Data Mining and Business Decision Making

This assignment delves into the role of data mining tools in contemporary business decision-making. It examines how these tools empower organizations to analyze complex data and make informed strategic decisions. The significance of data warehouses as a foundation for effective data analysis and their contribution to a holistic understanding of business operations are also explored.

MAN6905 - Assignment part 2
Name of the Student
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Author Note

Introduction
Data mining, the extraction of concealed perceptive data from substantial databases, is an
effective new innovation with incredible prospective to empower organizations to concentrate
on the utmost imperative data in their data warehouses. Data mining tools supports business
organizations to anticipate future patterns and practices. It is also enabling organizations to
make proactive, learning driven choices (Witten et al., 2016). The mechanized, planned
examinations offered by data mining helps to gain such inner information which cannot be
found with the help of traditional decision support systems. Data mining tools can answer
business issues that generally were excessively tedious, making it impossible to determine.
They search databases for concealed examples, finding prescient data that specialists may miss
since it lies outside their desires.
Most organizations officially gather and refine enormous amounts of information. Data mining
procedures can be executed quickly on existing programming and equipment stages to upgrade
the benefit of existing data assets, and can be coordinated with new items and frameworks as
they are expedited line (Braha, 2013). At the point when actualized on superior
customer/server or parallel handling PCs, data mining tools can dissect monstrous databases to
convey answers to inquiries, for example, "Which customers are well on the way to react to
newly introduced product, and why?" Tools like Rapid Miner, WEKA, Tableau and R are the
most important data mining tools that support business decision makers to work on these kind
of questions and get an idea for better decision making.

This essay gives a prologue to the essential advances of data mining. Cases of productive
applications outline its significance to the present business condition and an essential depiction
of how information stockroom structures can advance to convey the estimation of information
mining to end clients.
Important feature of Data Mining tools
Data mining is utilized to discover or produce new helpful data's from substantial measure of
information base. It is a procedure of removing already obscure and adequate data from
extensive databases and utilizing it to settle on critical business choices. A few developing
applications in data giving administrations, for example, information warehousing and on-line
benefits over the Internet, likewise call for different information mining and learning revelation
systems to comprehend client conduct better, to enhance the administration gave, and to
expand the business chances Of an outline of learning disclosure database and information
mining (Rokach & Maimon, 2014).
Over the years the importance of data mining gradually increase and todays organization
cannot think their operations without data mining tools. According to the study of Shmueli and
Lichtendahl Jr (2017), there are several important features data mining tools have among which
the most significant ones are “data preparation facilities”, “selection of data mining
operations”, “product scalability and performance” and “Facilities for understanding end
results”.
While talking about the first important feature that is “data preparation facilities”, it can be said
that business decision majorly depends on how well the raw data is prepared for analysis.

When the traditional decision support system experienced difficulties in preparing data; data
mining tools available at present makes it easier for decision maker with the help of data
preparation, data cleansing, data describing, data transforming and data sampling functions.
These predefined functions enables the decision maker to finalize what data needs to be
considered for a specific decision (Wu et al., 2014).
The second most essential feature is “selection of data mining operations”. It has seen that
business decision makers tried to understand the historical data in order to predict the future
trend and perceptions of end users. In order to do so, it is very much important to understand
the characteristics of the operations (algorithms) used in specific data mining tool to ensure
that they meet the user’s requirements (Papamitsiou & Economides, 2014). In other words, it is
imperative to make a choice regarding how well the algorithms understand historical data and
accordingly work on new dataset. Fortunately, there are extensive list of options available in
most data mining tools and because of this use of such tools is cooperatively easy than
traditional decision making systems.
The third important feature is “product scalability and performance”. It means, data mining
tools are capable of dealing with growing amounts of data, perhaps with refined validation
controls. Not only has that it also supports decision makers in terms of sustaining satisfactory
enactment may require inquiries into whether a tool is proficient of ancillary parallel processing
using technologies such as SMP or MPP (Gupta, 2014).
Finally, the fourth most important feature of data mining tool is “Facilities for understanding
end results”. By giving measures, for example, those depicting precision and criticalness in

valuable organizations, for example, perplexity networks, by enabling the client to perform
affectability investigation on the outcome, and by introducing the outcome in elective courses
utilizing for instance perception strategies.
Value of Data Warehouse
A Data Warehouse (DW) is the center of any business insight (BI) stage and its activity is to
coordinate information from various information sources paying little heed to where they are
found (Larose, 2014). Programming, for example, Tableau and QlikView are not BI devices, they
are information perception devices, much like Excel. They are not databases, they do not
interface information and consolidation your informational indexes, organization require extra
instruments for that.
A Data Warehouse is fundamentally "Lord" spreadsheet however on an appropriate database
able to do effectively extend and join extra information sources, that can without much of a
stretch channel information for every area or per individual or per item, and that has every
single accessible measurement and measurements that are essential to you prepared to
envision in a reliable and worldwide way (Roelofs et al., 2013).
Subsequently it is required to "module" the perception apparatuses, Excel into them to
extricate the information and imagine it, yet at this point the critical step of the activity is
improved the situation you (Ferreira et al., 2015). What's more, more significantly, on the off
chance that you are utilizing the privilege DW it is done consequently.
Putting away data in an information distribution center commonly known as data warehouse
does not give the advantages an association is looking for. To understand the estimation of a

data warehouse, it is important to separate the learning covered up inside the warehouse. Be
that as it may, as the sum and unpredictability of the information in an information stockroom
develops, it turns out to be progressively troublesome, if certainly feasible, for business
investigators to recognize patterns and connections in the information utilizing basic inquiry
and detailing apparatuses (Khan & Hoque, 2015). Data mining can give immense paybacks to
organizations who have made a critical interest in information warehousing. In spite of the fact
that information mining is as yet a generally new innovation, it is as of now utilized as a part of
various enterprises (Gupta, 2014).
The aftereffects of information combination encompass business organizations, empowering
exceptionally profitable business exercises in their associations. However, they do not generally
look past those exercises to see information joining as the imperative, in the background
empowering influence that it is (Braha, 2013). On the off chance that one have to substantiate
the business estimation of information joining—which is a typical essential for the financing,
sponsorship, or execution of information coordination—at that point they have to disclose to
their associates the empowering part that information reconciliation plays for some
information driven business hones (Rokach & Maimon, 2014). Besides, on the off chance that
one organization need to keep information coordination arrangements completely lined up
with business objectives, at that point they should be always aware of the particular sorts of
business esteem that outcome from information reconciliation's groups, devices, and systems.
Many take a gander at this as a revealing issue that should be tended to yet in actuality this is
an information issue most importantly (Papamitsiou & Economides, 2014). Tackle this issue
with a Data Warehouse and you will receive the rewards of quick information examination,

brought down costs, less time spent on non-basic errands and a superior incorporated
perspective of your business.
Conclusion
To conclude, it can be said that data mining tools are the part and parcel of today’s business
decision making. Several features enable business decision makers to employ data mining tool
in complex situation and make strategy for success in the longer run. When data mining tools
support business organizations to analyze data in a better way. Data warehouse plays pivotal
role as decision makers mostly rely on such data available in data warehouse for decision
making.

Reference
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