scholarly journals Implementing Data Strategy

2020 ◽  
Vol 24 ◽  
Author(s):  
Radhakrishnan Balakrishnan ◽  
Satyasiba Das ◽  
Manojit Chattopadhyay
Keyword(s):  
Big Data ◽  
Data Management ◽  
Value Creation ◽  
Customer Value ◽  
Data Extraction ◽  
Management Framework ◽  
Value Propositions ◽  
Four Levels ◽  
Using Data ◽  
Transactional Data

With the arrival of Big Data, organizations have started building data-enabled customer value propositions to increase monetizing and cost-saving opportunities. Organizations have to implement a set of guidelines, procedures, and processes to manage, process and transform data that could be leveraged for value creation. This study has approached the journey of an organization towards data-enabled value creation through four levels of data processing, such as data extraction, data transformation, value creation, and value delivery. This study has critical inferences on using data management solutions such as RDBMS, NoSQL, NewSQL, Big Data and real-time reporting tools to support transactional data in internal systems, and other types of data in external systems such as Social Media. The outcome of this study is a methodological technology independent data management framework an organization could use when building a strategy around data. This study provides guidelines for defining an enterprise-wide data management solution, helping both the academicians and practitioners.

scholarly journals Value Creation Attributes—Clustering Strategic Options for Romanian SMEs

2020 ◽  
Vol 12 (17) ◽  
pp. 7007 ◽  
Author(s):  
Alin Stancu ◽  
Alina Filip ◽  
Mihai Roșca ◽  
Daniela Ioniță ◽  
Raluca Căplescu ◽  
...  
Keyword(s):  
Value Creation ◽  
Customer Value ◽  
Quantitative Research ◽  
Principal Component ◽  
Customer Experience ◽  
Value Proposition ◽  
Multiple Sources ◽  
Customer Delight ◽  
Value Propositions ◽  
Strategic Options

Value proposition can be an important source of competitive advantage for small and medium sized enterprises (SMEs). Unlike large companies which follow a rational and sequential process, developing a value proposition in an SME is instead a trial and error process. Therefore, those companies are experimenting with various options. The purpose of this paper was to identify the value strategies used by SMEs based on value dimensions and attributes and to find specific groups of SMEs with a similar market approach. We present a theoretical framework on customer value creation and customer value communication, followed by a quantitative research on 399 Romanian SMEs. We used a principal component analysis to reduce the number of choices and afterwards we ran a cluster analysis to identify the distinct groups of SMEs using specific value propositions. We found that there are three major strategic options based on customer experience, affordability and customization, and four distinct clusters: customer delight (A), multiple sources of differentiation (B), one-to-one marketing (C) and cost—effectiveness (D). Three groups use distinct value propositions—A focuses on customer experience, C on customization, D on affordability—while B mixes all of them.

scholarly journals Value creation from big data: Looking inside the black box

2017 ◽  
Vol 16 (2) ◽  
pp. 105-140 ◽  
Author(s):  
Jing Zeng ◽  
Keith W. Glaister
Keyword(s):  
Big Data ◽  
Data Management ◽  
Conceptual Framework ◽  
Value Creation ◽  
Current Theory ◽  
Black Box ◽  
Future Research ◽  
Individual Data ◽  
Timely Manner

The advent of big data is fundamentally changing the business landscape. We open the ‘black box’ of the firm to explore how firms transform big data in order to create value and why firms differ in their abilities to create value from big data. Grounded in detailed evidence from China, the world’s largest digital market, where many firms actively engage in value creation activities from big data, we identify several novel features. We find that it is not the data itself, or individual data scientists, that generate value creation opportunities. Rather, value creation occurs through the process of data management, where managers are able to democratize, contextualize, experiment and execute data insights in a timely manner. We add richness to current theory by developing a conceptual framework of value creation from big data. We also identify avenues for future research and implications for practicing managers.

Big Data and Advance Analytics: Architecture, Techniques, Applications, and Challenges

2017 ◽  
Vol 4 (4) ◽  
pp. 21-47 ◽  
Author(s):  
Surabhi Verma
Keyword(s):  
Big Data ◽  
Data Management ◽  
Big Data Analytics ◽  
Competitive Advantages ◽  
Organizational Goals ◽  
Current State ◽  
Management Processes ◽  
Using Data ◽  
Tools And Techniques ◽  
Different Characteristics

The insights that firms gain from big data analytics (BDA) in real time is used to direct, automate and optimize the decision making to successfully achieve their organizational goals. Data management (DM) and advance analytics (AA) tools and techniques are some of the key contributors to making BDA possible. This paper aims to investigate the characteristics of BD, processes of data management, AA techniques, applications across sectors and issues that are related to their effective implementation and management within broader context of BDA. A range of recently published literature on the characteristics of BD, DM processes, AA techniques are reviewed to explore their current state, applications, issues and challenges learned from their practice. The finding discusses different characteristics of BD, a framework for BDA using data management processes and AA techniques. It also discusses the opportunities/applications and challenges managers dealing with these technologies face for gaining competitive advantages in businesses. The study findings are intended to assist academicians and managers in effectively quantifying the data available in an organization into BD by understanding its properties, understanding the emerging technologies, applications and issues behind BDA implementation.

