Are They Ready for the Big Thing?

2021 ◽  
pp. 200-210
Author(s):  
Matthias Lederer ◽  
Juluis Lederer
Keyword(s):  
Big Data ◽  
Data Management ◽  
Process Management ◽  
Business Processes ◽  
Future Trend ◽  
Data Driven ◽  
Big Data Applications ◽  
All Solutions ◽  
Management Approaches ◽  
And Control

Data-driven business processes management (BPM) is regarded as a central future trend because automation often makes huge amounts of data (big data) available for the optimisation and control of workflows. Software manufacturers also use this trend and call their solutions big data applications, even if some features are reminiscent of traditional data management approaches. This chapter derives from the basic definitions of big data including 13 central requirements that a big data BPM solution must meet in order to be described as such. One hundred twenty-one process management solutions are evaluated on the basis of these to determine whether they are real big data applications. As a result, less than 5% of all solutions analysed meet all requirements.

scholarly journals DaLiF: a data lifecycle framework for data-driven governments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis
Keyword(s):  
Big Data ◽  
Data Management ◽  
Good Governance ◽  
Business Community ◽  
Data Driven ◽  
Governmental Organizations ◽  
Data Lifecycle ◽  
The Government ◽  
Data Ecosystem ◽  
Governance Transparency

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

scholarly journals Big data-driven stochastic business planning and corporate valuation

2018 ◽  
Vol 15 (3-1) ◽  
pp. 189-204 ◽  
Author(s):  
Roberto Moro Visconti ◽  
Giuseppe Montesi ◽  
Giovanni Papiro
Keyword(s):  
Big Data ◽  
Research Question ◽  
Predictive Ability ◽  
Cash Flows ◽  
Data Driven ◽  
Business Planning ◽  
Information Asymmetries ◽  
Methodological Innovation ◽  
Set Up ◽  
And Control

The research question of this paper is concerned with the investigation of the links between Internet of Things and related big data as input parameters for stochastic estimates in business planning and corporate evaluation analytics. Financial forecasts and company appraisals represent a core corporate ownership and control issue, impacting on stakeholder remuneration, information asymmetries, and other aspects. Optimal business planning and related corporate evaluations derive from an equilibrated mix of top-down and bottom-up approaches. While the former follows a traditional dirigistic methodology where companies set up their strategic goals, the latter are grass-rooted with big data-driven timely evidence. Real options can be embedded in big data-driven forecasting to make expected cash flows more flexible and resilient, improving Value for Money of the investment and reducing its risk profile. More accurate and timely big data-driven predictions reduce uncertainties and information asymmetries, making risk management easier and decreasing the cost of capital. Whereas stochastic modeling is traditionally used for budgeting and business planning, this probabilistic process is seldom nurtured by big data that can refresh forecasts in real time, improving their predictive ability. Combination of big data and stochastic estimates for corporate appraisal and governance issues represents a methodological innovation that goes beyond the traditional literature and practice.

scholarly journals Characterizing Big Data Management

10.28945/2192 ◽  
2015 ◽  
Author(s):  
Rogério Rossi ◽  
Kechi Hirama
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Big Data ◽  
Data Management ◽  
Process Management ◽  
Three Dimensions ◽  
Storage And Retrieval ◽  
Human Aspects ◽  
Business Approach ◽  
Support Decision Making

[The final form of this paper was published in the journal Issues in Informing Science and Information Technology.] Considering that big data is a reality for an increasing number of organizations in many areas, its management represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial dimensions to facilitate the management of big data in any organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management must be supported by technology, people and processes; hence, this article discusses these three dimensions: the technologies for storage, analysis and visualization of big data; the human aspects of big data; and, in addition, the process management involved in a technological and business approach for big data management.

