Traditional Business Intelligence Research Articles

A Generic Functional Architecture for Operational BI System

In recent years, Operational Business Intelligence has emerged as an important trend in the Business Intelligence (BI) market. Majority of BI application architectures are bespoke in nature which have several architectural limitations like tightly coupled, static, historic, subjective, no performance measurement of business processes, limited user access, limited analytical processing, querying and reporting features. In this article, a generic functional architecture for Operational BI systems based on software architecture principles is presented. All functional modules of the system are derived from the key features of the system and by using top down approach of software design principles. The similar functional modules are grouped into sub-systems and a set of these sub-systems constitutes overall functional architecture. The proposed architecture overcomes the limitations of traditional BI architectures.

The impact of supply chain management on business intelligence

This conceptual paper investigates the impact of the supply chain on businessintelligence (BI) in private companies. The article focuses on these two subjects in order tobroadly understand the concept of business intelligence, supply chain and characteristicsimplement such as OLAP, data warehouse or data mining. It looks at the joint advantages ofthe business intelligence and supply chain concepts and revisits the traditional BI concept. Wefound that the supply chain includes many data samples collected from the first supplier to thelast customer, which have to be analysed by the company in order to be more efficient. Basedon these observations the authors argue for why it makes sense to see the BI function as anextension of supply chain management, but moreover they show how difficult it has become toseparate BI from other IT intensive processes in the organization.

Operational intelligence through performance trends: an oracle prototype

Improving business process performance through operational intelligence is essential to enhance an organisation's ability to achieve business objectives and competitive advantage. This paper proposes an extension on insights provided by traditional business intelligence analytics through star schema that goes beyond snapshots of business process performance to outlining details on factors influencing business process operations over a period of time. Such extended insights, referred as performance trends, enable a richer assessment of business process performance. Performance trends essentially allow a business to determine whether the direction of business process performance is going up, down, or staying flat with respect to some success measure over a period of time. It is expressed as an analytic business rule that can be applied on the business process through analytic triggers. The paper illustrates the concepts through a prototype that is adapted from the oracle's e-business suite lead to forecast business process. The prototype is implemented in oracle's PL/SQL language.

Re-evaluating the Definition of Intelligence in Business Intelligence

Objective - Business Intelligence has little bearing with graphs and dashboards of traditionally defined Business Intelligence. Rather it is all about experience and sound judgement of the person at the helm of the decision-making process. In line with this view, we evaluate and subsequently, reposition the current definition of Business Intelligence in the literature. Methodology/Technique - The initial development of the data, information, knowledge and wisdom (DIKW) hierarchy excluded intelligence and so it never questioned the accepted definition of Business Intelligence. The extended DIKIW hierarchy includes intelligence but we raise the question about the definition of intelligence in Business Intelligence. This paper positions the existing definition of Business Intelligence as Business Information instead, and so, it redefines traditional Business Intelligence. Findings – Applying the DIKIW hierarchy, the new definition of Business Intelligence is shown in equation as the transformation of "Business Data to Business Information to Business Knowledge to Business Intelligence to Business Wisdom". Novelty - The impact of the new definition of Business Intelligence is that it changes its meaning from one that belongs to information science into one that is a human behavioural science and profiling concept. It does not do away with the existing work in literature but it redefines Business Intelligence as belonging to the realm of Business Information. Type of Paper - Review Keywords: Business Intelligence; DIKW hierarchy; DIKIW hierarchy; Knowledge Management; Wisdom. JEL Classification: L25, M10

