scholarly journals Big data for small businesses: Abstracting security and decision-making tools

10.29007/6tpw ◽  
2019 ◽  
Author(s):  
Sara Salih ◽  
Kennedy Njenga
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Information Security ◽  
Small Businesses ◽  
Theoretical Underpinning ◽  
Substantive Theory ◽  
Business Decision ◽  
Data Usage ◽  
The Relationship ◽  
Financial Consideration

The study delineates the understanding of big data as an emergent phenomenon that has brought a notable shift in the relationship between technology and business decision- making. Using grounded theory techniques, the study espouses opportunities and alternative perceptions from small businesses regarding the value that big data may offer in contrast to usage experience by big businesses. Information security lies at the heart of these consideration. The study draws on concepts and tenets from the discipline of information security to support a theoretical underpinning for big data usage in small businesses. A substantive theory has been developed from this work with three distinct concepts emerging that show that financial consideration, management mindset and size consideration play a big part in influencing small business perceptions.

The Role of Big Data and Business Analytics in Decision Making

2021 ◽  
pp. 226-257
Author(s):  
Pedro Caldeira Neves ◽  
Jorge Rodrigues Bernardino
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Open Source ◽  
Specific Area ◽  
Business Analytics ◽  
Business Decision ◽  
Business Sustainability ◽  
Open Source Systems ◽  
Fundamental Shift

The amount of data in our world has been exploding, and big data represents a fundamental shift in business decision-making. Analyzing such so-called big data is today a keystone of competition and the success of organizations depends on fast and well-founded decisions taken by relevant people in their specific area of responsibility. Business analytics (BA) represents a merger between data strategy and a collection of decision support technologies and mechanisms for enterprises aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. The authors review the concept of BA as an open innovation strategy and address the importance of BA in revolutionizing knowledge towards economics and business sustainability. Using big data with open source business analytics systems generates the greatest opportunities to increase competitiveness and differentiation in organizations. In this chapter, the authors describe and analyze business intelligence and analytics (BI&A) and four popular open source systems – BIRT, Jaspersoft, Pentaho, and SpagoBI.

Decision-Making with Big Data Using Open Source Business Intelligence Systems

2016 ◽  
pp. 120-147 ◽  
Author(s):  
Jorge Bernardino ◽  
Pedro Caldeira Neves
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Open Source ◽  
Business Intelligence ◽  
Business Performance ◽  
Specific Area ◽  
Enterprise Management ◽  
Business Decision ◽  
Business Intelligence Systems ◽  
Fundamental Shift

The importance of supporting decision making for improving business performance is a crucial, yet challenging task in enterprise management. The amount of data in our world has been exploding and Big Data represents a fundamental shift in business decision-making. Analyzing such so-called Big Data is becoming a keystone of competition and the success of organizations depends on fast and well-founded decisions taken by relevant people in their specific area of responsibility. Business Intelligence (BI) is a collection of decision support technologies for enterprises aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. We review the concept of BI as an open innovation strategy and address the importance of BI in revolutionizing knowledge towards economics and business sustainability. Using Big Data with Open Source Business Intelligence Systems will generate the biggest opportunities to increase competitiveness and differentiation in organizations. In this chapter, we describe and analyze four popular open source BI systems - Jaspersoft, Jedox, Pentaho and Actuate/BIRT.

Goal Programming for Decision Making in Closely Held Businesses

1979 ◽  
Vol 3 (4) ◽  
pp. 31-41 ◽  
Author(s):  
Sang M. Lee ◽  
Robert T. Justis ◽  
Lori Sharp Franz
Keyword(s):  
Decision Making ◽  
Small Business ◽  
Decision Maker ◽  
Goal Programming ◽  
Small Businesses ◽  
Fast Food ◽  
Programming Model ◽  
Multiple Goals ◽  
Business Decision ◽  
Goal Programming Model

There are few analytical and managerial tools available to assist the small business decision maker. This paper presents a practical goal Programming model which can be easily generalized to fit the planning needs of most small businesses. Specifically the model explicitly considers the multiple goals and priorities of the owner-manager and determines if these goals can be accomplished under various demand Projections. An illustrative example of the use of this model with a small fast-food business is given.

