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

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.” 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

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

6 6 % + + 47 B Problem of Practice. 6 6 % + 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)

Opportunity. Scale Speed Specificity Automation Big Data

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

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

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?

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)

Big Data Landscape. Organizational Stability Dynamic Environment Operational Agility

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

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

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

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.

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

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

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

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

Thank You! Questions? dijo.alexander@sap.com