Storytelling, business analytics and big data interpretation

2019 ◽  
Vol 43 (2) ◽  
pp. 204-222 ◽  
Author(s):  
Valeriia Boldosova ◽  
Severi Luoto
Keyword(s):  
Decision Making ◽  
Data Interpretation ◽  
Daily Basis ◽  
Data Driven ◽  
Complex Data ◽  
Business Analytics ◽  
Content Type ◽  
Individual Level ◽  
Business Practitioners ◽  
Data Driven Decisions

Purpose The purpose of this paper is to explore the role of storytelling in data interpretation, decision-making and individual-level adoption of business analytics (BA). Design/methodology/approach Existing theory is extended by introducing the concept of BA data-driven storytelling and by synthesizing insights from BA, storytelling, behavioral research, linguistics, psychology and neuroscience. Using theory-building methodology, a model with propositions is introduced to demonstrate the relationship between storytelling, data interpretation quality, decision-making quality, intention to use BA and actual BA use. Findings BA data-driven storytelling is a narrative sensemaking heuristic positively influencing human behavior towards BA use. Organizations deliberately disseminating BA data-driven stories can improve the quality of individual data interpretation and decision-making, resulting in increased individual utilization of BA on a daily basis. Research limitations/implications To acquire a deeper understanding of BA data-driven storytelling in behavioral operational research (BOR), future studies should test the theoretical model of this study and focus on exploring the complexity and diversity in individual attitudes toward BA. Practical implications This study provides practical guidance for business practitioners who struggle with interpreting vast amounts of complex data, making data-driven decisions and incorporating BA into daily operations. Originality/value This cross-disciplinary study develops existing BOR, storytelling and BA literature by showing how a novel BA data-driven storytelling approach can facilitate BA adoption in organizations.

scholarly journals How do top- and bottom-performing companies differ in using business analytics?

2017 ◽  
Vol 30 (6) ◽  
pp. 874-892 ◽  
Author(s):  
Guangming Cao ◽  
Yanqing Duan
Keyword(s):  
Organizational Performance ◽  
Business Processes ◽  
Academic Research ◽  
Data Driven ◽  
Business Analytics ◽  
Analytical Strategy ◽  
Content Type ◽  
And Performance ◽  
Data Driven Decisions ◽  
Effective Use

Purpose Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA. Design/methodology/approach Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies. Findings Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment. Practical implications Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities. Originality/value This study provides useful management insights into the effective use of BA for improving organizational performance.

scholarly journals The effect of individuals’ analytical orientation and capabilities on decision quality and regret

2022 ◽  
Vol 9 (1) ◽  
pp. 0-0
Keyword(s):  
Decision Making ◽  
Least Squares ◽  
Business Students ◽  
Decision Makers ◽  
Data Driven ◽  
Decision Quality ◽  
Business Analytics ◽  
Path Modeling ◽  
Individual Decision ◽  
Data Driven Decisions

Decision makers are exposed to an increasing amount of information. Algorithms can help people make better data-driven decisions. Previous research has focused on both companies’ orientation towards analytics use and the required skills of individual decision makers. However, each individual can make either analytically based or intuitive decisions. We investigated the characteristics that influence the likelihood of making analytical decisions, focusing on both analytical orientation and capabilities of individuals. We conducted a survey using 462 business students as proxies for decision makers and used partial least squares path modeling to show that analytical capabilities and analytical orientation influence each other and affect analytical decision-making, thereby impacting decision quality and decision regret. Our findings suggest that when implementing business analytics solutions, companies should focus on the development not only of technological capabilities and individuals’ skills but also of individuals’ analytical orientation.

Data science in the business environment: customer analytics case studies in SMEs

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jing Lu ◽  
Lisa Cairns ◽  
Lucy Smith
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Process Model ◽  
Data Science ◽  
Business Environment ◽  
Data Driven ◽  
Complex Data ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Customer Analytics

Purpose A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising. Design/methodology/approach Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions. Findings Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams. Research limitations/implications The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper. Originality/value Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

The value of sociality

foresight ◽  
2014 ◽  
Vol 16 (4) ◽  
pp. 309-328 ◽  
Author(s):  
Evgeniya Lukinova ◽  
Mikhail Myagkov ◽  
Pavel Shishkin
Keyword(s):  
Decision Making ◽  
Utility Function ◽  
Social Relationship ◽  
Social Utility ◽  
Content Type ◽  
Individual Level ◽  
Know How ◽  
General Utility ◽  
The Social ◽  
Individual Decisions