An empirical test of the perceived relationship between risk and the constituents severity and probability

2016 ◽  
Vol 24 (2) ◽  
pp. 194-204 ◽  
Author(s):  
Teodor Sommestad ◽  
Henrik Karlzén ◽  
Peter Nilsson ◽  
Jonas Hallberg
Keyword(s):  
Decision Making ◽  
Information Security ◽  
Empirical Test ◽  
Security Risk ◽  
Content Type ◽  
Procedural Support ◽  
The Mean ◽  
Information Security Risk ◽  
The Relationship ◽  
Mean Variance

Purpose In methods and manuals, the product of an information security incident’s probability and severity is seen as a risk to manage. The purpose of the test described in this paper is to investigate if information security risk is perceived in this way, if decision-making style influences the perceived relationship between the three variables and if the level of information security expertise influences the relationship between the three variables. Design/methodology/approach Ten respondents assessed 105 potential information security incidents. Ratings of the associated risks were obtained independently from ratings of the probability and severity of the incidents. Decision-making style was measured using a scale inspired from the Cognitive Style Index; information security expertise was self-reported. Regression analysis was used to test the relationship between variables. Findings The ten respondents did not assess risk as the product of probability and severity, regardless of experience, expertise and decision-making style. The mean variance explained in risk ratings using an additive term is 54.0 or 38.4 per cent, depending on how risk is measured. When a multiplicative term was added, the mean variance only increased by 1.5 or 2.4 per cent. For most of the respondents, the contribution of the multiplicative term is statistically insignificant. Practical Implications The inability or unwillingness to see risk as a product of probability and severity suggests that procedural support (e.g. risk matrices) has a role to play in the risk assessment processes. Originality/value This study is the first to test if information security risk is assessed as an interaction between probability and severity using suitable scales and a within-subject design.

scholarly journals Big Data Management in Maritime Transport

2019 ◽  
Vol 57 (1) ◽  
pp. 123-141
Author(s):  
Marija Jović ◽  
Edvard Tijan ◽  
Rebecca Marx ◽  
Berit Gebhard
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Understanding ◽  
Future Perspective ◽  
Maritime Transport ◽  
Data Usage ◽  
Data Analyses ◽  
Efficient Data ◽  
Routing Monitoring ◽  
Efficient Data Collection

As maritime transport produces a large amount of data from various sources and in different formats, authors have analysed current applications of Big Data by researching global applications and experiences and by studying journal and conference articles. Big Data innovations in maritime transport (both cargo and passenger) are demonstrated, mainly in the fields of seaport operations, weather routing, monitoring/tracking and security. After the analysis, the authors have concluded that Big Data analyses can provide deep understanding of causalities and correlations in maritime transport, thus improving decision making. However, there exist major challenges of an efficient data collection and processing in maritime transport, such as technology challenges, challenges due to competitive conditions etc. Finally, the authors provide a future perspective of Big Data usage in maritime transport.

scholarly journals "DETERMINANTS OF PROFITABILITY: EVIDENCE FROM RUSSIAN AGRICULTURAL FIRMS"

2020 ◽  
Vol 13(62) (2) ◽  
pp. 167-176
Author(s):  
Deari Fitim ◽  
Alija Sadri ◽  
Valeriya V. Lakshina
Keyword(s):  
Decision Making ◽  
Agricultural Sector ◽  
Trade Credit ◽  
Working Capital ◽  
Financial Leverage ◽  
Business Decision ◽  
Leverage Ratio ◽  
Vital Importance ◽  
The Relationship

