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The Intersection of Business Intelligence & CRM
From Data to Insights
David J. Rosenthal
President & CEO
Atidan
It takes more than just software to create truly intelligent
data mining and customer relationship management
solutions. Learn why BI and CRM solutions are the next-
generation technologies your clients are demanding, and
what steps you should take to sell these types of tools.
Why is This an Opportunity?
It is essential for businesses to make
quick decisions to maximize each
customer opportunity
Q: How many are
utilizing CRM today
for your own
business?
What is Important to Your Customers?!
• Drive higher levels of sales efficiency and
effectiveness
• Measure pipeline trends across the entire sales
cycle
• Analyze customers and prospects across
multiple dimensions and data sources
• Plan and track more effective marketing
campaigns and linked sales activities
• Increase demand generation, cross-sell and up-
sell opportunities
Simple, Insightful & Mobile
About Advanced Visual Analytics
• Dynamic data content
• Visual querying
• Multiple-dimension, linked
visualization
• Animated visualization
• Personalization
• Business-actionable alerts
Demo
Data & Decisions for Everyone
Convergence & Trends
BICRM
MobileBig
Data
SocialCloud
Empowerment Integration
One-to-one Marketing
Internationalization
Where is the Channel Opportunity?
From softwarestrategiesblog.com
Data From Every Device,
Transaction & Action
Parting Thoughts
• Careful planning
– Targeted industries & technology platforms
– Complement your existing solutions
• Small & Fast steps
– Quickly changing industry
• Partnerships
– We are ‘better together’
– Expert experience required
Appendix
Screen shots have been sourced from a variety of vendors.
All product and company names herein may be trademarks of their registered owners.
Why BI Can be Complex
1. Data sources: where the data comes from
2. Data rationalization – mapping apples to oranges.
This layer includes:
1. Virtualized data access
2. Extract, transform, load (ETL)
3. Business event processing
4. Complex event processing (CEP)
5. Text and natural language processing (NLP)
6. Data quality
7. Master data management (MDM)
8. Data governance enabling tools
9. Direct data source access
3. Derived data sources – where a single enterprise
data warehouse (EDW) may not be a practical
option:
1. Staging areas
2. Operational data store (ODS)
3. Data warehouse (DW)
4. Data marts
5. OLAP cubes
4. Analytical data virtualization or semantic layers
5. Data usage – what business users touch and feel:
1. Reports
2. Ad-hoc queries
3. OLAP
4. Dashboards
5. Exploration and discovery
6. Advanced and predictive analytics
7. Process analytics
8. Analytical performance management
6. Data delivery – where it all ends up:
1. Alerts
2. Actions
3. Portal
4. Collaboration
5. Mobile
6. Office apps
7. Other components that span all layers of the BI
reference architecture:
1. Integrated metadata
2. Information life-cycle management (ILM)
3. Enterprise content management (ECM)
4. “BI out of the box” applications
5. BI on BI
6. Embedded BI / BI services
7. Big data
