Dashboard in tableau
Dashboard can be considered to be a set of numerous views that helps in comparing varied data simultaneously. For instance, in case if there remains a set of outlooks that it is reviewed on a daily basis, then this can generate a dashboard that can display all the visions all at once, rather than find the way to divide worksheets.
Just like worksheets, it is possible to gain access to dashboards from specific tabs present underneath a workbook (Narra et al. 2018). In essence, data present in different sheets as well as dashboards is linked. Again, when a sheet is changed, any kind of dashboards that contains the same also alters, and vice versa. In addition to this, both the sheets along with dashboards revise with the most recent data obtainable from the source of data.
A properly-designed dashboard can bring into line attempts of enterprises, assist reveal important insights, and accelerate process of decision-making (Narra et al. 2018). This can help in understanding functions and audiences, influencing the most viewed mark, designing for the real world and restricting total number of views.
Data mining in knime
Data mining refers to a specific practice of investigating large sized pre-subsisting databases that can help in generating novel information. In essence, this data mining can be considered to be a sorting procedure that utilizes large sets of data for identifying specific patterns and instituting associations to resolve issues by means of data analysis. Thus, this process of data mining can be employed by different firms to convert raw data into effectual information (Narra et al. 2018). However, employing specific software can help in looking for particular patterns in huge clusters of data.
KNIME, (also known as “Konstanz Information Miner”), can be considered to be a free and at the same time open-source data analytics that helps in reporting as well as assimilating platform. Knime is said to assimilate different components for the purpose of machine learning as well as data mining by means of modular pipelining theme. A user interface that is graphical in nature permits assembly of different nodes combining diverse sources of data, counting pre-processing components (including Extraction-Transformation-Loading) (Lausch et al. 2015). This is used for modelling, analysis of data and visualization without/with just minimal method of programming (Gandomi and Haider 2015). In actual fact, KNIME permits users to visually generate flows of data, selectively implement certain analytical stages, and thereafter scrutinize the outcomes, models, together with interactive visions (Lausch et al. 2015). To certain degree advanced analytics tool Knime can be regarded as an alternative to SAS.
Gandomi, A. and Haider, M., 2015. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), pp.137-144.
Lausch, A., Schmidt, A. and Tischendorf, L., 2015. Data mining and linked open data–New perspectives for data analysis in environmental research. Ecological Modelling, 295, pp.5-17.
Narra, J.M., Bein, D. and Popa, V., 2018. Business Intelligence Dashboard Application for Insurance Cross Selling. In Information Technology-New Generations (pp. 427-432). Springer, Cham.