"The analysis of profitability and the factors that can influence it is of vital importance in business decision making. Thus, the purpose of this study is to examine the relationship between profitability and working capital, leverage, and net trade credit. The study is developed based on a sample of Russian firms, which operate in the agricultural sector, for the period from 2013 to 2017. The result denoted that firms were both, profitable and liquid ones, and bought more than sold on credit. Among other results, the study showed that more profitable firms operated with higher liquidity. Onward, the study suggested that firms should decrease the financial leverage ratio in order to increase profitability"

scholarly journals A big data analysis of the relationship between future thinking and decision-making

2018 ◽  
Vol 115 (8) ◽  
pp. E1740-E1748 ◽  
Author(s):  
Robert Thorstad ◽  
Phillip Wolff
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Population Level ◽  
Future Thinking ◽  
Individual Level ◽  
The Future ◽  
The Individual ◽  
Near Future ◽  
The Relationship ◽  
Far Future

We use big data methods to investigate how decision-making might depend on future sightedness (that is, on how far into the future people’s thoughts about the future extend). In study 1, we establish a link between future thinking and decision-making at the population level in showing that US states with citizens having relatively far future sightedness, as reflected in their tweets, take fewer risks than citizens in states having relatively near future sightedness. In study 2, we analyze people’s tweets to confirm a connection between future sightedness and decision-making at the individual level in showing that people with long future sightedness are more likely to choose larger future rewards over smaller immediate rewards. In study 3, we show that risk taking decreases with increases in future sightedness as reflected in people’s tweets. The ability of future sightedness to predict decisions suggests that future sightedness is a relatively stable cognitive characteristic. This implication was supported in an analysis of tweets by over 38,000 people that showed that future sightedness has both state and trait characteristics (study 4). In study 5, we provide evidence for a potential mechanism by which future sightedness can affect decisions in showing that far future sightedness can make the future seem more connected to the present, as reflected in how people refer to the present, past, and future in their tweets over the course of several minutes. Our studies show how big data methods can be applied to naturalistic data to reveal underlying psychological properties and processes.

scholarly journals DATAFICATION AS A NECESSARY STEP IN THE PROCESSING OF BIG DATA IN DECISION-MAKING TASKS OF BUSINESS

2021 ◽  
Vol 2 ◽  
pp. 75-80
Author(s):  
Martin Misut ◽  
Pavol Jurik
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Quantitative Methods ◽  
Digital Data ◽  
Classification Model ◽  
Decision Making Process ◽  
Digital Form ◽  
Suitable Alternative ◽  
The Given ◽  
The Relationship

The digital transformation of business in the light of opportunities and focusing on the challenges posed by the introduction of Big Data in enterprises allows for a more accurate reflection of the internal and external environmental stimuli. Intuition ceases to be present in the decision-making process, and decision-making becomes strictly data-based. Thus, the precondition for data-based decision-making is relevant data in digital form, resulting from data processing. Datafication is the process by which subjects, objects and procedures are transformed into digital data. Only after data collection can other natural steps occur to acquire knowledge to improve the company's results if we move in the industry's functioning context. The task of finding a set of attributes (selecting attributes from a set of available attributes) so that a suitable alternative can be determined in its decision-making is analogous to the task of classification. Decision trees are suitable for solving such a task. We verified the proposed method in the case of logistics tasks. The analysis subject was tasks from logistics and 80 well-described quantitative methods used in logistics to solve them. The result of the analysis is a matrix (table), in which the rows contain the values of individual attributes defining a specific logistic task. The columns contain the values of the given attribute for different tasks. We used Incremental Wrapper Subset Selection IWSS package Weka 3.8.4 to select attributes. The resulting classification model is suitable for use in DSS. The analysis of logistics tasks and the subsequent design of a classification model made it possible to reveal the contours of the relationship between the characteristics of a logistics problem explicitly expressed through a set of attributes and the classes of methods used to solve them.

scholarly journals An Empirical Examination of the Effects of CTO Leadership on the Alignment of the Governance of Big Data and Information Security Risk Management Effectiveness

