Understanding Data Analytics Is Good but Knowing How to Use It Is Better!

2019 ◽  
pp. 125-160
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Models ◽  
Big Data Analytics ◽  
Success Factor ◽  
New Business ◽  
Technical Architecture ◽  
The Veil ◽  
Key Success Factor ◽  
New Business Models

Collecting the data and being able to generate value from it: this is certainly the key success factor of tomorrow's champions, one that will allow you to innovate and create new business models. Faced with the 3Vs of big data, many companies are embarking on big data projects with the main objective: generating value. The goal is to succeed, by the detailed analysis of large amounts of data, to lift the veil and discover hitherto hidden models and barely perceptible correlations, as many new business opportunities that companies must grasp. The key to the success of any big data analytics initiative is to define your goals, identify specific business questions that a suitable technical architecture will need to answer, and use the data experts to generate value from data by using specific algorithms.

Datenbasierte Produktionsoptimierung*/Data-driven production optimization: Areas of development for manufacturing companies

2018 ◽  
Vol 108 (03) ◽  
pp. 108-112
Author(s):  
D. Bauer ◽  
T. Maurer ◽  
T. Bauernhansl
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Applied Research ◽  
Business Models ◽  
Big Data Analytics ◽  
Production Optimization ◽  
Data Driven ◽  
Manufacturing Companies ◽  
New Business ◽  
New Business Models

Unternehmen sehen in Big-Data-Analysen ein großes Potenzial zur Optimierung der klassischen Produktionsziele sowie zur Entwicklung neuer Geschäftsmodelle. Eine Studie des Fraunhofer IPA analysiert, welche Herausforderungen bei der Umsetzung dieser Potenziale auftreten. Darauf aufbauend werden Entwicklungsfelder für die angewandte Forschung und produzierende Unternehmen erarbeitet.   Companies expect huge benefits from big data analytics both to improve traditional production targets and to develop new business models. A study conducted by Fraunhofer IPA analyzes the upcoming challenges in exploiting these opportunities. It provides the basis for identifying areas of development for applied research and for manufacturing companies.

An Effective Model for Consumer Need Prediction Using Big Data Analytics

2021 ◽  
Author(s):  
Yihao Tian
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Customer Behavior ◽  
Data Set ◽  
Customer Data ◽  
Demand Prediction ◽  
Management Ratio ◽  
The Veil

Big data is an unstructured data set with a considerable volume, coming from various sources such as the internet, business organizations, etc., in various formats. Predicting consumer behavior is a core responsibility for most dealers. Market research can show consumer intentions; it can be a big order for a best-designed research project to penetrate the veil, protecting real customer motivations from closer scrutiny. Customer behavior usually focuses on customer data mining, and each model is structured at one stage to answer one query. Customer behavior prediction is a complex and unpredictable challenge. In this paper, advanced mathematical and big data analytical (BDA) methods to predict customer behavior. Predictive behavior analytics can provide modern marketers with multiple insights to optimize efforts in their strategies. This model goes beyond analyzing historical evidence and making the most knowledgeable assumptions about what will happen in the future using mathematical. Because the method is complex, it is quite straightforward for most customers. As a result, most consumer behavior models, so many variables that produce predictions that are usually quite accurate using big data. This paper attempts to develop a model of association rule mining to predict customers’ behavior, improve accuracy, and derive major consumer data patterns. The finding recommended BDA method improves Big data analytics usability in the organization (98.2%), risk management ratio (96.2%), operational cost (97.1%), customer feedback ratio (98.5%), and demand prediction ratio (95.2%).

The Concept-Creative Business Model as Success Factor

2008 ◽  
Vol 9 (1) ◽  
pp. 13-35
Author(s):  
Gunter Faltin ◽  
Liv Jacobsen
Keyword(s):  
Business Models ◽  
Business Administration ◽  
Entrepreneurial Activity ◽  
Success Factor ◽  
Entrepreneurial Success ◽  
Complex Dynamic ◽  
Economic Rationale ◽  
New Business ◽  
Research Findings ◽  
New Business Models

Current discussions about entrepreneurship are framed primarily in terms of business administration. But entrepreneurship is more: a complex, dynamic and multifaceted phenomenon with a creative dimension that is in parts beyond economic-rationale discourse. Business models can be built upon something else than patents or research findings by transforming genuine concepts into entrepreneurial activity. Unconventionality and original thinking are essential factors for entrepreneurial success. In a world of ever-easier division of labor, entrepreneurs have the possibilities to use existing components to create new business models. This will open up perspectives for many more people to participate in entrepreneurship than previously imagined.

Big Data and Data Analytics Research

Web Services ◽  
2019 ◽  
pp. 2161-2171
Author(s):  
Miltiadis D. Lytras ◽  
Vijay Raghavan ◽  
Ernesto Damiani
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Business Models ◽  
Internet Technology ◽  
Cognitive Computing ◽  
New Paradigm ◽  
Web Science ◽  
New Business ◽  
Value Propositions

The Big Data and Data Analytics is a brand new paradigm, for the integration of Internet Technology in the human and machine context. For the first time in the history of the human mankind we are able to transforming raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship. For the Web Science research, this is a new methodological and technological spectrum of advanced methods, frameworks and functionalities never experienced in the past. At the same moment communities out of web science need to realize the potential of this new paradigm with the support of new sound business models and a critical shift in the perception of decision making. In this short visioning article, the authors are analyzing the main aspects of Big Data and Data Analytics Research and they provide their own metaphor for the next years. A number of research directions are outlined as well as a new roadmap towards the evolution of Big Data to Smart Decisions and Cognitive Computing. The authors do hope that the readers would like to react and to propose their own value propositions for the domain initiating a scientific dialogue beyond self-fulfilled expectations.

