Data Handling and Business Intelligence

This document provides a comprehensive guide on data handling and business intelligence. It covers the uses of different excel formulas, data mining methods, and advantages of SPSS. The document also explores data processing and marketing research.

DATA HANDLING AND BUSINESS INTELLIGENCE

Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
Part 1............................................................................................................................................3
Uses of different excel formulas..................................................................................................3
PART 2............................................................................................................................................7
1. Using the nutrition.csv provided the workings with screenshots and explanation of results
are shown below:.........................................................................................................................7
2. Data mining methods.............................................................................................................11
3. Advantages and Disadvantages of SPSS...............................................................................12
CONCLUSION..............................................................................................................................13
REFERENCES..............................................................................................................................14

INTRODUCTION
Data processing and marketing research are the data collection industries in which actions are
made using an innovation framework where their knowledge is the most valuable commodity to
an enterprise (Psenka, Booty and Duncan, Ethority Inc, 2015). The report is based on two
separate tasks under which first task contains detailed information about use of different kinds of
Excel function is included. While in second part of report, different kinds of mining methods are
mentioned along with some SPSS functions are applied in the context of given data set.
MAIN BODY
Part 1
Uses of different excel formulas
Data handling is defined as the method of gathering, capturing, analysing and presenting
valuable and gathered information in a way that is very efficient for the customer. Market
intelligence (BI) maximises goods and technology to translate data into information of practice
that informs even an entity's strategic decision making. In reports, overviews, research methods,
tables, interactive presentations, view and analyse datasets of Software applications and report
empirical findings that can give more detailed facilities to consumers about the company's
situation (Vossen, 2014).
For customers, business managers, home bosses, data collection, regular performance
monitoring, like Excel Spreadsheets tools, companies may be identified. The key guidelines
include Microsoft Excel.
Evaluating AND Saving DATA- Indeed, Microsoft Excel is a perfect way of testing the system
and tamper with details. Details may be written in different column and in paper rows. The
processing of data is aided by maps and graphs. Due to the inherent errors, and its benchmark
method, the information obtained for the monitoring accuracy was considered as the extreme
range of the error feature. For machine assessment and even for measuring objects, criteria are
used. Throughout this step, the software is visited and checked by the audit committees, relating
to the sequence of procedure parameters consisting of data types believed to be quality steps.

That is, there appears to be an assumption that these systems will lead to quality outcomes. These
processes are more reflective of the various ways of quality assurance including self-appraisal
and also daily site teams, guided by defined process criteria. Information from various papers
and archives is available from the Spread-sheet. Data is stored in order. It helps us to spend a lot
of time in order to fully enjoy the exams. It does not support data extraction using the Pivot
Table.
DATA RECOVERY- Users will get that from the app, until the information is corrupted. Data
from the DATA sheet is extracted from the MS Excel software (Grossmann and Rinderle-Ma,
2015). The circumstances of the same data loss that the application development software used
to create the substitute and also the restoration target detail, the healing recovery process. For
example, certain desktop and laptop recovery software devices allow consumers to retrieve lost
files directly, but the recovery of a corrupted archive from a disc backup is a much more
complex process involving IT intervention. Data recovery mechanisms can be used to retrieve
files that have not yet been mistakenly supported and removed from a computer but remain
dispersed on the hard disk.
Formulating Study- It may publish the research proposal in MS Excel, and also in MS Word
format or table style. You can use this data with Ms Excel using that database. MS Excel may be
used to perform simultaneous device distinction. Inevitably, MS Excel monitors businesses, but
it also helps staff to record their performance records on a constant basis. Teachers can assist
students with the use of charts, maps, techniques and forecasts.
Increasing the awareness- The normal effort to hit degrees of accomplishment in the past, but
progress also helps us to complete our future studies. Why have we spent many years living this
way? Approach relates to addressing the question of what is or actually is. Users build
possibilities for the future through different algorithms and historical patterns. The planning and
production is carried out through organisations, as per this article. Since then it appears to be the
world benchmark for excellence, integrating all sorts of functionality and features. As such to
support all kinds of customers, some important, outstanding formulas are accessible. The amount
of valuable Excel formulas as well as activities is almost infinite. The software can be used for
many types of uses, such as making budgets, handling data, creating charts and graphs, and many
more. Even then there were formulas that could help specific instances. It will focus on the

