Data Analytics and Decision Making: Tableau and KNIME Analysis

This report provides an overview of data analytics and decision-making processes, focusing on the application of tools like Tableau and KNIME. It explains how dashboards in Tableau can consolidate various data views for comparative analysis, facilitating informed decision-making by aligning enterprise efforts and revealing key insights. Furthermore, the report discusses data mining in KNIME, highlighting its role in identifying patterns and establishing associations within large datasets. KNIME, an open-source data analytics platform, integrates components for machine learning and data mining through a modular pipelining theme, allowing users to visually create data flows and analyze outcomes with minimal programming. The report concludes by referencing relevant literature on big data concepts and data mining in environmental research, emphasizing the practical applications of these tools in converting raw data into actionable information. Desklib offers a wealth of similar solved assignments for students seeking assistance.

Running head: DATA ANALYTICS AND DECISION MAKING
Data Analytics and Decision Making
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DATA ANALYTICS AND DECISION MAKING
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.

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DATA ANALYTICS AND DECISION MAKING
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.

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References
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.

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DATA ANALYTICS AND DECISION MAKING