1. Building Big Data Analytics as a
Strategic Capability in Industrial Firms
Firm Level Capabilities and Project Level Practices
for Digital Transformation
Dijo Alexander, PhD
Head of Technology – eLearning, SAP
Management Design & Innovation Research Fellow, Case Western Reserve University
Adjunct Faculty, University of Wisconsin - Milwaukee

2. Motivation.
“Data is the new oil, and analytics is the combustion engine.”
Peter Sondergaard, Gartner
“Data is the new science. Big Data holds the answers.”
Pat Gelsinger, VMWare
“Software is eating the world.”
Marc Andreessen, Andreessen Horowitz

3. “Almost 60% of big data initiatives fail.”
Motivation.
“Data is the new oil, and analytics is the combustion engine.”
Peter Sondergaard, Gartner
“Data is the new science. Big Data holds the answers.”
Pat Gelsinger, VMWare
“Software is eating the world.”
Marc Andreessen, Andreessen Horowitz
“Almost 60% of big data initiatives fail.”
--Gartner, November 2016

4. The failure rate is closer to 85%.”
Motivation.
“Data is the new oil, and analytics is the combustion engine.”
Peter Sondergaard, Gartner
“Data is the new science. Big Data holds the answers.”
Pat Gelsinger, VMWare
“Software is eating the world.”
Marc Andreessen, Andreessen Horowitz
“We were too conservative.
The failure rate is closer to 85%.”
--Nick Heudecker, Gartner, November 9, 2017

5. 6 6 % + + 47 B Problem of Practice.% +
2 out of 3 US Companies have invested in Big Data
US$ 47B Total Investments in 2017
Uneven Returns from Big Data Investments
47 B +
Tech Startups
Source: Gartner (2014); Bughin et al. (2018); Kane et al. (2017); Kelly (2014); Manyika, et al. (2016); Ransbotham (2017)

6. Opportunity.
Scale
Speed
Specificity
Automation
Big Data

7. Challenges. Management Commitment Data Domain Expertise
Technology & Resources
Beta Customers
Business Model

8. Management Commitment
Challenges.
Management Commitment
Data
Domain Expertise
Technology & Resources
Beta Customers
Business Model
Traditional Industrial Firms can Overcome Most of these.

9. Research Questions. Qualitative Study Quantitative Study
What are the factors that affect the success of big data analytics initiatives at firm level?
Qualitative Study
What extend do factors such as mobilization and market integration contribute to the success of big data analytics efforts at firm level?
Quantitative Study
How are project level big data practices established, identified, stabilized, distributed and integrated across the firm?
How do traditional industrial firms reorganize and transform themselves to successfully exploit the emerging opportunities of big data analytics?

10. Theoretical Lens. Dynamic Capabilities Theory
Fusion of Business and Technology
Big Data Landscape is Evolving
Framework of Analysis:
Sense
Seize
Reconfigure
Sense: Path-creating Search (Ahuja & Katila, 2004)
Seize: Sensemaking for assimilating insights (Weick et al., 2005)
Reconfigure: Transform / Translate / Integrate into Action (Teece, 2007)
Dynamic
Capabilities
(Teece et al., 1997;
Eisenhardt & Martin, 2000)
Internal & Extemporal Absorptive Capacity (Zahra & George, 2002)

11. Big Data Landscape. Organizational Stability Dynamic Environment
Operational
Agility

12. Analytics Maturity. Analytics Competency Descriptive Predictive
Capability
Domain
Analytics Competency
Descriptive
Predictive
Prescriptive
Cognitive
Analytics Capability
Tools and Technology Centric
Methods and Techniques Centric
Customer and Use Case Centric
Domain Expertise Centric
Customer
Cognitive
Prescriptive
Method
Predictive
Tool
Descriptive
Maturity

13. Organizational Routines.
Integration
Experimentation
(Re)Orientation
Mobilization
Technology & Resources
Learning & Collaboration
Insights
Value Addition

14. Findings. Big Data Analytics is NOT a Technology problem
NOR an Information Technology (IT) implementation.

15. Findings. Rather, it is a company-wide business initiative.
Big Data Analytics is
NOT a Technology problem
NOR an Information Technology (IT) implementation.
Rather, it is a company-wide business initiative.

16. Findings. Organizations Utilize Analytics Projects Run Analytics
Organizational Practices Facilitate Operational Routines to Emerge
Agility and Speed of Organizational Change Moderate Analytics Success

17. Findings. Organizational/Strategic Level
Organizational culture of collaboration & learning
Knowledge management system
Business model innovation
Operational/Tactical Level
Agile project management
Experimentation & empowerment
Knowledge sharing & integration

18. Practice Recommendations.
Develop an evidence-based decision system
Less intuition, more experimentation
Facilitate organizational knowledge management and experiential learning
Learn, unlearn, relearn
Collaboration and mentoring
Be purposefully market-driven
Better customer experience
Solution-based business model

19. Future Research. Longitudinal study to monitor digital progression
Pair up quantitative and qualitative studies to extract deeper routine level factors
Game based experimental study on how operational practices are influenced by organizational capabilities.
Explore more into the business model challenges

20. Thank You!
Questions?