Re-engineering Data Mining Business Process

This article discusses the methodologies and techniques used in re-engineering data mining business process. It covers the six processes that data has to pass through before implementation, including data generation, business understanding, data preparation, data modelling, model evaluation, and report and decision making. The article also discusses the use of NORA and ANA techniques to filter data and ensure data security. The methodology used in the research is the waterfall method. The article concludes by emphasizing the importance of data mining techniques and processes in the field of business implementation.

Re-engineering Data Mining Business Process
Title
Name of Student
Course Name
Professor’s Name
University Name
Date

Re-engineering Data Mining Business Process
Contents
1.0 Abstract................................................................................................................................3
2.0 Introduction..........................................................................................................................3
3.0 Methodologies......................................................................................................................3
3.0.1Data generation...............................................................................................................3
3.0.2Business understanding..................................................................................................4
3.0.3Data preparation.............................................................................................................4
3.0.4Data modelling...............................................................................................................4
3.0.5 Model evaluation...........................................................................................................4
3.0.6 Report and decision making..........................................................................................4
Figure 1.1............................................................................................................................5
Figure 1.0............................................................................................................................5
Conclusion..................................................................................................................................6
References..................................................................................................................................6

Re-engineering Data Mining Business Process
1.0 Abstract
We study and analyze the current approaches used to determine the performance of various
businesses, with the reason of overseeing business progress. There are a number of
challenges affecting these methodologies which include how we can apply them on the
environment from all backgrounds and the history of education and the access to information
dependency which is very limited. A methodology that can be used has been proposed and
can be used by several business institutions to address these issues, knowledge discovery
databases have been put in place. Data mining is a key tool to help various business processes
get established due to high dependency rate of data from data mining centers.

Re-engineering Data Mining Business Process
2.0 Introduction
Data mining is a process by which hidden data are extracted or discovered from a large
dataset stored in the large database management system called the data warehouse. Data can
be extracted through the use of some specific techniques like carrying out statistical research,
embracing machine learning or artificial intelligence. For one to be able to extract the
required data from the repository site, data mining skills are required.
Data mining is being relied on by various business industries just ensure uniform growth and
efficiency of the industry. Thus there is need to ensure that data mining process has to be
standardized.
Data mining processes has to be improve day by day. This is done through a research where
users are being interviewed on the functionality of the current system. Since data mining is a
continuous growing process that keeps changing with time.
3.0 Methodologies
3.0.1Data generation. This is the process where data are established and analyzed. After the
data has been collected, they are analyzed using the data analysis tools such as the machine
learning and the artificial intelligence. During analysis process, it very prudent to filter some
very sensitive data to avoid issues to do with security. For security reasons, some data are
filtered and made secure before storing them in the data mining server. The data mining
server is the repository site which contains all the data to be extracted.
3.0.2Business understanding. This is the phase where we have to understand the current
business fully and the objective of the business are laid out clearly and the needs of the
business are supposed to be found. The existing business behavior must be assessed.
Resources, assumptions, and constraints are taken into account. From there we have to
establish the data mining goals to ensure the objective of the business are achieved. The goal
of reconciliation is to ensure that a good data mining plan is created.
3.0.3Data preparation. It takes a lot of time to do data preparation. Data preparation implies
analyzing those data and grouping them together according to how they relate. It provides the
final data to be stored in the data warehouse. The data collected are filtered and clean at this
phase so that the sensitive data are made secure and unnecessary data are disposed.
3.0.4Data modelling. A data mart is designed which include the conceptual design for
checking the dimensions and facts about the business process model. The techniques to be

Re-engineering Data Mining Business Process
used in data modelling has to be chosen so as to be used to prepare the dataset. The model is
tested for its validity and quality to meet the business requirements.
3.0.5 Model evaluation. In this stage, the data are studied and analyze so that the actual data
required are gathered. The actual system need to be studied very well and understood. New
ideas regarding the business are raised so that the new techniques which have been explored
are up to the task. In this stage, it is where decision is made whether or not the business
process will continue.
3.0.6 Report and decision making. The decision is made after all the processes have been
passed and an implementation of the new ideas is set to action. The stakeholder come up with
a full report of all the processes done during the business model process. The processes of
implementation, monitoring and maintenance has to be created.
The figure below is a design process to show all the process steps that are required during
data mining business process scheduled
Figure 1.0

