Self-service business intelligence and analytics application scenarios: A taxonomy for differentiation

Self-service business intelligence and analytics (SSBIA) empowers non-IT users to create reports and analyses independently. SSBIA methods and processes are discussed in the context of an increasing number of application scenarios. However, previous research on SSBIA has made distinctions among these scenarios only to a limited extent. These scenarios include a wide variety of activities ranging from simple data retrieval to the application of complex algorithms and methods of analysis. The question of which dimensions are suitable for differentiating SSBIA application scenarios remains unanswered. In this article, we develop a taxonomy to distinguish among SSBIA applications more effectively by analyzing the relevant scientific literature and current SSBIA tools as well as by conducting a case study in a company. Both researchers and practitioners can use this taxonomy to describe and analyze SSBIA scenarios in further detail. In this way, the opportunities and challenges associated with SSBIA application can be identified more clearly. In addition, we conduct a cluster analysis based on the SSBIA tools thus analyzed. We identify three archetypes that describe typical SSBIA tools. These archetypes identify the application scenarios that are addressed most frequently by SSBIA tool providers. We conclude by highlighting the limitations of this research and suggesting an agenda for future research

Challenges and drivers for data mining in the AEC sector

Purpose: This paper explores the current challenges and drivers for data mining in the AEC sector. Design/methodology/approach: Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics. Findings: The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals. Originality/value: With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and big data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for Data Mining in the AEC sector with a practical focus. Keywords: Business analytics, Data Mining, Data Analytics, AEC, Facilities Managemen

Technology Selection for Big Data and Analytical Applications

The term Big Data has become pervasive in recent years, as smart phones, televisions, washing machines, refrigerators, smart meters, diverse sensors, eyeglasses, and even clothes connect to the Internet. However, their generated data is essentially worthless without appropriate data analytics that utilizes information retrieval, statistics, as well as various other techniques. As Big Data is commonly too big for a single person or institution to investigate, appropriate tools are being used that go way beyond a traditional data warehouse and that have been developed in recent years. Unfortunately, there is no single solution but a large variety of different tools, each of which with distinct functionalities, properties and characteristics. Especially small and medium-sized companies have a hard time to keep track, as this requires time, skills, money, and specific knowledge that, in combination, result in high entrance barriers for Big Data utilization. This paper aims to reduce these barriers by explaining and structuring different classes of technologies and the basic criteria for proper technology selection. It proposes a framework that guides especially small and mid-sized companies through a suitable selection process that can serve as a basis for further advances

A review and future direction of agile, business intelligence, analytics and data science

Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

Towards better organizational analytics capability:a maturity model

Abstract. Data and analytics are changing the markets. Significant improvements in competitiveness can be achieved through utilizing data and analytics. Data and analytics can be used to support in all levels of decision making from operational to strategic levels. However, studies suggest that organizations are failing to realize these benefits. Many of the analytics initiatives fail and only a small partition of organizations’ data is used in decision making. This happens mostly because utilizing data and analytics in larger scale is a difficult and complex matter. Companies need to harness multiple resources and capabilities in a business context and use them synergistically to deliver value. Capabilities must be developed step by step and cannot be bought. Bottlenecks like siloed data, lack of commitment and lack of understanding slow down the development. The focus of this thesis is to gain insight on how these resources and capabilities can be managed and understood better to pursue a position where modern applications of data and analytics could be utilized even better. The study is conducted in two parts. In the first part, the terminology, disciplines, analytics capabilities, and success factors of data and analytics development are examined through the literature. Then a comprehensive tool for identifying and reviewing these analytics capabilities is built through analyzing and combining existing tools and earlier insights. This tool, organizational analytics maturity model, and other findings are then reviewed and complemented with empirical interviews. The main findings of this thesis were mapped analytics capabilities, success factors of analytics, and the organizational analytics maturity model. These results help practitioners and researchers to better understand the complexity of the subject and what dimensions must be taken into account when pursuing success with data and analytics.Kohti parempaa organisaation analytiikkakyvykkyyttä : maturiteettimalli. Tiivistelmä. Datan ja analytiikka muuttaa eri organisaatioiden välistä kilpailua. Huomattavia parannuksia kilpailukyvyssä voidaan saada aikaan oikeanlaisella datan ja analytiikan hyödyntämisellä. Data ja analytiikkaa voidaan käyttää kaikilla päätöksen teon asteilla operatiivisista päätöksistä strategiselle tasolle asti. Tästä huolimatta tutkimukset osoittavat, että organisaatiot eivät ole onnistuneet saavuttamaan näitä hyötyjä. Monet analytiikka-aloitteet epäonnistuvat ja vain pientä osaa yritysten keräämästä datasta hyödynnetään päätöksenteossa. Tämä johtuu pääosin siitä, että datan ja analytiikan hyödyntäminen isossa kontekstissa on vaikeaa ja monimutkaista. Organisaatioiden täytyy valjastaa useita resursseja ja kyvykkyyksiä liiketoimintakontekstissa ja käyttää näitä synergisesti tuottaakseen arvoa. Näitä kyvykkyyksiä ei voida ostaa suoraan, vaan ne joudutaan asteittain kehittämään osaksi organisaatiota. Kehitykseen liittyy myös paljon ongelmakohtia, jotka hidastavat kokonaiskehitystä. Siiloutunut data ja sitoutumisen ja ymmärryksen puute ovat esimerkkejä kehityksen kompastuskivistä. Tämän opinnäytteen tarkoitus on syventää ymmärrystä siitä, miten näitä resursseja ja kyvykkyyksiä hallitaan ja ymmärretään paremmin. Miten organisaatio pääsee tilaan, jossa se voi hyödyntää moderneja datan ja analytiikan mahdollisuuksia? Tutkimus muodostuu kahdesta osasta. Ensimmäisessä osassa käsitellään terminologia, analytiikkakyvykkyydet ja niiden menestystekijät. Sen jälkeen luodaan kokonaisvaltainen työkalu, organisaation analytiikkamaturiteettimalli, kyvykkyyksien tunnistamiseksi ja kehittämiseksi. Tämä malli rakennetaan ensimmäisten löydösten pohjalta. Tutkimuksen toisessa osassa aiemmat löydökset ja rakennettu malli validoidaan ja täydennetään empiirisillä haastatteluilla. Tämän työn päälöydökset ovat kartoitetut analytiikkakyvykkyydet, niiden menestystekijät ja organisaation analytiikkamaturiteettimalli. Nämä löydökset auttavat ammattilaisia ja tutkijoita ymmärtämään paremmin aiheen monimutkaisuuden ja mitä dimensioita tulee ottaa huomioon, kun pyritään menestykseen datan ja analytiikan avulla

How Do Different Types of BA Users Contribute to Business Value?

To explain how different types of business analytics (BA) users contribute to business value, we propose a new variance model called organizational benefits from business analytics use (OBBAU). The model captures three key mechanisms through which two distinct types of BA users drive organizational benefits: 1) data scientists providing advisory services, 2) end users using BA tools, and 3) both data scientists and end users creating and enhancing BA tools. To build the OBBAU, we thoroughly reviewed the BA and IS literatures and interviewed 15 BA experts

Combining Process Guidance and Industrial Feedback for Successfully Deploying Big Data Projects

Companies are faced with the challenge of handling increasing amounts of digital data to run or improve their business. Although a large set of technical solutions are available to manage such Big Data, many companies lack the maturity to manage that kind of projects, which results in a high failure rate. This paper aims at providing better process guidance for a successful deployment of Big Data projects. Our approach is based on the combination of a set of methodological bricks documented in the literature from early data mining projects to nowadays. It is complemented by learned lessons from pilots conducted in different areas (IT, health, space, food industry) with a focus on two pilots giving a concrete vision of how to drive the implementation with emphasis on the identification of values, the definition of a relevant strategy, the use of an Agile follow-up and a progressive rise in maturity

Big Data Ethics in Research

The main problems faced by scientists in working with Big Data sets, highlighting the main ethical issues, taking into account the legislation of the European Union. After a brief Introduction to Big Data, the Technology section presents specific research applications. There is an approach to the main philosophical issues in Philosophical Aspects, and Legal Aspects with specific ethical issues in the EU Regulation on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (Data Protection Directive - General Data Protection Regulation, "GDPR"). The Ethics Issues section details the specific aspects of Big Data. After a brief section of Big Data Research, I finalize my work with the presentation of Conclusions on research ethics in working with Big Data. CONTENTS: Abstract 1. Introduction - 1.1 Definitions - 1.2 Big Data dimensions 2. Technology - 2.1 Applications - - 2.1.1 In research 3. Philosophical aspects 4. Legal aspects - 4.1 GDPR - - Stages of processing of personal data - - Principles of data processing - - Privacy policy and transparency - - Purposes of data processing - - Design and implicit confidentiality - - The (legal) paradox of Big Data 5. Ethical issues - Ethics in research - Awareness - Consent - Control - Transparency - Trust - Ownership - Surveillance and security - Digital identity - Tailored reality - De-identification - Digital inequality - Privacy 6. Big Data research Conclusions Bibliography DOI: 10.13140/RG.2.2.11054.4640