Business Intelligence and IoT Report Analysis for Enterprises

This report delves into the realm of Business Intelligence (BI) and its applications within the Internet of Things (IoT). It explores how BI strategies and technologies, including data mining and online analytical processing, are essential for analyzing data and generating valuable business insights. The report highlights the importance of real-time data analysis, especially in the context of IoT devices and the need for efficient data handling. It discusses the challenges associated with data privacy and the benefits of using BI tools to make effective decisions. The report also explores the integration of various data sources and the role of BI in providing knowledge and information to decision-makers. It also covers the business migration tools that are used to support infrastructure, the investments in infrastructure for supporting the endeavors and working towards a dedicated streamlining process with persisting and processing the connected device data, and the services focus on creating a data-driven pattern for the business with analyzing real-time sensor data that is received from the customer network connected hardware.

REPORT

Business Intelligence in IOT
Abstract
Business Intelligence comprises of the different strategies and the enterprises which are
important for the analysis of the data and for better information. It provides the historical and the
predictive views based on business operations which include the reporting, online analytical
processing, data mining etc. Here, the technologies can handle the different large amount of data
in the structured or unstructured manner. The factors like tagging, sensing, shrinking and the
connections have made it easy to access and then share the data as well. It is available in the
different forms of sources like the commercial transactions, financial and administrative to
handle the transport and energy as well. The major issues come with the privacy, efficiency,
deletion and the identity fragmentation where the connection with more people lead to more loss
of privacy (Lee & Lee, 2015). Hence, for the effective and the timely decisions, the availability
of information and knowledge is from quantitative analysis to make all the effective decisions.
The ability is to react to the different actions and make the market needs a major critical success.
It is important to have better software tools that will help in identifying the effective and the
timely decisions as well. The analysis and the questions are alternative actions so that the data
from administrative, logistical and the commercial enterprises could be set under heterogenous
format. BI is most effective for handling the data where the need is to organize and process the
information using the appropriate tools. The data has a layered coding with the entities and the
transactions that involve the primary entities and the information. They are a result of extraction
and processing out the data to be carried. The information is contextualized and then enriched by
the experiences and the expertise of the different people who are decision makers (Dellermann,
Lipusch, Ebel & Leimeister, 2017). BI environment tends to provide better information and

knowledge to the decision maker from the data through the use of some models. They are able to
identify the objectives for analysis and performance, along with analysing the performance
indicators. Here, the business migration tools are defined to work on different patterns which
include the stronger demand for the real-time data analysis to support the infrastructure. The
integration is disparate into a consolidated and the interconnected network mainframe. IoT
companies tend to currently invest in the infrastructure for supporting the endeavours and
working towards a dedicated streamlining process with persisting and processing the connected
device data (Belov, Spohrer & Demirkan, 2017). Here, the specific attribute and the data works
on prescribing better solutions to the machine like the change in the setup which could be based
on the number of inputs. The services focus on creating a data-driven pattern for the business
with analyzing real-time sensor data that is received from the customer network connected
hardware.
Keywords: IoT, Business Intelligence, data analytics, big data

References
Belov, S., Spohrer, J., & Demirkan, H. (2017). Introduction to Business Intelligence, Analytics
and Cognitive: Case Studies and Applications (COGS) Minitrack.
Dellermann, D., Lipusch, N., Ebel, P., & Leimeister, J. M. (2017). Building Your IoT
Ecosystem: Proposing the Hybrid Intelligence Accelerator.
Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and
challenges for enterprises. Business Horizons, 58(4), 431-440.