# Conceptual Framework for Implementing Temporal Big Data Analytics in Companies
## 2 Background for Big Data Analytics in Companies--Literature Review  
Researchers have been interested in the potential of advanced analytics and big data analytics for achieving a competitive edge for some years [18; 22; 23]. Time is also commonly acknowledged as a crucial factor in corporate operations [24; 25]. Nonetheless, the time dimension has not been pushed to the center in studies of big data analytics or big data regulations. Either the practical and strategic applications of big data [26] or the processing of big data using proven information technology [27; 28] are the primary foci of scholarly literature. In order to make use of big data insights, some big data strategies have been proposed, e.g., by [PERSON] [28] and by [PERSON] [29]. The former is a cycle that includes the following steps at regular intervals:  
* Formulating a business strategy in conjunction with the requirements for big data analytics;
* Identifying the business initiatives that will implement the strategy;
* Determining the outcomes;
* Identifying the critical success factors;
* Identifying the primary data sources that will be used to support the strategy and Initiatives. With the latter comes the concept of a big data pipeline, which is essentially a series of steps that are repeated again and over. This pipeline (created during the big data implementation process) includes:  
* Big data acquisition and storage;
* Big data cleansing and enrichment;
* Big data mining;
* Big data results dissemination and management, and thus resembles the well-known steps of the knowledge discovery process. Big data transformation processes and established business goals inform the pipeline's design. Because of this, [PERSON]'s overall approach to big data is hazy at best. Up till now, there has not been proposed any general methodological or conceptual framework for the BDA implementation in organizations (and even less so for TBDA). This is most probably due to the fact that researchers have always been focusing on specific BDA tasks, such as supporting innovations or competitive advantage. For example, the question of BDA for innovations is discussed in [22; 30; 31]. [PERSON] and [PERSON] [22] develop a framework for service innovations, consisting of service ecosystems, service platforms, and value co-creation through the integration of resources, including big data ones. [PERSON] and [PERSON] [30] consider the process of service innovations, distinguishing three innovation layers: the IT technology, the process, and the business. In the discussion on innovations, [PERSON] [31] points to the role of organizational culture, knowledge management, analytical performance monitoring, and IT infrastructure. He stresses the need to measure the effectiveness of the innovation ecosystem by appropriately designing Key Performance Indicators (KPIs). The suitable KPIs could be, among others, market share, cost reduction, scale, and durability of implemented innovations. The big data process for the healthcare domain is discussed in [32]; the idea is to extend the well-known cloud solutions (IaaS, PaaS, SaaS) with Data-Mining-as-a-Service (DMaaS) and Decision-Science-as-a-Service (DSaaS) to analyze distributed data, among which big data stand out. [PERSON] et al. [33] consider temporal event tracing in big healthcare data analytics. The new, patient-driven big data architecture is proposed, based on the NoSQL paradigm. The solution is focused strictly on the technical and processing side of temporal big data with no reference to implementation perceived as a company-wide process. Similarly, [PERSON] et al. [34] present a temporal data science algorithm for analyzing big COVID-19 epidemiological data, focusing on temporal data analytics with ubiquitous computing. No implementation framework is presented. The practice-based view of business transformation is discussed in [35]. This research reveals causal relationships between the BDA and IT platform from one side, and business benefits and business value from the other.