The document outlines the key steps in the data mining process for business analytics. It discusses 1) understanding the business objectives, 2) collecting and organizing relevant data, 3) preparing the data for analysis, 4) applying mathematical models to identify patterns in the data, 5) evaluating the models and results, and 6) deploying the results. Data mining provides businesses with insights into customer behavior and operations to help improve strategies, sales, and decision-making.

13. In this phase, it can be helpful to document all the resources available,
including the employees, teams, and departments that will
be involved. Outline a clear picture of what each resource is
responsible for achieving and establish a communication
process that everyone should adhere to.

14. 3. Delegate the Work
Once you know what needs to be done to ensure success, determine who needs to do what and
when. Refer to your original timeline and goal list, and delegate tasks to the appropriate team
members.
4. Execute the Plan, Monitor Progress and Performance, and Provide Continued Support
Next, you’ll need to put the plan into action. One of the most difficult skills to learn as a
manager is how to guide and support employees effectively. While your focus will likely be on
delegation much of the time, it’s important to make yourself available to answer questions your
employees might have, or address challenges and roadblocks they may be experiencing. Check in
with your team regularly about their progress and listen to their feedback.
5. Take Corrective Action (Adjust or Revise, as Necessary)
Implementation is an iterative process, so the work doesn’t stop as soon as you think you’ve
reached your goal. Processes can change mid-course, and unforeseen issues or challenges can arise.
Sometimes, your original goals will need to shift as the nature of the project itself changes.

15. 6. Get Closure on the Project, and Agreement on the Output
Everyone on the team should agree on what the final product should look like based on the goals
set at the beginning. When you’ve successfully implemented your strategy, check in with each team
member and department to make sure they have everything they need to finish the job and feel like
their work is complete. You’ll need to report to your management team, so gather information, details,
and results from your employees, so that you can paint an accurate picture to leadership.
7. Conduct a Retrospective or Review of How the Process Went
Once your strategy has been fully implemented, look back on the process and evaluate how things
went. Ask yourself questions like:
 Did we achieve our goals?
 If not, why? What steps are required to get us to those goals?
 What roadblocks or challenges emerged over the course of the project that could have been
anticipated? How can we avoid these challenges in the future?

16. BUSINESS ANALYTICS
- is the process by which businesses use statistical methods and technologies
for analyzing historical data to gain new insight and improve strategic decision-
making.
Business analytics, a data management solution and business intelligence
subset, refers to the use of methodologies such as data mining, predictive
analytics, and statistical analysis to analyze and transform data into useful
information, identify and anticipate trends and outcomes, and ultimately make
smarter, data-driven business decisions.

17. The essentials of business analytics are typically
categorized as either descriptive analytics, which analyzes
historical data to determine how a unit may respond to a set
of variables; predictive analytics, which looks at historical
data to determine the likelihood of particular future
outcomes; or prescriptive analytics, the combination of the
descriptive analytics process, which provides insight on what
happened, and predictive analytics process, which provides
insight on what might happen, providing a process by which
users can anticipate what will happen, when it will happen,
and why it will happen.

18. The main components of a typical business analytics dashboard include:
 Data Aggregation: prior to analysis, data must first be gathered, organized, and filtered,
either through volunteered data or transactional records
 Data Mining: data mining for business analytics sorts through large datasets using
databases, statistics, and machine learning to identify trends and establish relationships
 Association and Sequence Identification: the identification of predictable actions that
are performed in association with other actions or sequentially
 Text Mining: explores and organizes large, unstructured text datasets for the purpose of
qualitative and quantitative analysis
 Forecasting: analyzes historical data from a specific period in order to make informed
estimates that are predictive in determining future events or behaviors

19.  Predictive Analytics: can help predict the future based on the past. It
involves digging into historical data, finding key patterns and predicting
future trends on the basis of those patterns
 Optimization: once trends have been identified and predictions have
been made, businesses can engage simulation techniques to test out best-
case scenarios
 Data Visualization: provides visual representations such as charts and
graphs for easy and quick data analysis

20. Analytics can play a vital role in the education industry by helping universities and
institutes make data oriented informed decisions. Analyzing academic, financial and
operational data helps identify specific patters and trends. This insight helps better
decision making around planning, budgeting and forecasting.
Tracking students’ performance across cohort, departments and courses and
creating clusters based on different characteristics enables targeted strategies for
specific segments of students, such as students pursuing a particular course and
performing exceptionally well or average or below average students finding the course
very tough. For the below average cluster, the university administration can initiate
structures intervention and provide them some special training to ensure retention and
improved performance.
Analyzing the attendance data and focusing on students who missing the assigned
course credit can help identify likely dropouts. Specific actions or retention programs
for such students can have a significant impact on dropout rates.

21. DATA MINING
 Data mining is the process that companies use to turn raw data into
useful information. They utilize software to look for patterns in large
batches of data so they can learn more about customers. It pulls out
information from data sets and compares it to help the business make
decisions. This eventually helps them to develop strategies, increase sales,
market effectively, and more.
 In education, data mining can help understand the reasons behind a
student’s decision to leave the course midway. E.g. insufficient or no
financial aid, high cost of education, poor grades, choice of subjects,
distance from home, good job opportunity or better choice of college, etc.

22. Overview of the data mining process
Almost all businesses use data mining, and it’s important to understand the
data mining process and how it can help a business make decisions.
 Business understanding. The first step to successful data mining is to
understand the overall objectives of the business, then be able to convert this
into a data mining problem and a plan. Without an understanding of the goal
of the business, you won’t be able to design a good data mining algorithm.
For example, a supermarket may want to use data mining to learn more about
their customers. The business understanding is that a supermarket is looking
to find out what their customers are buying the most.
 Data understanding. After you know what the business is looking for, it’s
time to collect data. There are many complex ways that data can be obtained
from an organization, organized, stored, and managed. Data mining involves
getting familiar with the data, identifying any issues, getting insights, or
observing subsets. For example, the supermarket may use a rewards program
where customers can input their phone number when they purchase, giving
the supermarket access to their shopping data.

23.  Data Preparation. Data preparation involves getting the information
production ready. This is the biggest part of data mining. It is taking the
computer-language data, and converting it into a form that people can
understand and quantify. Transforming and cleaning the data for modeling is
key for this step.
 Modeling. In the modeling phase, mathematical models are used to search for
patterns in the data. There are usually several techniques that can be used for
the same set of data. There is a lot of trial and error involved in modeling.
 Evaluation. When the model is complete, it needs to be carefully evaluated
and the steps to make the model need to be reviewed, to ensure it meets the
business objectives. At the end of this phase, a decision about the data mining
results will be made. In the supermarket example, the data mining results will
provide a list of what the customer has purchased, which is what the business
was looking for.
 Deployment. This can be a simple or complex part of data mining, depending
on the output of the process. It can be as simple as generating a report, or as
complex as creating a repeatable data mining process to happen regularly.

24. How does data mining inform business analytics?
 So why is data mining important for businesses? Businesses
that utilize data mining can have a competitive advantage, better
understanding of their customers, good oversight of business
operations, improved customer acquisition, and new business
opportunities. Different industries will have different benefits
from their data analytics. Some industries are looking for the
best ways to get new customers, others are looking for new
marketing techniques, and others are working to improve their
systems. The data mining process is what gives businesses the
opportunities and understanding for how to make their
decisions, analyze their information, and move forward.