selection of outstanding Excel formulas that can be applied to virtually any case. They can help
with the effectiveness of the server and make it much easier to manage it.
CONDITIONAL FORMATTING- Both associated persons may be listed under contingent
category. There may also be other examples of a contingent model. This also refers to
comprehension of definitions and acknowledgement of reproduction (Kimble and Milolidakis,
2015). Conditional Formatting (CF) is a process that involves the formatting of an entity or a
series of rows and the modification of the specification tax to the quality assessment or the
context of the equation. For example, if the meaning of the cell is greater than 100, they can only
make a cell appear brazen. Whenever the cell performance exceeds the format criteria, the design
that you select is applied to the cells. If the network security does not meet the format criteria,
the traditional container design has been used.
Security- for sheets, MS Excel provides robust system protection, allowing staff to easily track
their performance. The two MS Excel reports were stored inside of an excellent folder using
basic visual applications. In MS Spread-sheets, they file the basic details as well as keep the data
ordered such that time is saved. Everyone needs to protect their records, and MS Excel is very
great for solving this problem. Yet there's no one able to substitute or eliminate it.
IF Function
In Excel, a 3-part period clause is used in the IF feature or IF argument. In this case, it is easy to
see what revenue and benefit levels are decreased or what time the seller's payment is, and also
how the IF feature is implemented point by point.
In multiple websites, copy and paste the taxable period, revenue and benefit again for the first
instance.
Reorganize old data into the production process statement's first layout.
• Use the 'IF Part' type. Shape a code of sort = if (virus type: B2>C3),
If the customer fits the requirements, keep inserting a cellular key to be checked as well.
• Pick a comma: Chose "Increase"
• Delete the box; and click Start unless the requirement is not fulfilled.
Whether a feature highlight the significance of B3, because if the value of B3 is higher than that
of the portion of B3, the feature of B1 would then mean that The variables could be even more
important than B3.' As this functionality may be seen, consumers will obtain the layout of cell
B4 as they press the Enter key. To see the effect, move the handling from D4 to wireless D8400.

They would not be frustrated if individuals need to learn which H Lookup as well as V lookup
would have been in a system. Whether or not the customers want it is an important competence.
They will consider it in detail while they deal for small numbers (Kasemsap, 2015).
Value of Lookup: A row or base along with the search piece. That truly is the base of the string.
Series of tables: The appropriate background diagram. Choosing what the customer wants from
their table is challenging.
Number of the row index: The desired number is shown in the numbers of the rows. The original
row will have to be 1.
Range lookup]: Two iterations of the same set are included in the table, one correct (1) and
another incorrect (1) (0).
Use of Lookup function
Lookup value: A segment or line area suits a question and restores the same quality by the
standard segment or line zone behaviour through which LOOKUP overrides certain problems.
Used Cell G2, H2 and I2 in terms of time, transactions and revenue needed. The acquisition of
G3, H3 and I3 is required. So choose add the H3 cells to a Lookup component; use the Cache
key as a G3 cell.
Series: A2 to C8400 quality choice (A2:C8400) for the whole data collection
[Range lookup]: for the cells to be bought, B2 to B8400 (B2:B8400).
Charts
Steps:
Select a cell for even a bar chart which will be made.
To claim clarity, select a line graph.
01/01/2009
03/04/2009
04/07/2009
04/10/2009
04/01/2010
06/04/2010
07/07/2010
07/10/2010
07/01/2011
09/04/2011
10/07/2011
10/10/2011
10/01/2012
11/04/2012
12/07/2012
12/10/2012
-20000
0
20000
40000
60000
80000
100000
Sales
Profit

