Architecting the evidence-based firm: technical challenges and organizational approaches

architecting the evidence-based firm

Global organizations are beset by expanding complexity bearing accompanying uncertainty. In the wake of the still-unfolding Global Financial Crisis, an awareness of the destabilizing repercussions of misaligned incentives across an increasingly interconnected world are emerging, whether the frame be the mortgage industry, as per the U.S. subprime mortgage meltdown; securities portfolio management, via reoccurring trading scandals; or food staples, in the case of the Chinese melamine supply chain scandal.


Managers are acutely aware that, despite strengthened risk protocols and structured processes, global organizations are often flying blind in environments of uncertainty and doubt, with little warning concerning lurking crises. Concurrently, a new awareness of the limitations of individual and group decision making are emerging: behavioral economics research has uncovered inbuilt decision biases which commonly short-circuit attempts to improve decision quality, even for the best prepared. In an attempt to strengthen controls, marshal complexity, and bolster against poor judgment, large-scale organizations are keenly interested in identifying and adopting improved organizational decision making processes.

Meanwhile, not committed to a glass-half-full mindset, a rising elite of forward-thinking firms are embracing concerted systems-driven business analytics decision making as a strategic value driver. Celebrated companies such as Wal-Mart, Amazon, Apple, Dell, FedEx, and Zara have ushered in a revolution in strategic supply chain analytics, deriving value from concerted computational forecasting and prediction. Recent research explores the foundations of analytics as a powerful competitive differentiator (Davenport, Harris et al. 2010; Kiron, Shockley et al. 2011; LaValle, Lesser et al. 2011). In the wake of these dramatic success stories, technical and methodological advancements associated with business analytics have taken root in forward-thinking organizations.

The success of business analytics as a value driver has spawned growing practitioner and academic interest. Key research themes frame notions of evidenced-based management, decision-driven organizations, and the concept of organizational analytics maturity levels. The rising trend towards analytics is an emergent phenomenon driven by a desire to not only survive, but to thrive among synergistic forces: advancing information and communication technologies, expanding datasets resulting from increased collection and storage capacities, globalization facilitated by growing communication and transport capacity, virtual companies woven together by partnerships and outsourced services, expanding supply chain and market complexities, and broadening stakeholder factors associated with sustainability.

The rising feasibility of advanced analytics is propelled by a complex of facilitating emerging methods and technologies. Popularized under the rubric ‘Big Data’, the drive towards deep analytics has been called a fourth scientific paradigm which utilizes advanced computational techniques to gain machine-brokered insight from massive datasets. The ability to find patterns and meaningful efficiencies in large datasets with the aid of computational analytics has proven its efficacy across operational, financial, and commercial domains for forward-thinking companies. However, these emerging technologies and methods put fresh demands upon organizational leadership: new decision processes must be married to organizational structure and culture to be truly viable.

Companies are eager to understand not only how they can adopt formal techniques to drive improved analytics, but, crucially, the organizational culture of structured decision making to empower successful implementations. Survey research has revealed that political and cultural organizational factors are the greatest challenge in adopting advanced analytics programs (LaValle, Hopkins et al. 2010; Kiron and Shockley 2011; LaValle, Lesser et al. 2011). Analytics culture is cited as being a complex of fact-driven leadership, expertise (tools and skills), and processes linking analytical insight to strategy and operational decision-making (Kiron, Shockley et al. 2011). Decision-making rights in organizations are commonly hardened by organizational politics, making the adoption of new technology-driven decision-making processes fraught with both tacit and overt resistance. In this context, there is a driving need for improved understandings of the specific mechanisms for facilitating efficient analytics-driven organizational decision-making.

A core overlap suggested in recent literature is the link between analytics organizational culture and the decision-driven firm. The notion of an analytics culture or an evidence-based organization draws direct reference to the internal processes for making decisions in an organization. This highlights the importance of proper organizational design, in particular incentives and decision rights, as a keystone in the deployment of successful analytics programs. Proper organizational culture and context creates a viable foundation for the logistical components of a business analytics program, namely advanced technologies, methodological procedures, and skilled experts (Kiron, Shockley et al. 2011). When speaking of analytics culture, we thus are asserting a notion of an organizational decision-making architecture or set of repeatable practices or patterns which guide formal decision making quality. There is currently a gap in research literature regarding the particular mechanisms through which analytics-mediated organizational decision making occurs. The proposed research agenda following advocates a structured organizational decision network analysis methodology which can be used to guide the implementation

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About SARK7

Scott Allen Mongeau (@SARK7), an INFORMS Certified Analytics Professional (CAP), is a researcher, lecturer, and consulting Data Scientist. Scott has over 30 years of project-focused experience in data analytics across a range of industries, including IT, biotech, pharma, materials, insurance, law enforcement, financial services, and start-ups. Scott is a part-time lecturer and PhD (abd) researcher at Nyenrode Business University on the topic of data science. He holds a Global Executive MBA (OneMBA) and Masters in Financial Management from Erasmus Rotterdam School of Management (RSM). He has a Certificate in Finance from University of California at Berkeley Extension, a MA in Communication from the University of Texas at Austin, and a Graduate Degree (GD) in Applied Information Systems Management from the Royal Melbourne Institute of Technology (RMIT). He holds a BPhil from Miami University of Ohio. Having lived and worked in a number of countries, Scott is a dual American and Dutch citizen. He may be contacted at: LinkedIn: Twitter: @sark7 Blog: Web: All posts are copyright © 2020 SARK7 All external materials utilized imply no ownership rights and are presented purely for educational purposes.

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