

(Raw Tables)

Analytic Applications

(Reports, dashboard, embedded analytics, custom applications)

Data Consumer (50%)

Poin

t and

clic

kSe

arch

Data Explorer (40%)Dr

ag/D

rop/

Edit

Ad h

oc re

port

s

DesignConform

CreateRefine

Data Analyst (8%)

Conn

ect,

disc

over

,m

ash,

visu

alize

Data Scientist (2%)

Conn

ect,

prep

are,

mod

el, s

core

RDBMS, Hadoop, Spark, Cloud PaaS

Systems Analysts

100/0

Data Engineers

80/20

Business Developers

30/70

Data Pipelining, DW Automation, Streaming

DB Views, DV tools APIs, SDKs, IDEs

With permission

With permissi

on

KEY:

Da

ta/B

usin

ess s

kills

ARCH

ITEC

TURE

DEVE

LOPE

RSBU

SIN

ESS

USE

RSTO

OLS Analytic Platforms, App Development

App Developers

20/80

Operationalized models

Data Mining Toolkit

Statistician

Top-Down: Silver ServiceBottom-up: Self ServiceKE

Y: %

=%

of B

usin

ess U

sers

Enterprise Governance Grassroots Governance

Operations

Data Catalog

Data Prep Data

Prep


Right Organization

Director/VPBI tools specialists

Data architectsETL developers

BA Team

Business Analytics Council

Executive Committee – Business unit sponsorsFacilitated by BA Head

Working Committee – Business Unit Analyst ManagersFacilitated by BA Head and Relationship Managers

Analytics Forum – Business unit analysts and data scientistsFacilitated by BI Relationship Managers

Analytics Dir/VPStatisticians/Data Scientists

Business UnitsAnalyst ManagersBusiness analystsReport analysts

Source System – Owners

- DBAs

BA Relationship Managers BA Liaisons

1 per unit 1 per system

40+ Years of Sustained InnovationMaking Money with Analytics

Dr. Rado KotorovChief Innovation Officer

Not Your Physical Assets; Data Is the Most Strategic AssetInfonomics is the new management science about how to manage data assets

Bitcoin is data!

From Data Management to Data Monetization

You maximize the business value of your information investments through complete information management and pervasive business intelligence and analytics.

IB Platform is Built for Data MonetizationThe methodology for data monetization is aligned with our technology

Decision ManagementApplications

Valu

eHigh

Low

Raw Data Integrated andEnriched Data

AnalyzedInformation

Repurpose Information forOther Revenue Generation Use Cases

Land ofOpportunities

Business Use of Information

IT Value-Added

Operational

Value-Added

Business

Value-Added

Customer

Value-Added

AnalyticalTools

IT Tools InfoApps™(Analytic Applications)

CustomerFacingInfoApps™

Data Assets Data Monetization

Managing The Data Value Chain

Where Do We See The Opportunities?

Opportunity 1: Operationalize AnalyticsWhen analytics is implemented, operationalization is never being considered

Only 22% of employees get BI 90% of BI projects fail to

produce ROI

The secret to success is in empowering the 78%

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Why are the 78% so important?

"In the study of organizations, the employee must be the focus of attention, for the success of the structure will be judged by his performance within it".

Herbert Simon, Nobel Prize winner for studies in decision-making in 1978

Money Are Made in Operations

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Insights create opportunities, but only operationalization turns insights into value

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Empower the Mechanics to Save Money

An InfoApp from Information Builders available in 14 different languages for 60,000+ users in 14,000 dealerships. Helps employees make on the job repair-or-replace decisions and save Ford $60 million per year.

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Opportunity 2: Big Data Leads to More CustomersPeople discuss only big data analytics, but what about getting more customers?

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Empowering 260,000 Small Advertisers

Yellow Pages provides advertisers information to measure the return on their advertising dollars and track the success of their campaigns:

Approximately 52 billon rows (nine TB) of raw data per day

Response rate 2 to 10 seconds

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Empowering 350,000 Merchants with Spend Analytics

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Opportunity 3: Empower the Consumer With AnalyticsWhy not consumerise and commoditize BI and analytics ?

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Billions of Documents Are Distributed to ConsumersEvery document is an opportunity to consumerise BI and analytics

20Billion

10Per

month

137Pages

71%No

HTML

10%Provide Linking

14%Navigatbe

PDF

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Static PDF Leaves the Consumer PowerlessAll static PDFs are a missed opportunity to consumerise BI and analytics

98%static

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PDF (No analytics) vs ADF (For In-document analytics)IB Analytical Document Format allows to put the analytics in every document

Each PDF file encapsulates:(1) a complete fixed-layout, (2) fonts & images(3) text, data, and charts(4) Requires a reader to render file

Each ADF file Encapsulates:(1) a complete fixed-layout, (2) fonts & images(3) text, data, and charts(4) Requires a reader to render file(4) Interactive Analytic Engine(5) Responsive design to fit any device

PDF (Portable Document Format) ADF (Analytical Document Format)

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Distributing 2 Million 401K Statements MonthlyHuge savings, high adoption, increased customer satisfaction

Principal Financial Group:A $2.80 to print, stuff, mail,

support17% switched to eStatements

within three months

http://bcove.me/i4jk4h4b

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Thank you

Succeeding with Analytics: Mastering People, Process, and Technology

Data & Analytics

Transcript of Succeeding with Analytics: Mastering People, Process, and Technology