Proactive Organizational Structure in Financial System

2015 ◽  
pp. 393-404
Author(s):  
Korhan Arun ◽  
Tekin Yenigün
Keyword(s):  
Structural Changes ◽  
Business Models ◽  
Success Factor ◽  
Operation System ◽  
Critical Success ◽  
Reactive Behavior ◽  
New Business ◽  
Modern Business ◽  
Service Sectors ◽  
New Business Models

Technology alters the structure of the systems in the finance and service sectors. Nevertheless, technology has been chancing operating systems and as a source to the emergence of new business models. The boundaries of departments in enterprises are weakened and disappeared, these changes give rise to the emergence of showing less commitment in the behavior of employees. In modern business the survival of the organizations does not seem possible, which see success in reactive behavior of the strategy-structure-interaction classical triple. Critical success factor is based foresight and proactivity in all areas of operations including organizing. In this chapter, enterprise organizations' financial departments and resulting changes of structures of the financial sector entities, the effects of this structural changes in the operation system with the new business models is discussed, the tips on how financial system's agencies and departments can fulfill the requirements of proactive nature revealed is studied.

Big data analytics for investigating Taiwan Line sticker social media marketing

2019 ◽  
Vol 32 (2) ◽  
pp. 589-606 ◽  
Author(s):  
Shu-Hsien Liao ◽  
Szu-Yu Hsu
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Model ◽  
Data Analytics ◽  
Business Models ◽  
Model Development ◽  
Big Data Analytics ◽  
Social Media Marketing ◽  
Content Type ◽  
Purchase Behaviors

Purpose Line sticker, a social media, it allows users to exchange multimedia files and engage in one-to-one and one-to-many communication with text, pictures, animation and sound. The purpose of this paper is to examine various Taiwan user experiences in the Line sticker use behaviors. Further, this research looks at how the situations of Line sticker proprietors and their affiliates are disseminated for formulating social media marketing (SMM) in its business model concerns. Design/methodology/approach This study examines the experience of various Taiwanese Line stickers users utilizing a market survey, a total of 1,164 valid questionnaire data, and the questionnaire is divided into five sections with 30 items in terms of the database design. All questions use nominal and order scales. This study develops a big data analytics approach, including cluster analysis and association rules, based on a big data structure and a relational database. Findings The authors divide Taiwan Line sticker users into three clusters by their profiles and then find each group’s social media utilization and online purchase behaviors for investigating the Line sticker SMM and business models. Originality/value This is the first study to offer a big data analytics to investigate and analyze the varieties in the use of Line sticker by exploring users’ behaviors for further SMM and business model development.

Positioning big data analytics capabilities towards financial service agility

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abeeku Sam Edu
Keyword(s):  
Big Data ◽  
Financial Services ◽  
Data Analytics ◽  
Business Models ◽  
Empirical Test ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Financial Service ◽  
Content Type ◽  
It Capability

PurposeEnterprises are increasingly taking actionable steps to transform existing business models through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility.Design/methodology/approachThe study relied on survey data from 485 banking professionals' perspectives with BDA usage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed.FindingsBased on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources.Practical implicationsThe study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products.Originality/valueThis study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations.

scholarly journals The Hype of Big Data Analytics and Auditors

2019 ◽  
Vol 8 (2) ◽  
pp. 1-4 ◽  
Author(s):  
Prem Lal Joshi ◽  
Govindan Marthandan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Models ◽  
Big Data Analytics ◽  
Cognitive Tasks ◽  
Fast Tracking ◽  
The Future

In the era of fast-tracking digitization and unconventional big data analytics, business models are being reshaped and they impact auditing amongst auditors. This viewpoint paper takes into account the procedures underlying on big data and its analytics in driving the evolution of business and identifies some of the unresolved issues and concerns on auditors, especially in the context of cognitive tasks. The paper continues to focus on the current spate of discussions on big data and auditing profession. It explains the nature of big data and its characteristics as well as the output types. This paper also tries to find answers for what is new in it, how it assists the auditors along with some unresolved  issues and concerns. Since big data analytics is the future, auditors need to reshape themselves in terms of skills and competencies to meet the emerging technological challenges. 

scholarly journals How Does a Social Open Innovation Succeed? Learning from Burro Battery and Grassroots Innovation Festival of India

2018 ◽  
Vol 24 (1) ◽  
pp. 122-143 ◽  
Author(s):  
Jinhyo Joseph Yun ◽  
Abiodun A. Egbetoku ◽  
Xiaofei Zhao
Keyword(s):  
Open Innovation ◽  
Business Models ◽  
Social Innovation ◽  
Innovation Policy ◽  
Success Factor ◽  
First Case ◽  
Grassroots Innovation ◽  
New Business ◽  
New Business Models

As people pay attentions to social innovation as the source of innovative ideas and the repository of new business models, this study poses the following research questions: How does a social open innovation succeed? What is the success factor of social open innovation? What are the successful dynamics of social open innovation? This article selected two case studies: one is the Burro Battery Company in Ghana and the other is grassroots innovation enterprise of India known as the Honey Bee Network and its collaborator, National Innovation Foundation (NIF), Ahmedabad. The first case is a social open innovation firm case while the second case is a social open innovation policy case. Through deep case study, we found out the ways of success of social open innovation strategy and social open innovation policy.

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