Frontiers of Business Intelligence and Analytics
To understand definitions regarding the taxonomy of BI, read this paper, where an example of the methodology in the research process is used. It also discusses how the taxonomy for BI and analysis was developed, how it is applied, and an analysis of the current status with predicted development for the next wave or 3.0 of BI, as well as potential gaps. A clear diagram of the taxonomy development process is shown in Figure 6. While a picture is worth a thousand words, sometimes you must explain complex processes narratively.
Business intelligence and analytics
BI & A evolution
Davenport describes BI as software that is used to query and report data in data warehouses. Since the early 1950s, the term intelligence is often used in the context of artificial intelligence. The term "business analytics" was born to represent key analytical capabilities of an organization. Nowadays, analytical skills are tied with the capability to analyze large amounts of heterogeneous data. Chen et al. as well as Davenport divide the evolution of BI & A into three major phases, whose characteristics are depicted in Fig. 1.
Fig. 1 Characteristics of BI & A evolution
BI & A 1.0 begins in the early 1970s and covers primarily the analysis of structured data. Applications in the first evolution phase focus on extraction, transformation, and loading (ETL) processes to select decision relevant data from transactional
systems and bring them into the right format for analyses . To store the collected data, companies implement relational database management systems (RDBMS) and data warehouses. Analysis technologies applied in the first evolutionary phase comprise
mainly statistical methods. Since the 1980s data mining techniques are additionally applied to analyze the data. Online analytical processing (OLAP) applications enable an intuitive and simple data analysis via reports and dashboards.
Since the accelerating rise of the internet and the web in the early 2000s, new ways of collecting and analyzing unstructured and social media data came up. In particular, the appearance of user-generated content and web analytics, collected through Web 2.0 applications, are drivers for the second evolutionary phase of BI & A. These new opportunities of collecting and analyzing customer feedback and opinion data enables new innovative business models, such as user-centered advertisement in Facebook or the reveal of user's purchasing patterns in Google analytics. In addition to the DBMS-based systems of BI & A 1.0, systems of BI & A 2.0 require mature text mining, web mining, and social network analysis capabilities.
BI & A 3.0 focus on the analysis of unstructured data from mobile devices and sensor data. Sensor-based devices, which are connected to the internet and equipped e.g. with RFID or radio tags, enable "location-aware, person-centered, and context-relevant operations and transactions". BI & A 3.0 focus on analyzing these vast amount of sensor-generated data. The analysis of data, generated by cyber-physical systems, data of mobile devices, such as the development of situation-aware data mining applications, and position related data, such as the analysis of using location-based services are examples for research in this third evolutionary phase of BI & A.
Recent analyses of BI & A research
Several literature reviews about BI & A and big data have been published in the past decade. In the following, we introduce them and classify these reviews according to their addressed BI & A evolution phase(s) as well as the applied search strategy (unstructured, semi-structured, and structured approach), as depicted in Fig. 2.
Fig. 2 Current BI & A literature reviews
The state of the art analyses from Kowalczyk et al. as well as Shollo and Kautz mainly address the evolution stage BI & A 1.0. Solely the work of Trieu additionally addresses the second BI & A evolution phase. All other reviewed literature analyses mainly focus on artifacts in the area of BI & A 2.0. From a methodological perspective, solely Kowalczyk et al. apply a structured literature search method with the methodological rigor search approach according to Webster and Watson. However, even the structured literature review of Kowalczyk et al. solely focuses on BI & A 1.0 research results. All other literature reviews apply an unstructured or a semi-structured approach, which might lead to either an uncompleted scope or a missing concept. Both, completeness and concept usage, are basic requirements for a high-quality review. Despite the high number of literature analyses in the last years, to our best knowledge, we could not find a study that systematically analyzes research results of the third evolution phase of BI & A. Furthermore, a valid and applicable taxonomy for such research work is missing. The paper at hand aims at filling this research gap and shed light on BI & A 3.0 research results of the past decade.