Data science for business: benefits, challenges and opportunities

2020 ◽  
Vol 33 (2) ◽  
pp. 149-163 ◽  
Author(s):  
Mauricius Munhoz de Medeiros ◽  
Norberto Hoppen ◽  
Antonio Carlos Gastaud Maçada
Keyword(s):  
Organizational Performance ◽  
Performance Management ◽  
Data Science ◽  
Business Environment ◽  
Data Driven ◽  
Business Organizations ◽  
Data Governance ◽  
Economic Sectors ◽  
Content Type ◽  
Challenges And Opportunities

Purpose This paper aims to identify the benefits of data science (DS) for organizations, highlighting the challenges and opportunities related to developing this capability. Design/methodology/approach Initially, a literature review was performed. Later, empirical data were collected through a structured electronic interview answered by 211 informants, who are most experienced managers of medium and large organizations from different economic sectors, and data were submitted to content analysis. Findings The most frequently observed benefits are as follows: support for data analysis and insight generation with agility; creation of a data-driven culture; improvement of data quality; facilitating the understanding of the business environment, opportunity sensing; and organizational performance management. The most observed challenges are as follows: data-driven culture; DS training; allocation of investments in analytical technologies; and data governance and strategy. Research limitations/implications In addition, to mapping the state of the art on the subject, it contributes to the expansion of scientific knowledge through the identification and disclosure of 11 benefit indicators and 16 challenge indicators associated with analytical capabilities. Practical implications To transform data into information and add value to the business, organizations need to make efforts to enable executive mindset change, the formulation of strategies and governance mechanisms gave the renewal of workforce competencies and the allocation of investments in information technology. Originality/value A vast body of empirical evidence is gathered that consolidates different views on the benefits and challenges associated with DS for business.

The mediating effect of teamwork on the relationship between strategic orientation and performance of Nigerian banks

2019 ◽  
Vol 31 (5) ◽  
pp. 744-760 ◽  
Author(s):  
Innocent Otache
Keyword(s):  
Organizational Performance ◽  
Banking Sector ◽  
Business Environment ◽  
Strategic Orientation ◽  
Mediating Effect ◽  
Superior Performance ◽  
Business Organizations ◽  
Content Type ◽  
The Relationship ◽  
Practical Implications

Purpose The purpose of this study is to empirically explore the mediating effect of teamwork on the relationship between strategic orientation and organizational performance. Design/methodology/approach This study adopted a descriptive research design. A self-reported questionnaire was used to collect data from 253 bank managers representing 20 commercial banks in Nigeria. The author used SmartPLS-SEM to analyze the data collected. Findings The results of the structural models showed a significantly positive relationship between strategic orientation and organizational performance on the one hand and between strategic orientation and teamwork on the other. It was also found that teamwork had a significantly positive link with organizational performance. Further analysis revealed that teamwork fully mediated the relationship between strategic orientation and organizational performance. Research limitations/implications This study focuses on the Nigerian banking sector. Thus, it limits the generalizability of its findings to other sectors not covered. Future researchers could extend the study to other sectors to corroborate the findings presented. Practical implications The findings of this study provide some practical implications for business organizations and managers. Business organizations must be strategically positioned so that they can compete in today’s highly dynamic and competitive business environment and achieve superior performance. Likewise, business managers should make sure that all employees and sections in their organizations work cooperatively as a team by creating a collaborative climate where team spirit and teamwork thrive. Originality/value To the best of the author’s knowledge, this study is the first to provide empirical evidence of the mediating effect of teamwork on the relationship between strategic orientation and organizational performance. In that regard, it makes a valuable contribution to the field of strategic management and enhances the applicability and the generalizability of contingency and resource-based view theories across different environmental settings.

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.