Purpose – This paper aims to study the value of sociality. Recent experimental evidence has brought to light that the assumptions of the Prospect Theory by Kahneman and Tversky do not hold in the proposed substantive domain of “sociality”. In particular, the desire to be a part of the social environment, i.e. the environment where individuals make decisions among their peers, is not contingent on the framing. Evolutionary psychologists suggest that humans are “social animals” for adaptive reasons. However, entering a social relationship is inherently risky. Therefore, it is extremely important to know how much people value “sociality”, when the social outcomes are valued more than material outcomes and what kinds of adaptations people use. Design/methodology/approach – We develop a new theory and propose the general utility function that features “sociality” component. We test the theory in the laboratory experiments carried out in several countries. Findings – Our results suggest that when stakes are low the theory of “sociality” is successful in predicting individual decisions: on average, people do value “sociality” and it surpasses the monetary loss. Originality/value – The main contribution of this paper is the breakdown of the risk attitudes under low stakes and individual level of decision-making. Another advancement is the ability to formalize the social utility or the theory of “sociality” in an economic model; we use general utility function that we define both on the outcomes and on the process of the decision-making itself and test in laboratory studies.

How to turn managers into data-driven decision makers

2019 ◽  
Vol 25 (3) ◽  
pp. 553-578 ◽  
Author(s):  
Kevin Daniel André Carillo ◽  
Nadine Galy ◽  
Cameron Guthrie ◽  
Anne Vanhems
Keyword(s):  
Measurement Instrument ◽  
Data Driven ◽  
Business Analytics ◽  
Business World ◽  
Content Type ◽  
Multi Stage ◽  
Attitudes Toward Statistics ◽  
Attitude Toward Business ◽  
Professional Contexts ◽  
The Way

Purpose The purpose of this paper is to emphasize the need to engender a positive attitude toward business analytics in order for firms to more effectively transform into data-driven businesses, and for business schools to better prepare future managers. Design/methodology/approach This paper develops and validates a measurement instrument that captures the attitude toward business statistics, the foundation of business analytics. A multi-stage approach is implemented and the validation is conducted with a sample of 311 students from a business school. Findings The instrument has strong psychometric properties. It is designed so that it can be easily extrapolated to professional contexts and extended to the entire domain of business analytics. Research limitations/implications As the advent of a data-driven business world will impact the way organizations function and the way individuals think, work, communicate and interact, it is crucial to engage a transdisciplinary dialogue among domains that have the expertise to help train and transform current and future professionals. Practical implications The contribution provides educators and organizations with a means to measure and monitor attitudes toward statistics, the most anxiogenic component of business analytics. This is a first step in monitoring and developing an analytics mindset in both managers and students. Originality/value By demonstrating how the advent of the data-driven business era is transforming the DNA and functioning of organizations, this paper highlights the key importance of changing managers’ and all employees’ (to a lesser extent) mindset and way of thinking.

The framework of data-driven and multi-criteria decision-making for detecting unbalanced bidding

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huimin Li ◽  
Limin Su ◽  
Jian Zuo ◽  
Xiaowei An ◽  
Guanghua Dong ◽  
...  
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Public Procurement ◽  
Unit Price ◽  
Data Driven ◽  
Relative Distance ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Content Type ◽  
The Government

PurposeUnbalanced bidding can seriously imposed the government from obtaining the best value for the taxpayers' money in public procurement since it increases the owner's cost and decreases the fairness of the competitive bidding process. How to detect an unbalanced bid is a challenging task faced by theoretical researchers and practical actors. This study aims to develop an identification method of unbalanced bidding in the construction industry.Design/methodology/approachThe identification of unbalanced bidding is considered as a multi-criteria decision-making (MCDM) problem. A data-driven unit price database from the historical bidding document is built to present the reference unit prices as benchmarks. According to the proposed extended TOPSIS method, the data-driven unit price is chosen as the positive ideal solution, and the unit price that has the furthest absolute distance measure as the negative ideal solution. The concept of relative distance is introduced to measure the distances between positive and negative ideal solutions and each bidding unit price. The unbalanced bidding degree is ranked by means of relative distance.FindingsThe proposed model can be used for the quantitative evaluation of unbalanced bidding from a decision-making perspective. The identification process is developed according to the decision-making process. The finding shows that the model will support owners to efficiently and effectively identify unbalanced bidding in the bid evaluation stage.Originality/valueThe data-driven reference unit prices improve the accuracy of the benchmark to evaluate the unbalanced bidding. The extended TOPSIS model is applied to identify unbalanced bidding; the owners can undertake objective decision-making to identify and prevent unbalanced bidding at the stage of procurement.