Self-Service Business Intelligence

In recent years, Business Intelligence (BI) systems have undergone two distinctly different fundamental changes. On the one hand, social media systems, machine sensors, devices like smart phones, and other sources generate new data which often differ from traditional operational data regarding their structure, rate of growth, and volume (McAfee and Brynjolfsson 2012). On the other hand, the scope of BI has been extended from strategic questions to operational tasks so that more employees have a need to apply BI (Bohringer et al. 2010). These developments have created more demand for BI reporting and pattern search. In addition, the frequency of requests for changes by businesses has also increased (Yu et al. 2013). Consequently, BI specialists who are either IT professionals or experienced BI users in functional departments (so called power users) has become an even bigger bottleneck than before (Kobielus 2009). Unexperienced users (so called casual users) who need to make time-critical business decisions sometimes act, therefore, without exploiting all available data (Abello et al. 2013). In response to these developments, the approach of Self-Service BI (SSBI) has been suggested (e.g., Imhoff and White 2011). SSBI should, on one hand, empower casual users to perform custom analytics and to derive actionable information from large amounts of multifaceted data without having to involve BI specialists. Power users, on the other hand, can accomplish their tasks with SSBI more easily and quickly than before. Or, in Eckerson’s words (2009): ‘‘users create exactly the reports they want, when they want them...’’. The description of this term does not reveal the difference to the goals of traditional BI systems, but SSBI systems offer new features to accomplish the goals. One new option that will be described below in more detail can serve as an example: in some SSBI applications, business users (casual or power users) are enabled to integrate external data (e.g., from social networks) with data in the data warehouse and analyze them ‘‘on the fly.’’ SSBI also takes advantage of new software architectures as explained below. The desire to let business users access enterprise data has existed for a long time. SQL was partly developed with this goal in mind (Codd 1974). It indeed made the access easier, but mainly for programmers. The next attempt was undertaken with the introduction of data warehouses (Devlin 1996). This enabled power users to retrieve data and build reports or models by themselves. Meanwhile, more users experience the need to analyze data quickly and independently. While the goals of traditional BI have not changed, in SSBI the computing environment and new tools can fulfill promises and meet the users’ needs. The first step in this direction was taken when BI architecture changed from client/server applications to Web applications. In this architecture, client software installations for new users (and updates for old users) are no longer necessary, users can access the resources through an interface Accepted after one revision by Prof. Dr. Sinz.

Big data analytics bring new insights and higher business value - an experiment carried out to divulge sales forecasting solutions

Over the last few years, the volume and the variety of data that we deal has grown enormously from various sources such as social media networks, machine generated data, mobile networks, CRM data etc. The exponential growth of data creates various challenges to the traditional business intelligence BI and analytic solutions. Newer approaches to handle Big data and extract better business value are the topmost priorities for both IT management and business users. In recent years, organisations have made strategic decisions using Big data analytics, which have resulted in better business processes, minimise risks, reduce costs and increase business value in broad-spectrum. In this paper, we epitomise Big data analytics and the promising technologies to analyse Big data. We have also experimented predictive analytics to evaluate the sales forecasting trend and enrich customer relationship management CRM data with all the obtainable insights. This experiment has been executed for a pharmaceutical company using SFDC SalesForce.Com in cloud environment.

SLOD-BI

The tremendous popularity of web-based social media is attracting the attention of the industry to take profit from the massive availability of sentiment data, which is considered of a high value for Business Intelligence (BI). So far, BI has been mainly concerned with corporate data with little or null attention to the external world. However, for BI analysts, taking into account the Voice of the Customer (VoC) and the Voice of the Market (VoM) is crucial to put in context the results of their analyses. Recent advances in Sentiment Analysis have made possible to effectively extract and summarize sentiment data from these massive social media. As a consequence, VoC and VoM can be now listened from web-based social media (e.g., blogs, reviews forums, social networks, and so on). However, new challenges arise when attempting to integrate traditional corporate data and external sentiment data. This paper deals with these issues and proposes a novel semantic data infrastructure for BI aimed at providing new opportunities for integrating traditional and social BI. This infrastructure follows the principles of the Linked Open Data initiative.

Leveraging big-data for business process analytics

Purpose – This paper aims to present a solution that enables organizations to monitor and analyse the performance of their business processes by means of Big Data technology. Business process improvement can drastically influence in the profit of corporations and helps them to remain viable. However, the use of traditional Business Intelligence systems is not sufficient to meet today ' s business needs. They normally are business domain-specific and have not been sufficiently process-aware to support the needs of process improvement-type activities, especially on large and complex supply chains, where it entails integrating, monitoring and analysing a vast amount of dispersed event logs, with no structure, and produced on a variety of heterogeneous environments. This paper tackles this variability by devising different Big-Data-based approaches that aim to gain visibility into process performance. Design/methodology/approach – Authors present a cloud-based solution that leverages (BD) technology to provide essential insights into business process improvement. The proposed solution is aimed at measuring and improving overall business performance, especially in very large and complex cross-organisational business processes, where this type of visibility is hard to achieve across heterogeneous systems. Findings – Three different (BD) approaches have been undertaken based on Hadoop and HBase. We introduced first, a map-reduce approach that it is suitable for batch processing and presents a very high scalability. Secondly, we have described an alternative solution by integrating the proposed system with Impala. This approach has significant improvements in respect with map reduce as it is focused on performing real-time queries over HBase. Finally, the use of secondary indexes has been also proposed with the aim of enabling immediate access to event instances for correlation in detriment of high duplication storage and synchronization issues. This approach has produced remarkable results in two real functional environments presented in the paper. Originality/value – The value of the contribution relies on the comparison and integration of software packages towards an integrated solution that is aimed to be adopted by industry. Apart from that, in this paper, authors illustrate the deployment of the architecture in two different settings.

