Enterprise Analytics Strategy: Taking Business Analytics to the User
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Transcript of 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