Reinforcing the significance of human factor in achieving quality performance in data-driven supply chain management

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanjana Mondal ◽  
Kaushik Samaddar
Keyword(s):  
Supply Chain ◽  
Organizational Performance ◽  
Industry 4.0 ◽  
Management Practices ◽  
Human Factor ◽  
Research Work ◽  
Data Driven ◽  
Theory Approach ◽  
Quality Performance ◽  
Content Type

PurposeThe paper aims to explore the various dimensions of human factor relevant for integrating data-driven supply chain quality management practices (DDSCQMPs) with organizational performance. Keeping the transition phase from “Industry 4.0” to “Industry 5.0” in mind, the paper reinforces the role of the human factor and critically discusses the issues and challenges in the present organizational setup.Design/methodology/approachFollowing the grounded theory approach, the study arranged in-depth interviews and focus group sessions with industry experts from various service-oriented firms in India. Dimensions of human factor identified from there were grouped together through a morphological analysis (MA), and interlinkages between them were explored through a cross-consistency matrix.FindingsThis research work identified 20 critical dimensions of human factor and have grouped them under five important categories, namely, cohesive force, motivating force, regulating force, supporting force and functional force that drive quality performance in the supply chain domain.Originality/valueIn line with the requirements of the present “Industry 4.0” and the forthcoming “Industry 5.0”, where the need to collaborate human factor with smart system gets priority, the paper made a novel attempt in presenting the critical human factors and categorizing them under important driving forces. The research also contributed in linking DDSCQMPs with organizational performance. The proposed framework can guide the future researchers in expanding the theoretical constructs through initiating further cross-cultural studies across industries.

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.

Interagency collaboration for graduate employment opportunities in Uganda

2020 ◽  
Vol 62 (3) ◽  
pp. 271-291
Author(s):  
Peter Ntale ◽  
Jude Ssempebwa ◽  
Badiru Musisi ◽  
Muhammed Ngoma ◽  
Gyaviira Musoke Genza ◽  
...  
Keyword(s):  
Organizational Performance ◽  
Supply And Demand ◽  
Business Environment ◽  
Civil Society Organizations ◽  
Future Research ◽  
University Graduates ◽  
Cross Sectional Survey ◽  
Employment Opportunities ◽  
Content Type ◽  
Graduate Employment

PurposeThe purpose of this paper is to identify gaps in the structure of organizations that hinder collaboration of organizations involved in the creation of graduate employment opportunities in Uganda.Design/methodology/approachData was collected from staff and leaders of 14 organizations that were purposely selected to represent government, private, and civil society organizations. These organizations were selected based on their mandates, which touch on the employability of university graduates in the country in very direct ways. This was a cross-sectional survey design—based on a self-administered questionnaire, key informant interviews, and documentary analysis.FindingsOrganizations were found to have “Tell”/directive decision-making, high power distance between employees, and jobs were not coded in a way that gives employees freedoms to interact and build collaborative relationships. Finally, rules and regulations were very restrictive, disorienting employee's abilities to collaborate.Research limitations/implicationThis research concentrated on the gaps that exist in the structure of organizations from which the results point to inadequate relational, interactional, inclusive, and democratic space among different stakeholders. It would be useful for future research to examine the extent to which the structure of organizations not only impacts collaboration but also measures the level to which it affects organizational performance.Practical implicationsThe knowledge economy of the twenty-first century demands for collaborative engagements with different stakeholders if they are to survive the competitive business environment. Collaborative engagement helps in the sharing of knowledge, expertise, and resources, development of more coherent services, facilitation of innovation and evaluation, avoiding duplication of work, and minimizing conflicts and competition while creating synergy among partners.Originality/valueUnlike previous studies, which have examined employability of graduates from a supply side perspective, this study investigates organizations from both the supply and demand perspectives and identifies synergy that is as a result of bringing organizations to work together.

Global challenges, sustainable development, and their implications for organizational performance

2015 ◽  
Vol 27 (4) ◽  
pp. 430-446 ◽  
Author(s):  
Busaya Virakul
Keyword(s):  
Sustainable Development ◽  
Organizational Performance ◽  
Relevant Literature ◽  
Business Practices ◽  
Business Organizations ◽  
Effective Response ◽  
Content Type ◽  
Global Challenges ◽  
Sustainability Policy ◽  
The Impact