Letter From the Editor

Letter From the Editor

Why Analytics Will Be the Next Competitive Edge

Analytics simplify data to amplify its value. The power of analytics is to reduce huge volumes of data into revealing and actionable insight. More traditional business intelligence (BI), by contrast, mainly summarized historical data, typically in table reports and graphs. BI consumes stored information, whereas analytics produces new information. © 2015 Wiley Periodicals, Inc.

Towards Software Performance Monitoring: An Approach for the Aerospace Industry

Software applications are becoming one of the most valuable assets for companies, providing critical capabilities and functionalities to perform a wide range of operations in the industry. This paper aims to provide a view on software application portfolio monitoring and its integration into business intelligence systems for aerospace manufacturing companies. The key research question addressed is how critical software has become for aerospace industry and how software applications could be monitored. This question has been addressed by conducting an in depth review of current literature and by interviewing professionals from different aerospace companies. The results are a set of key findings regarding software impact in aerospace industry, and a monitoring proposal based in a traditional business intelligence architecture.By incorporating condition monitoring methodologies into the software application portfolio of the enterprise, benefits in maintenance budget allocation and risk avoidance are expected, thanks to a more precise and agile way of processing business data. Additional savings should be possible through further application portfolio optimisation.

Comparing Business Intelligence and Big Data Skills

While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.

Why Analytics Will Be the Next Competitive Edge

Analytics simplify data to amplify its value. The power of analytics is to reduce huge volumes of data into revealing and actionable insight. More traditional business intelligence (BI), by contrast, mainly summarized historical data, typically in table reports and graphs. BI consumes stored information, whereas analytics produces new information.

Cloud Business Intelligence Survey

are always seeking to become more intelligent and achieve a greater competitive advantage by using Business Intelligence (BI) solutions. The new phenomenon of business intelligence is how businesses handle data. One of the instruments that can make the BI tools more accessible is Cloud Computing. This paper outlines some important definitions of Cloud Computing and Business Intelligence, the importance of business intelligence and some problems in traditional business intelligence solutions. Also, the benefits and challenges of Cloud Business Intelligence is discussed with a comparison between public and private cloud and how to choose the appropriate Cloud Business Intelligence for an organization.

Agile BI � The Future of BI

In rapidly changing economy, Business Intelligence solutions have to become more agile. This paper attempts to discuss some questions which help in creating agile BI solution such as: What is Agile? Why agile is so well suited for BI? Which are key elements that promote agile BI solution? Also, paper briefly looks at technologies that can be used for enabling agile BI solution.Keywords: Agile Business Intelligence, Agile Business Analytics, Agile Development Methodologies, In-Memory Bi Approaches, Data Virtualization Server1 IntroductionBusiness Intelligence (BI) was defined in different ways. The Data-Warehousing Institute has defined Business Intelligence as the tools, technologies and processes required to turn data into information and information into knowledge and plans that optimize business actions [6], Turban has defined BI as a broad category of applications and techniques for gathering, storing, analyzing and providing access to data to help enterprise user make better business and strategic decisions. [23], The range of capabilities that can be defined as business intelligence is very broad. The spectrum of BI technologies is presented in [16].Most enterprises have hundreds of internal and external data sources such as: databases, e-mail archives, file systems, spreadsheets, digital images, audio files and more. 80% of organizational data are unstructured and semi-structured data.Traditional Business Intelligence systems use small fraction of all data available. Also, traditional BI systems use only structured The core components of traditional BI architecture are: ETL tools, enterprise data warehouse with metadata repository and business analytics (Figure 1).Traditional BI systems use ETL tools for extracting data from multiple sources and temporarily storing those datasets at staging area. Organizations use data warehouses to aggregate cleaned and structured Business analytics/BI tools include enterprise reporting tools, ad hoc query tools, statistical analysis tools, OLAP tools, spatial-OLAP analysis tools, dashboards, scorecards and advanced analytics. Advanced analytics typically refer to: data mining tools, text mining tools, predictive analytics, artificial intelligence, and natural language processing. But architecture is unable to get adapted to change. A study by Aberdeen Group [24] showed that this style ofBI is predominantly controlled, driven and delivered by corporate IT. Often, only static views of data are available and any changes or enhancements must be made by IT organization. These characteristics are in contradiction with frequently changing business and big data. Big data typically refers to following types of data: semi-structured data (XML and similar standards), unstructured data, Web data (social data, Web logs) and real-time data (event data, spatial data, machine-generated data).Table 1 briefly summarizes main disadvantages of traditional BI systems.How to eliminate these problems? By building agile BI solution. A study by TDWI Research [22] showed that many traditional business intelligence systems are not agile:* 33% of organizations needed more than three months to add new data source to existing business intelligence system* developing complex report or dashboard with about 20 dimensions, 12 measures and 6 user access rides took on average 7 weeks in 2011The next section presents briefly concept of agile BI and key elements that together promote agile BI solution.2 Agile BIAgile means ability to be adaptable. Agile BI was defined in different ways. The Forrester Research defines agile BI as an approach that combines processes, methodologies, organizational structure, tools and technologies that enable strategic, tactical and operational decision -makers to be more flexible and more responsive to fast pace of changes to business and regulatory requirements [9], According to Data Warehousing Institute agile BI addresses broad need to enable flexibility by accelerating time it takes to deliver value with BI projects. …