10.28945/4799 ◽  
2021 ◽  
Vol 18 ◽  
pp. 041-061
Author(s):  
Shannon Block ◽  
Steven Munkeby ◽  
Samuel Sambasivam
Keyword(s):  
United States ◽  
Big Data ◽  
Information Security ◽  
Regression Model ◽  
Leadership Styles ◽  
Future Research ◽  
Security Risk ◽  
Information Security Risk ◽  
Security Risk Management ◽  
The Relationship

Aim/Purpose: Board of Directors seek to use their big data as a competitive advantage. Still, scholars note the complexities of corporate governance in practice related to information security risk management (ISRM) effectiveness. Background: While the interest in ISRM and its relationship to organizational success has grown, the scholarly literature is unclear about the effects of Chief Technology Officers (CTOs) leadership styles, the alignment of the governance of big data, and ISRM effectiveness in organizations in the West-ern United States. Methodology: The research method selected for this study was a quantitative, correlational research design. Data from 139 participant survey responses from Chief Technology Officers (CTOs) in the Western United States were analyzed using 3 regression models to test for mediation following Baron and Kenny’s methodology. Contribution: Previous scholarship has established the importance of leadership styles, big data governance, and ISRM effectiveness, but not in a combined understanding of the relationship between all three variables. The researchers’ primary objective was to contribute valuable knowledge to the practical field of computer science by empirically validating the relationships between the CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness. Findings: The results of the first regression model between CTOs leadership styles and ISRM effectiveness were statistically significant. The second regression model results between CTOs leadership styles and the alignment of the governance of big data were not statistically significant. The results of the third regression model between CTOs leadership styles, the alignment of the governance of big data, and ISRM effectiveness were statistically significant. The alignment of the governance of big data was a significant predictor in the model. At the same time, the predictive strength of all 3 CTOs leadership styles was diminished between the first regression model and the third regression model. The regression models indicated that the alignment of the governance of big data was a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness. Recommendations for Practitioners: With big data growing at an exponential rate, this research may be useful in helping other practitioners think about how to test mediation with other interconnected variables related to the alignment of the governance of big data. Overall, the alignment of governance of big data being a partial mediator of the relationship between CTOs leadership styles and ISRM effectiveness suggests the significant role that the alignment of the governance of big data plays within an organization. Recommendations for Researchers: While this exact study has not been previously conducted with these three variables with CTOs in the Western United States, overall, these results are in agreement with the literature that information security governance does not significantly mediate the relationship between IT leadership styles and ISRM. However, some of the overall findings did vary from the literature, including the predictive relationship between transactional leadership and ISRM effectiveness. With the finding of partial mediation indicated in this study, this also suggests that the alignment of the governance of big data provides a partial intervention between CTOs leadership styles and ISRM effectiveness. Impact on Society: Big data breaches are increasing year after year, exposing sensitive information that can lead to harm to citizens. This study supports the broader scholarly consensus that to achieve ISRM effectiveness, better alignment of governance policies is essential. This research highlights the importance of higher-level governance as it relates to ISRM effectiveness, implying that ineffective governance could negatively impact both leadership and ISRM effectiveness, which could potentially cause reputational harm. Future Research: This study raised questions about CTO leadership styles, the specific governance structures involved related to the alignment of big data and ISRM effectiveness. While the research around these variables independently is mature, there is an overall lack of mediation studies as it relates to the impact of the alignment of the governance of big data. With the lack of alignment around a universal framework, evolving frameworks could be tested in future research to see if similar results are obtained.

Intelligent Big Data Analytics

2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Decision ◽  
Technology System ◽  
System Service ◽  
Managerial Perspective

Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores intelligent big data analytics from a managerial perspective. More specifically, it first looks at the age of trinity and argues that intelligent big data analytics is at the center of the age of trinity. This chapter then proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service, and management for improving business decision making. Then it examines intelligent big data analytics for management taking into account four managerial functions: planning, organizing, leading, and controlling. The proposed approach in this chapter might facilitate the research and development of intelligent big data analytics, big data analytics, business intelligence, artificial intelligence, and data science.

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