# On a Certain Research Gap in Big Data Mining for Customer Insights
## Theoretical Lens
### Customer Insights--The Prelude
#### 2.1.3 Other Technologies Helpful with Obtaining Customer Insights  
In parallel to CRM tools, business intelligence (BI), along with data warehousing (DW) as a key BI technology [46], play a key role in building relationships with customers. Their role was particularly evident before the comprehensive and widespread use of big data, when the algorithms and methods used in BI and data warehouses had to be transformedto meet the new standards rooted in Web 3.0 (like cloud services or data analytics) [47; 48]. The very idea of BI is to understand customer behavior and forecast their purchasing patterns to optimize the business and to maximize the organization's value [49; 50].  
Business intelligence along with data warehousing is widely used in various business and social fields [51; 52; 53]. However, the use of BI as an independent approach for the organizational development is noticeable mainly in the case of the entities where the inflow of new data (including customers) can be handled using conventional tools, i.e., without the use of approaches used in big data dispensing. Data warehouses, despite the possibility of access from heterogeneous sources, mainly operate within one organization with access to historical data. They operate merely based on structured data, due to their database foundations [54]. This can be named the \"atemporal approach\" defined in the proprietary Temporal Big Data Maturity Model (TBDMM), which constitute the lowest level of organization maturity in the context of readiness to use big data [16; 55]. At this level, the organization uses only static knowledge and multi-dimensional data models but scarcely uses BI tools with data warehouses, not big data. Therefore, temporal inference is not possible, despite the presence of scarce time stamps.  
It is also seen in terms of customer insights investigation. The most important pillars of customer insights are data (beyond the transactional data on which traditional CRM leans) but also customer profiling, social media participation, and sentiment analysis [56]. The latter has been the subject of many studies, such as that of [57], which concerned obtaining the opinions of Twitter users (i.e., in fact translating into knowledge derived from them) based on publicly available databases from this portal. In the study, there is a time range in which the study was conducted (i.e., data from a specific period was collected), and data from a social network were used. Nonetheless, the analyzed data was structured (from a specific source); hence, the analyzes were simplified. A similar situation can be described in [58], which consisted in segmenting customer groups based on their opinions (discussions) from Twitter based on a semantic approach (i.e., using patterns and clustering methods such as \(k\)-means).  
Willing to discuss analyzes and interpretations in a temporal context (including inference), there is a need to develop the big data theory (which is what Section 2.2 serves for) to understand the higher-level approaches according to the TBDMM model mentioned above.