Data Mining: Definition, Importance, and Problems

BUSINESS INFORMATION SYSTEMS
Authors name
BUS 105
Name of the professor
Institution name
City/state
Date

Part A
Question 1
Definition of data mining
Data mining is defined as the process that involved sorting large data sets as a way of
identifying and establishing patterns and relationships which are necessary for analytical
problem solving. Through the data mining tools enterprises are able to extrapolate trends into
the future.
Phases of the Cross-Industry Standard Process for Data Mining
Business understanding
This is the primary phase whose, main objective is to understand the objectives and
requirements of a project. This knowledge is thereafter converted into a definition that can fit
data mining (IBM Corporation 2012).
Data understanding
This phase began with data collection then proceeds to activities that enhance familiarity with
the data set. It assists detect subsets of interest that are useful in the hypothesis formulation.
Data preparation
This phase accounts for all the activities that are necessary to construct the final dataset from
the raw data. The tasks here include recording, tabling as well as selection of attributes. Data
transformation and cleaning also forms part of this phase.
Modelling
This stage involved selection and application of a number of models to obtain optimal values.
Evaluation
After building the models, this stage involves evaluation and reviewing the steps which have
been executed so as to create the model (Chapman et al. 2000). This way the application of
the model in solving a business problem is verified.
Deployment

After creating the model there is need to organize the knowledge gained in a way that it can
be useful to the consumers. This phase therefore entails carrying out of the deployment tasks
by the clients.
Question 2
Importance of data mining
Data mining assists in making informed decisions by projecting the future using the past
available data.
Elements of data mining
Below elements need to be put in place when carrying out data mining
Accuracy: a resource is only valuable if the quality aspect is reliable. Hence, when preparing
a tool for data mining it’s important to ensure the sources where the information is to be
gathered are accurate.
For example, a retail trader may be interested in product pricing offered by a number of
competitors. So as to ensure the information gathered is accurate, he/ she needs to consider
season, source and the products of concern.
The tools used to gather the data need to be able to differentiate the nature of data that is to be
collected this way data accuracy is aligned to the harvesting reason.
Relevancy: For information to be useful for decision making, it’s vital that it do consider the
context aspect. In a situation where a simple tool is designed to support harvesting of data it
is possible that it may fail to ensure the context needed is enough to ensure the data source is
relevant. The use of machine learning has assisted bridge the context gap. According to IBM
there are three areas that context need to cover that is industry, data and transfer.
For example; when crafting MySQL, it is not possible for your code to recognise context. A
more sophisticated tool needs to be applied so as to learn about the data context by using the
accumulated data and logical expressions.
Specificity: in the real world there are huge chunks of data that is availed on a daily basis.
When collecting data its therefore vital that the researcher do mine only the vital information
that can be in line with business strategy and the awareness of the industry (Cerami 2018).
Question 3