Re-engineering Data Mining Business Process
Figure 1.1
Figure 1.0 describes the six processes through which the data has to pass through before
implementation. In each stage, the stakeholders have to discuss about the requirements for the
startup of the business. As shown in the figure 1.1 above every process or the stage is
discussed in detail so that they come to an agreement first before they implement the project.
The data are generated through collection and then they are studied and analyze. This data
can be analyze by use of interviews and creating questionnaires so that people have to answer
some questions (Anon, 2018). Data security concerns are discussed in every stage so that the
privacy of the business is kept. Some of the data are filtered (these are the sensitive data that
might affect the behavior and functionality of the business model.
In this research of the business model, the methodology used to research is the waterfall
method. This is a step by step procedure to reach a specific target goal. The end of analysis of
all the data collected a report is generated so that it can help to reconcile the stakeholders
agreement and will allow the immediate implementation of the data mining project.
Maintenance and monitoring team is setup to enhance the proper working of the system.
The goal of NORA (Non-Obvious Relational Awareness) is to give scholars a workspace for
exploring the system-identified features of common documents and further documents that

Re-engineering Data Mining Business Process
havebeen recommended by the system. Each of these projects is discussed within the
framework of visualizations involving browsing through dynamic grouping.
The function of NORA is to give scholars a space to work on discovering the kind of system
identified features of the important documents. It has been recommended by the system to
process some functional inputs. The NORA project is normally discussed within the system
framework of visualizations involving internet search through grouping the data.
ANA (Anonymized Data) is another technique used to filter data which are encrypted during
data mining process. It ensures that the retrieved data are safe and are relevant to the user.
Normally ANA is used to ensure that data security is kept and is well protected.
As-Is and To-Be process is typical of one cycle since if follows the system that we must
follow the way in which we need to study the SWOT analysis process through which we
analyze the strengths, threads, opportunities and weaknesses. The process is clearly shown in
this diagram.
The cost of To-Be process is much higher since it requires the inputs which deliver the
intellectual product. Input cost for this process is very high and therefor it is very expensive.

Re-engineering Data Mining Business Process
Conclusion
Data mining techniques and processes have greatly played an important role in the field of
business implementation. It provides a clear outline on how the program of the business is
going to be implemented. Therefore researches have shown that data stored in a very large
database, have helped business firm when they are doing some research about what they need
to do. BPMN (Business process modelling notation) is a tool that makes it easier for various
organization to plan what they are supposed to do in a series of stages. This means that a
certain methodology has to be followed. Specifically, waterfall is the best to be used in the
reengineering for the data mining process model.
References
Burcu Uçel, E. and Katrinli, A. (2014). A Qualitative Inquiry from the Aegean Region:
Changing Ruling Clas in Busines Circles. bilig, Journal of Social Sciences of the turkish
World, (71), pp.247-268.
Stein, S., Hamilton, B., Peterson, T., Guyer, C., 2016, Develop using Always Encrypted
with .NET Framework Data Provider. [Online] Available at: https://docs.microsoft.com/en-
us/sql/relational-databases/security/encryption/develop-using-always -encrypted-with-net-
framework-data-provider (Accessed 24 October. 2017)
Prasad, V., Ramdevputram A., 2015, "Identifying the Networks of Criminals Using
Management Information System", International Journal of Innovations & Advancement in
Computer Science, vol.4, Special Issues, pp. 144-148.
Ramos-Merino, M., Santos-Gago, J., Álvarez-Sabucedo, L., Alonso-Roris, V. and Sanz-
Valero, J. (2018). BPMN-E2: a BPMN extension for an enhanced workflow
description. Software & Systems Modeling./5-real-life-applications-of-data-mining-and-
business-intelligence/ [Accessed 9 Sep. 2018].
Solaimani, S. and Bouwman, H. (2012). A framework for the alignment of business model
and business processes. Business Process Management Journal, 18(4), pp.655-679.