scholarly journals Data competence maturity: developing data-driven decision making

2018 ◽  
Vol 11 (2) ◽  
pp. 139-158 ◽  
Author(s):  
Thomas G. Cech ◽  
Trent J. Spaulding ◽  
Joseph A. Cazier
Keyword(s):  
Decision Making ◽  
Secondary Education ◽  
Data Science ◽  
Data Driven ◽  
Decision Making Process ◽  
Maturity Model ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Development Data ◽  
Education Performance

Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

scholarly journals M&As get another assist: when CEOs add intuition to the decision mix

2019 ◽  
Vol 41 (3) ◽  
pp. 57-65 ◽  
Author(s):  
Hannu Kuusela ◽  
Siiri Koivumäki ◽  
Mika Yrjölä
Keyword(s):  
Decision Making ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Strategic Decisions ◽  
Group Interactions ◽  
Content Type ◽  
Individual Level ◽  
Top Executives ◽  
Positive Effects

Purpose The purpose of this paper is to analyze the use of intuition in successful merger and acquisition (M&A) decisions. M&As are strategic decisions that can create growth, open up new markets and strengthen the company’s position and competence portfolio. Strategic decisions involve, by their very nature, considerable investments and have company-wide and long-lasting implications. At the same time, the decision-makers have access to large amounts of data from various sources, but these data are often uncertain and inaccurate and entail numerous assumptions. Therefore, M&A decisions are only rational to a degree, and emotional elements, such as intuition, likely play a significant role. Design/methodology/approach Acknowledging how critically important, but also how difficult, M&As are, the authors analyzed nine instances (cases) of successful acquisitions, in which the executives believed that the role of intuition was critical. Findings The findings show that intuition in strategic decision-making emerges on three levels: individual, collective and environmental. Practical implications This paper encourages top executives to proactively acknowledge and take advantage of intuition in their strategic decision-making. It proposes a framework to help with these endeavors. Originality/value This paper contributes by highlighting that intuition is not just a factor on an individual level; it can also surface from group interactions as well as the environment. Surprisingly, all the executives interviewed spoke of the positive effects that intuition can have on acquisition decisions. This is in contrast to the dominant view that considers intuition as nonrational and even as a form of bias.

Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yasanur Kayikci
Keyword(s):  
Decision Making ◽  
Transport System ◽  
Environmental Performance ◽  
Stream Processing ◽  
Data Driven ◽  
Content Type ◽  
Sustainable Logistics ◽  
Processing Data ◽  
Decision Making Model ◽  
Resilience Capacity

PurposeAs the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains.Design/methodology/approachA causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted.FindingsThe result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure.Originality/valueIn this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data.

scholarly journals Building strong health supply chain systems: the visibility and analytics network approach to improving the Nigeria immunisation supply chain

2018 ◽  
Vol 25 (4) ◽  
pp. 199-206 ◽  
Author(s):  
Chibuzo Ottih ◽  
Kevin Cussen ◽  
Mahmud Mustafa
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Data Analytics ◽  
Data Driven ◽  
Evidence Based ◽  
Type Method ◽  
Effective Operation ◽  
Data Driven Decisions ◽  
Vaccine Availability ◽  
Evidence Based Decision Making

BackgroundHealth supply chain managers are unable to effectively monitor the performance of the immunisation supply chain in Nigeria. As a result, they are unable to make effective, data-driven decisions. This results in poor vaccine availability at some service delivery points. A lack of reliable data for evidence-based decision making is a significant contributor to this challenge.MethodThe visibility and analytics network (VAN) principles were introduced to enable end-to-end visibility in the immunisation supply chain and logistics (ISCL) system and make more accurate data available to health supply chain managers.ResultsThe application of the VAN principles has led to improved data collection, real-time stock visibility and enhanced data analytics framework. This enhanced visibility has promoted a culture of accountability and data-driven decision-making, previously unattainable. Health supply chain managers are now equipped with better skills and tools to promote effective operation of the immunisation supply chain.ConclusionThe introduction of VAN principles has been an effective approach to improving data visibility and creating incremental improvements in the ISCL in Nigeria.

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