Purpose – This paper aims to propose an effective response by business organizations to the impact of global challenges and sustainable development (SD). It also presents an overview model of organizational performance employing such an approach. Design/methodology/approach – This paper is a conceptual work based upon a review of theories, research findings and reports gathered from relevant literature. The review yielded the following research framework: many countries are facing global challenges; these global challenges are affecting business organizations as external factors; SD is a concept employed to address these challenges; SD can be applied in business organizations through corporate social responsibility (CSR), corporate governance (CG) and sustainability policy and practices; and embedding CSR, CG and sustainability concepts at a strategic level is an effective response to global challenges. Findings – Global challenges are impacting on business organizations and will continue to do so into the future. CSR, CG and sustainability concepts are increasingly being adopted by leading business organizations throughout the world. Embedding CSR, CG and sustainability concepts at a strategic level can sustain long-term organizational performance, as they help businesses face global challenges in a positive manner and maintain their position in societies on good terms with all stakeholders. Research limitations/implications – Different cultural or socio-economic environments may limit the interpretation and application of the findings or propositions in this research. Practical implications – How CSR, CG and sustainability concepts can be holistically implemented in business practices. Social implications – The role of business in lessening the effect of global challenges and supporting SD is illustrated in the proposed model. Originality/value – This paper demonstrates connections among the following critical influences on organizational performance: global challenges; SD; and CSR, CG and sustainability.

Business Environment as Key Driver of SME Performance

2021 ◽  
pp. 239-263
Author(s):  
Olalekan Asikhia ◽  
Vannie Naidoo
Keyword(s):  
Organizational Performance ◽  
Performance Management ◽  
Business Environment ◽  
The Body ◽  
Environmental Turbulence ◽  
Market Environment ◽  
Negative Effects ◽  
Body Of Knowledge ◽  
Validated Questionnaire ◽  
And Performance

The chapter established the effects of Nigerian market environment on SMEs performance. An empirical study was conducted with survey research design of 21,444 firms and a sample size of 1,102 was arrived at scientifically. Probability sampling methods were employed. An adapted validated questionnaire, and a 0.82-0.96 reliability coefficients range was used. Inferential statistics were used to analyse the data using SPSS software version 22.0. The findings reveal that Nigerian market environment had significant negative effects on the SME performance. The different components of the Nigerian market environment have different effects on the SME performance. The results imply that the environmental turbulence could be responsible for the high failure rate of SMEs in Nigeria. The study contributes to the body of knowledge on environmental and performance management by noting the criticality of the industry market environment in facilitating organizational performance.

Optimizing competitive performance of service firms in data-rich environment

2020 ◽  
Vol 30 (6) ◽  
pp. 681-706
Author(s):  
Muhammad Sabbir Rahman ◽  
Md Afnan Hossain ◽  
Fadi Abdel Muniem Abdel Fattah ◽  
Shahriar Akter
Keyword(s):  
Information System ◽  
Business Environment ◽  
Data Driven ◽  
Equation Modeling ◽  
Service Firms ◽  
Competitive Performance ◽  
Theoretical Understanding ◽  
Content Type ◽  
Management Capability ◽  
Marketing Information

PurposeThe marketing information system (MkIS) in the data-rich business environment receives all the attention these days, but as essential and perhaps even more essential is the marketing information system management capability (MkISMC). Although many service firms apprehend the return from MkIS, others clearly struggle. It seems that MkIS management capability dynamics and their direct/indirect holistic influence on service firm's competitive performance (SFCP) are unsolved in the current data-driven service economy. This study aims to conceptualize a model and test the antecedents on service firms' competitive performance.Design/methodology/approachThis study utilizes a survey of a sizeable sample of service firms’ managers at the firm level. A total of 250 useable responses were obtained and analyzed through structural equation modeling.FindingsResults reveal that variables under their respective direct influences are positively and significantly related. Interestingly, MkISMC has a relatively large magnitude of positive and direct effects on service firms' competitive performance. The other variables, such as the use of marketing analytics (UMAN), service innovation and management (SINM), partially mediate the effect of MkISMC on the competitive performance of service firms.Practical implicationsThe findings inform practitioners that MkISMC, UMAN and SINM play a vital role in attaining service firms' competitive performance in the data-rich environment. Overall, it deepens the understanding of the mediation effect of UMAN and SINM of service firms on competitive performance.Originality/valueThe study advances theoretical understanding of resource-based view (RBV), market orientation and dynamic capability that formulate the relationship of MkISMC, UMAN and SINM in attaining SFCP in the ever-changing data-driven business economy.