Interoperable Process Engineering System for Collaborative Optimization of Product Life-Cycle

2014 ◽  
pp. 166-191 ◽  
Author(s):  
Alberto Armijo ◽  
Mikel Sorli
Keyword(s):  
Life Cycle ◽  
Information Systems ◽  
Data Management ◽  
Product Life Cycle ◽  
Process Management ◽  
Business Processes ◽  
Collaborative Optimization ◽  
Market Conditions ◽  
Product Life ◽  
Business Agility

Most of the industrial organizations, including SMEs, need to quickly react and adapt to the changing market conditions imposed by globalization, such as new sustainability directives or new type of customers. The fulfillment of these requirements on time is a must so as to remain competitive in the global markets. Since data management information systems are already present in almost all the corpus of industrial enterprises as custom developments or standard PLM solutions, the natural technical evolution that aims to provide an effective answer to these changing market conditions comprises the shifting from a data management perspective towards a process management view. Hence, the challenge is how to manage business processes that build upon existing information systems so as to encourage business agility, efficiency, and interoperability. The proposed approach roots on the Business Process Management (BPM) discipline and leverages process optimization through the systematic modeling and reengineering of business processes accompanied by supporting interoperable and configurable eco-services, which are conceived as sustainability-aware services designed to optimize some aspects of the product life-cycle through eco-constraints management.

Data Analysis Services Related to the IoT and Big Data

Web Services ◽  
2019 ◽  
pp. 882-903
Author(s):  
Izabella V. Lokshina ◽  
Barbara J. Durkin ◽  
Cees J.M. Lanting
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Business Models ◽  
Third Party ◽  
Data Driven ◽  
Easy Access ◽  
Provision Of Services ◽  
And Control ◽  
Measurement And Control ◽  
The Internet Of Things

The Internet of Things (IoT) provides the tools for the development of a major, global data-driven ecosystem. When accessible to people and businesses, this information can make every area of life, including business, more data-driven. In this ecosystem, with its emphasis on Big Data, there has been a focus on building business models for the provision of services, the so-called Internet of Services (IoS). These models assume the existence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated by any party. Different business models may support opportunities that generate revenue and value for various types of customers. This paper contributes to the literature by considering business models and opportunities for third-party data analysis services and discusses access to information generated by third parties in relation to Big Data techniques and potential business opportunities.

Semantic Policies for Modeling Regulatory Process Compliance

2011 ◽  
pp. 311-336
Author(s):  
Marwane El Kharbili ◽  
Elke Pulvermueller
Keyword(s):  
Process Management ◽  
Business Processes ◽  
Regulatory Compliance ◽  
Information Systems Management ◽  
Policy Modeling ◽  
Compliance Requirements ◽  
Compliance Checking ◽  
Process Compliance ◽  
Abstraction Levels ◽  
And Control

Business process management (BPM) as a paradigm for enterprise planning and governance is nowadays a core discipline of information systems management. Growing up from the first process re-engineering initiatives in the 1980’s, BPM technologies now seek to span all of the organizational silos of enterprises, and also expand vertically from the strategy layers where visions and goals are defined to the lower data transaction layers. Ensuring the compliance of processes to the guidance and control provided to the business by regulations is an obligation to every enterprise. In this work, we motivate the need for automation in compliance management and propose the use of policies as a modeling concept for regulations. We introduce the CASE model for structuring regulatory compliance requirements as policies. Policies shall allow to model regulations at abstraction levels adequate to implementing platform independent mechanisms for policy verification. We describe the CASE model and explain how it can be used to structure and model policies extracted from regulations. This chapter also defines a policy modeling ontology that we propose as a language for formally modeling CASE policies. The basic CASE model and the corresponding policy modeling ontology support compliance of enterprise processes to regulations by enabling automation to compliance checking (verification). The utilization of the CASE method as well as the policy ontology is showcased using an example of resource access control in business processes.

scholarly journals Big data and risk management in business processes: implications for corporate real estate