Perspectives on Business Intelligence

In the 1980s, traditional Business Intelligence (BI) systems focused on the delivery of reports that describe the state of business activities in the past, such as for questions like "How did our sales perform during the last quarter?" A decade later, there was a shift to more interactive content that presented how the business was performing at the present time, answering questions like "How are we doing right now?" Today the focus of BI users are looking into the future. "Given what I did before and how I am currently doing this quarter, how will I do next quarter?"

BI-PaaS: parallel-based business intelligence system

Concerning the challenge of massive information to the traditional Business Intelligence(BI) system,the prototype of a BI system(BI-PaaS) based on parallel mechanism was designed and implemented.The structure was built upon the project named big cloud in China mobile,being powered by massively parallel computing and distributed storage and integrated with several technologies in terms of ETL,DM,OLAP,Report,etc.The results of the experiment prove that the function based upon parallel computing greatly raises the competency of data processing and effectively supports the data analysis.

A multidimensional data model using the fuzzy model based on the semantic translation

With the rapid development of Web 2.0 sites such as Blogs and Wikis users are encouraged to express opinions about certain products, services or social topics over the web. There is a method for aggregating these opinions, called Opinion Aggregation, which is made up of four steps: Collect, Identify, Classify and Aggregate. In this paper, we present a new conceptual multidimensional data model based on the Fuzzy Model based on the Semantic Translation to solve the Aggregate step of an Opinion Aggregation architecture, which allows exploiting the measure values resulting from integrating heterogeneous information (including unstructured data such as free texts) by means of traditional Business Intelligence tools. We also present an entire Opinion Aggregation architecture that includes the Aggregate step and solves the rest of steps (Collect, Identify and Classify) by means an Extraction, Transformation and Loading process. This architecture has been implemented in an Oracle Relational Database Management System. We have applied it to integrate heterogeneous data extracted from certain high end hotels websites, and we show a case study using the collected data during several years in the websites of high end hotels located in Granada (Spain). With this integrated information, the Data Warehouse user can make several analyses with the benefit of an easy linguistic interpretability and a high precision by means of interactive tools such as the dashboards.

Predefined Three Tier Business Intelligence Architecture in Healthcare Enterprise

Business Intelligence (BI) has caused extensive concerns and widespread use in gathering, processing and analyzing data and providing enterprise users the methodology to make decisions. Different from traditional BI architecture, this paper proposes a new BI architecture, Top-Down Scalable BI architecture with defining mechanism for enterprise decision making solutions and aims at establishing a rapid, consistent, and scalable multiple applications on multiple platforms of BI mechanism. The two opposite information flows in our BI architecture offer the merits of having the high level of organizational prospects and making full use of the existing resources. We also introduced the avg-bed-waiting-time factor to evaluate hospital care capacity.

Using Dashboard for Lean Revenue Cycle Management

Lean methodologies are increasingly deployed by healthcare providers to improve billing and collection process in revenue cycle management. Measurement on key performance metrics generates large amount of data. Traditional tabular and spreadsheet form analytics are not efficient to support enterprise scale lean improvement sustainability. This paper introduces a new breed of visual dashboard technology and its value as emerging differentiator from traditional business intelligence, especially in the rapid deployment and usability perspective. We present an exemplary case of using QlikView dashboard for lean revenue cycle management. Initial evaluation result is promising and indicates the dashboard empowers end users with intuitive and interactive experience, ad-hoc questions and answers. Integrating performance dashboard with enterprise data governance, predictive modeling will be evaluated further in future study.