Big Data Management

2017 ◽  
Vol 7 (3) ◽  
pp. 44-55 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Business Leaders ◽  
Privacy And Security ◽  
Review Analysis ◽  
Using Data

The objective of this article is to provide the advanced issues and approaches of big data management. The literature review indicates the overview of big data management; the aspects of Big Data Analytics (BDA); the importance of big data management; the methods for big data management; the privacy and security concerns of big data management; and the big data management in the health care industry. Organizations that have been successful in working with effective big data management have accomplished this issue using data to help make sense of the information. The volume of data that companies are able to gather about customers and market conditions can provide business leaders with insights into new revenue and business opportunities, presuming they can spot the opportunities in vast amounts of data. The literature review analysis provides both practitioners and researchers an important understanding about big data management in modern organizations.

scholarly journals Big Data Management Canvas: A Reference Model for Value Creation from Data

2019 ◽  
Vol 3 (1) ◽  
pp. 19 ◽  
Author(s):  
Michael Kaufmann
Keyword(s):  
Big Data ◽  
Data Management ◽  
Value Creation ◽  
Reference Model ◽  
Solution Space ◽  
Use Cases ◽  
Data Systems ◽  
Big Data Applications ◽  
Big Data Systems ◽  
Map Data

Many big data projects are technology-driven and thus, expensive and inefficient. It is often unclear how to exploit existing data resources and map data, systems and analytics results to actual use cases. Existing big data reference models are mostly either technological or business-oriented in nature, but do not consequently align both aspects. To address this issue, a reference model for big data management is proposed that operationalizes value creation from big data by linking business targets with technical implementation. The purpose of this model is to provide a goal- and value-oriented framework to effectively map and plan purposeful big data systems aligned with a clear value proposition. Based on an epistemic model that conceptualizes big data management as a cognitive system, the solution space of data value creation is divided into five layers: preparation, analysis, interaction, effectuation, and intelligence. To operationalize the model, each of these layers is subdivided into corresponding business and IT aspects to create a link from use cases to technological implementation. The resulting reference model, the big data management canvas, can be applied to classify and extend existing big data applications and to derive and plan new big data solutions, visions, and strategies for future projects. To validate the model in the context of existing information systems, the paper describes three cases of big data management in existing companies.

A Study of Big Data Analytical Frameworks in Research Data Management Using Data Mining Techniques

2021 ◽  
pp. 48-67
Author(s):  
Madhavi Arun Vaidya ◽  
Meghana Sanjeeva
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Big Data ◽  
Data Management ◽  
Large Volume ◽  
Research Data ◽  
Research Data Management ◽  
Visualization Tools ◽  
Using Data ◽  
Analytical Frameworks

Research, which is an integral part of higher education, is undergoing a metamorphosis. Researchers across disciplines are increasingly utilizing electronic tools to collect, analyze, and organize data. This “data deluge” creates a need to develop policies, infrastructures, and services in organisations, with the objective of assisting researchers in creating, collecting, manipulating, analysing, transporting, storing, and preserving datasets. Research is now conducted in the digital realm, with researchers generating and exchanging data among themselves. Research data management in context with library data could also be treated as big data without doubt due its properties of large volume, high velocity, and obvious variety. To sum up, it can be said that big datasets need to be more useful, visible, and accessible. With new and powerful analytics of big data, such as information visualization tools, researchers can look at data in new ways and mine it for information they intend to have.

A Framework for Managing Big Data in Enterprise Organizations

2020 ◽  
Vol 12 (1) ◽  
pp. 84-97
Author(s):  
Youssef Ahmed ◽  
Walaa Medhat ◽  
Tarek El Shishtawi
Keyword(s):  
Big Data ◽  
Data Management ◽  
Rapid Growth ◽  
Decision Makers ◽  
Data Service ◽  
Correct Decision ◽  
Management Framework ◽  
Critical Problems ◽  
Business Needs ◽  
Main Components

Big Data management is trending research that seeks to find a framework that will give support to decision makers in governments and enterprises organizations. For the rapid growth of data, dealing with Big Data with respect to management and finding new values has drawn attention recently. Strategies should be established together with the goals, vision, and objectives of an organization to manage Big Data. Big data management frameworks are the main components for the implementation of Big Data service. Many organizations that deals with Big Data have three critical problems, how to manage Big Data, how can Big Data create new values reference to its strategies and business needs, and how it can take the correct decision in the correct time. In this article, the authors propose a Big Data management framework that will handle all Big Data operation beginning with collecting data until making analysis and how new value can be created. The proposed framework also takes care of other factors such as organization strategies, governance, and security.

scholarly journals Research on the Relationship Between EM and NVP – Exploring the Moderating Role of EU

2018 ◽  
Vol 22 (6) ◽  
pp. 853-860
Author(s):  
Lidong Zhu ◽  
Hui Zhang ◽  
◽  
Keyword(s):  
Value Creation ◽  
Customer Value ◽  
New Ventures ◽  
Environmental Uncertainty ◽  
Entrepreneurial Marketing ◽  
Positive Effects ◽  
Using Data ◽  
The Relationship ◽  
Moderating Role

This paper explores the role of entrepreneurial marketing (EM) and environmental uncertainty (EU) in driving new ventures’ performance (NVP). Using data from a survey of 883 small ventures based in Anhui province, China, we find that EM is an important drive of NVP. Only 4 EM dimensions (customer value creation, pro-activeness, innovations, and opportunity-focus) have positive effects on growth performance (GP), and only 5 EM dimensions (customer value creation, pro-activeness, innovations, opportunity-focus, and resource leveraging) have positive effects on profit performance (PP) as well. On the other hand, we found that environmental uncertainty (EU) has a partly moderating role between EM and GP, EU only negatively moderates the relationship between pro-activeness and GP, and EU positively moderates the relationship between opportunity-focus, risk-taking and GP. In addition, EU does not have a moderating role between EM and PP.

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