The document discusses some of the challenges business leaders face when adopting advanced analytics, including determining return on investment, trusting technology to make decisions, and managing organizational changes. It notes that ROI can come from both cost savings and revenue generation use cases, but there may be hidden project and lifecycle costs. Advanced analytics requires descriptive, predictive, and prescriptive models to build trust. Finally, the document compares centralizing analytics under a Chief Data Officer versus keeping functions decentralized, noting pros and cons of each approach including potential hidden costs of organizational changes or data management challenges.

1. Helping business leaders get over
their learning curve in advanced
analytics
Feb 2017
saidevulapalli

2. The Advanced Analytics Divide

3. What is Return on Investment and when ?
How can I trust technology to make decisions ?
What are the hidden costs ?
Business and organizational process changes ?

4. Return on Investment
Cost and Risk Mitigation Revenue Generation
Current Costs
Cost saving Use Cases
Predictive Models
Cost savings Model
Project and lifecycle costs*
50% - 300%
Norm
Revenue Use Cases
Predictive Models
Revenue Model
Project and lifecycle costs*
???
*Hidden costs

5. Trusting technology to make decisions
Descriptive Predictive Prescriptive
Level of Business Trust
Target Business Entities
Predictive
Model
Business
Intuition
Target Business Entities
Prescriptive
Model
Business
Intuition
Env Env

6. Business and Organizational structures
CDO
IT LOBLOB Corp
+ Higher quality Models
+ Economies of Scale
+ Data Ownership Boundaries
- Data Migration Projects*
- Org Changes*
*Hidden costs
IT LOBLOB Corp
+ Fewer Dependencies
+ Quicker Time to Insights
- Data Federation*
- Data Security*
- Data Ownership and Management*