enterprise analytics strategy: taking business analytics to the user

Enterprise Analytics Strategy:

Taking Business Analytics to the User

Ruben Mancha, PhDAssistant Professor of Information Systems

Babson College

March 8, 2016

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Take-home message:

Taking business analytics to the user requires strategic planning and

action on technologies, processes, and key indicators.

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

The Purpose of Business Analytics

•Improve decision making

• Strategic insight• Product and service offerings• Operational efficiencies

•Enable new business models

Rationalization

Reengineering

Paradigm shift

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Business Analytics - Requirements•Data

• Volume, variety, velocity, and veracity• Internal and external to the enterprise (APIs)

•Models• Problem—data—models

•Technology• Landscape of analytics technologies

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Data

• Variety and velocity more important than volume to improve operations and decisions

• Not necessarily “big data”• Veracity is an issue (sampling)• Structured and unstructured• Challenge of making the decision of what to keep and what

to discard• Adequate and clean data is expensive to obtain and costly to

maintain and store long-term

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Models

• Models create value (foresight)• Models incorporate assumptions in the decision-making

process• Models are built in technology and business

environments

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Technology

• Data is stored on hardware• Data is managed by algorithms, which are constrained by the

technology• User interfaces with data through technology• Technology is used to collect data (e.g., IoT)

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Landscape of Analytics Technologies

(Fast evolving and incomplete…)

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Analytics Maturity – TechnologiesCo

mpe

titive

Adv

anta

ge

Reporting

Visualization (Dashboards)

Diagnostic

Prediction

Analytics Maturity

Prescription (Optimization)

CognitiveAnalytics

Foresight

DescriptionInsight

Hindsight

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Analytics Maturity – TechnologiesDisconnect between technical competencies and analytics solutions

• Data Scientists: 0.1%• Analysts: ~ 3%• Business User:97%

AutomationQlikView

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Analytics Maturity – Data Management

Pote

ntial

for C

ompe

titive

Adv

anta

ge*

Data Management Enabled Analytics Maturity

Structured Data Structured + Unstructured Data

RDBMS(SQL)

DW

Spread-mart

Online Transaction Processing

NoSQL

• Performance• Scalability• Cost per GB

Data Lakes

*GIGO

Parallel NoSQL +Analytics

Operations & Reporting

Analytics

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Analytics Maturity – Data Management

Stor

age

Perfo

rman

ce

Volume of Data

NoSQL Database

Relational Database

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Analytics Maturity – Data Management

XMLJSON

Application Program Interface

(API)

NoSQL Frameworks Parallel Data Processing and Distributed File Systems with Analytics

Platforms

HD File System

IaaS

Analytics as a Service

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Yes to Business Analytics, but how?

Key Performance

Indicators (KPIs)

Business Processes

Business Goals

Enterprise Strategy

Business Analytics Strategy

Data

Models

Technology

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

“Analytics technologies are useless. They can only give answers”

Adapted from Pablo Picasso

“If you can’t measure it, you can’t manage it.”

“People don’t want to buy a quarter-inch drill. They want a quarter-inch hole”

Theodore Levitt

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

The Last Mile of Business Analytics Strategy

We have business goals, we have identified relevant data, and we have formulated appropriate models; how do we realize value?

•The last mile of business analytics transformation requires the alignment of goals, data, and models with business processes, technology and key performance indicators

•Complementary assets must be in place

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

The Last Mile of Business Analytics Strategy

- Goals- Data- Models

- Business Processes- Technology- Key Performance Indicators

Network of Complementary Assets:

- Organizational culture and structures

- Governance, security and ethics

- Analytics acumen (digital innovators)

- Skills: technical, communication, etc.

- Infrastructure

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

The Last Mile of Business Analytics Strategy

Key Performance Indicators

(KPIs)

Business Processes

Business Goals

Data

Models

Technology

Business Analytics Last Mile:

“Analytics User Domain”

© 2016 RUBEN MANCHA – ALL RIGHTS RESERVED

Thank you.

@RubenMMancha