2019 ◽  
Vol 26 (5) ◽  
pp. 1141-1155 ◽  
Author(s):  
Enrico Battisti ◽  
S.M. Riad Shams ◽  
Georgia Sakka ◽  
Nicola Miglietta
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Big Data ◽  
Real Estate ◽  
Business Strategy ◽  
Process Management ◽  
Business Processes ◽  
Content Type ◽  
Data Intensive ◽  
Corporate Real Estate

Purpose The purpose of this paper is to improve understanding of the integration between big data (BD) and risk management (RM) in business processes (BPs), with special reference to corporate real estate (CRE). Design/methodology/approach This conceptual study follows, methodologically, the structuring inter-textual coherence process – specifically, the synthesised coherence tactical approach. It draws heavily on theoretical evidence published, mainly, in the corporate finance and the business management literature. Findings A new conceptual framework is presented for CRE to proactively develop insights into the potential benefits of using BD as a business strategy/instrument. The approach was found to strengthen decision-making processes and encourage better RM – with significant consequences, in particular, for business process management (BPM). Specifically, by recognising the potential uses of BD, it is also possible to redefine the processes with advantages in terms of RM. Originality/value This study contributes to the literature in the fields of real estate, RM, BPM and digital transformation. To the best knowledge of authors, although the literature has examined the concepts of BD, RM and BP, no prior studies have comprehensively examined these three elements and their conjoint contribution to CRE. In particular, the study highlights how the automation of data-intensive activities and the analysis of such data (in both structured and unstructured forms), as a means of supporting decision making, can lead to better efficiency in RM and optimisation of processes.

Big data analysis on the business process and management for the store layout and bundling sales

2019 ◽  
Vol 25 (7) ◽  
pp. 1783-1801 ◽  
Author(s):  
Shu-hsien Liao ◽  
Yi-Shan Tasi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Business Process ◽  
Process Management ◽  
Business Processes ◽  
Analysis Data ◽  
Database System ◽  
Big Data Analysis ◽  
Content Type

Purpose In the retailing industry, database is the time and place where a retail transaction is completed. E-business processes are increasingly adopting databases that can obtain in-depth customers and sales knowledge with the big data analysis. The specific big data analysis on a database system allows a retailer designing and implementing business process management (BPM) to maximize profits, minimize costs and satisfy customers on a business model. Thus, the research of big data analysis on the BPM in the retailing is a critical issue. The paper aims to discuss this issue. Design/methodology/approach This paper develops a database, ER model, and uses cluster analysis, C&R tree and the a priori algorithm as approaches to illustrate big data analysis/data mining results for generating business intelligence and process management, which then obtain customer knowledge from the case firm’s database system. Findings Big data analysis/data mining results such as customer profiles, product/brand display classifications and product/brand sales associations can be used to propose alternatives to the case firm for store layout and bundling sales business process and management development. Originality/value This research paper is an example to develop the BPM of database model and big data/data mining based on insights from big data analysis applications for store layout and bundling sales in the retailing industry.

Object-Aware Business Processes

2011 ◽  
Vol 2 (2) ◽  
pp. 19-46 ◽  
Author(s):  
Vera Künzle ◽  
Barbara Weber ◽  
Manfred Reichert
Keyword(s):  
Process Modeling ◽  
Process Management ◽  
Business Processes ◽  
Data Driven ◽  
Process Support ◽  
Management Technology ◽  
It Support ◽  
Business Objects ◽  
Knowledge Intensive

Despite the increasing maturity of process management technology not all business processes are adequately supported by it. Support for unstructured and knowledge-intensive processes is missing, especially since they cannot be straight-jacketed into predefined activities. A common characteristic of these processes is the role of business objects and data as drivers for process modeling and enactment. This paper elicits fundamental requirements for effectively supporting such object-aware processes; i.e., their modeling, execution, and monitoring. Imperative, declarative, and data-driven process support approaches are evaluated and how well they support object-aware processes are investigated. A tight integration of process and data as major steps towards further maturation of process management technology is considered.

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