Product-level profitability

2019 ◽  
Vol 33 (1) ◽  
pp. 214-237
Author(s):  
Hannu Hannila ◽  
Joni Koskinen ◽  
Janne Harkonen ◽  
Harri Haapasalo
Keyword(s):  
Business Processes ◽  
Data Driven ◽  
Product Portfolio ◽  
Data Governance ◽  
Content Type ◽  
Technical Product ◽  
Product Level ◽  
Level Data ◽  
Corporate Level

Purpose The purpose of this paper is to analyse current challenges and to articulate the preconditions for data-driven, fact-based product portfolio management (PPM) based on commercial and technical product structures, critical business processes, corporate business IT and company data assets. Here, data assets were classified from a PPM perspective in terms of (product/customer/supplier) master data, transaction data and Internet of Things data. The study also addresses the supporting role of corporate-level data governance. Design/methodology/approach The study combines a literature review and qualitative analysis of empirical data collected from eight international companies of varying size. Findings Companies’ current inability to analyse products effectively based on existing data is surprising. The present findings identify a number of preconditions for data-driven, fact-based PPM, including mutual understanding of company products (to establish a consistent commercial and technical product structure), product classification as strategic, supportive or non-strategic (to link commercial and technical product structures with product strategy) and a holistic, corporate-level data model for adjusting the company’s business IT (to support product portfolio visualisation). Practical implications The findings provide a logical and empirical basis for fact-based, product-level analysis of product profitability and analysis of the product portfolio over the product life cycle, supporting a data-driven approach to the optimisation of commercial and technical product structure, business IT systems and company product strategy. As a virtual representation of reality, the company data model facilitates product visualisation. The findings are of great practical value, as they demonstrate the significance of corporate-level data assets, data governance and business-critical data for managing a company’s products and portfolio. Originality/value The study contributes to the existing literature by specifying the preconditions for data-driven, fact-based PPM as a basis for product-level analysis and decision making, emphasising the role of company data assets and clarifying the links between business processes, information systems and data assets for PPM.

Data-driven marketing for growth and profitability

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Balakrishna Grandhi ◽  
Nitin Patwa ◽  
Kashaf Saleem
Keyword(s):  
Big Data ◽  
Online Survey ◽  
Shareholder Value ◽  
Business Environment ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Data Driven ◽  
Perceived Ease Of Use ◽  
Content Type ◽  
Customer Centricity

PurposeIn the current business environment, more uncertain than ever before, understanding consumer behavior is an integral part of an organization's strategic planning and execution process. It is the key driver for becoming a market leader. Therefore, it is important that all processes in business are customer centric. Marketers need to harness big data by engaging in data driven-marketing (DDM) to help organizations choose the “right” customers, to “keep” and “grow” them and to sustain “growth” and “profitability”. This research examines DDM adoption practices and how companies can aim to enhance shareholder value by bringing about “customer centricity”.Design/methodology/approachAn online survey conducted in 2016 received 180 responses from junior, middle and senior executives. Of the total responses, 26% were from senior management, 39% from middle management and the remaining 35% from junior management. Industries represented in the survey included retail, BFSI, healthcare and government, automobile, telecommunication, transport and logistics and IT. Other industries represented were aviation, marketing research and consulting, hospitality, advertising and media and human resource.FindingsSuccess of DDM depends upon how well an organization embraces the practice. The first and foremost indicator of an organization's commitment is the extent of resources invested for DDM. Respondents were divided into four categories; Laggards, Dabblers, Contenders and Leaders based on their “current level of investments” and “willingness to enhance investments” soon.Research limitations/implicationsWith storming digital age and the development of analytics, the process of decision-making has gained significant importance. Judgment and intuition too are critical to the process. Choosing an appropriate action cannot be done strictly on a rational basis.Practical implicationsThe results of the study offer interesting implications for managing the growing sea of data. An iterative and incremental approach is the need of the hour, even if it has to start with baby steps, to invest in and reap the fruits of DDM. The intention to use any system is always dependent on two primary belief factors: perceived usefulness and perceived ease of use; however, attitudes and social factors are equally important.Originality/valueThere is a dearth of knowledge with regards to who is and is not adopting DDM, and how best big data can be harnessed for enhancing effectiveness and efficiency of marketing budget. It is, therefore, imperative to build a knowledge base on DDM practices, challenges and opportunities. Better use of data can help companies enhance shareholder value by bringing about “customer centricity”.

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