Description: The related figures say that the business had the maximum income and lowest
expenses, also in January. On the other hand, in 2012, twice the maximum income was reported.
The statistics indicate that sales and profits vary dramatically; from 2009 through 2010,
excluding 2012, the company experienced substantial losses. True revenues increased through
2009, while in 2009 there was no decline in following years.
PART 2
1. Using the nutrition.csv provided the workings with screenshots and explanation of
results are shown below:
The following review was performed with the aid of multiple tasks in the SPSS from the sample
group given. Below the observations are seen and viewed:
Descriptive analysis of Gender and rice consumption
Statistics
Gender Rice
N Valid 100 100
Missing 9 9
Mean 1.50 .60
Median 1.50 1.00
Mode 1a 1
Std. Deviation .503 .492
Range 1 1
a. Multiple modes exist. The smallest
value is shown
Gender
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 1 50 45.9 50.0 50.0

2 50 45.9 50.0 100.0
Total 100 91.7 100.0
Missing System 9 8.3
Total 109 100.0
Rice
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 0 40 36.7 40.0 40.0
1 60 55.0 60.0 100.0
Total 100 91.7 100.0
Missing System 9 8.3
Total 109 100.0
Descriptive analysis between age group consuming the rice
Statistics
Rice Age
N Valid 100 100
Missing 9 9
Mean .60 20.35
Median 1.00 19.00
Mode 1 22
Std. Deviation .492 3.560
Range 1 13
Rice

Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 0 40 36.7 40.0 40.0
1 60 55.0 60.0 100.0
Total 100 91.7 100.0
Missing System 9 8.3
Total 109 100.0
Age
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid 13 5 4.6 5.0 5.0
15 5 4.6 5.0 10.0
17 8 7.3 8.0 18.0
18 13 11.9 13.0 31.0
19 20 18.3 20.0 51.0
20 5 4.6 5.0 56.0
22 21 19.3 21.0 77.0
23 1 .9 1.0 78.0
25 12 11.0 12.0 90.0
26 10 9.2 10.0 100.0
Total 100 91.7 100.0
Missing System 9 8.3
Total 109 100.0
The other statistical test are presented below:
One way Anova test between gender and age.

Correlation test
One sample T test;
One-Sample Statistics
N Mean
Std.
Deviation
Std. Error
Mean
Gender 100 1.50 .503 .050
Rice 100 .60 .492 .049
One-Sample Test

Test Value = 0
t df
Sig. (2-
tailed)
Mean
Difference
95% Confidence Interval of
the Difference
Lower Upper
Gender 29.850 99 .000 1.500 1.40 1.60
Rice 12.186 99 .000 .600 .50 .70
2. Data mining methods
Statistical tools: The analytical nation is facing the very same big problem at a particular time as
machine learning: whether insufficient lithosphere findings can be derived correctly (Sherman,
2014). A short and accurate explanation of the relationship between observations and
conclusions is the most successful data solution. The same protocol parallels this basic text and
addresses a series of fundamental problems resulting from scientific evidence: management of
assumptions, calculation of confidence and inconsistent analysis of numbers. Statistical analysis
is a division that gathers and presents findings of numerical maths. Computer technology
approaches are also another methodology that is not considered by some as a way of analysing
data. But the study of prediction data and trends is also advocated. The database administrator is
often receptive to other methods in mathematics. In mathematical research, the earth actually
consists of enormous big data and vital problems.
View: The simplest approach is to imagine data structures to understand them. From the outset of
data processing, this approach should be used. Several research programmes, known as sprints,
are currently evolving creative activities. A variety of data mining methods produce useful
patterns for analysis. Thus, graphical image is a methodology that turns poor data into strong
outcomes for use and various methods in data processing.
Tree Methodology for Induction Decision- Indeed a decision tree is a wooded unit; it also
implies the tree by word. Any other tree branch is also used in this method as a binary grouping,
as well as understanding the tree splits for the database section. This methodology can be used
for that both preceding and study in exploratory experiments. The data set can be used as a
subset of the training sample for a particular purpose (Kimball and Ross, 2015). The data in the

segment shows some differences in their estimation of their results. The decision tree has strong
ramifications for access for students.
Clustering Techniques- Clustering is one of the key mathematical methods, and is a method by
which clustering of data is equally grouped. This offers examples of inconsistencies and links
through research. This is also known as separation, which lets users identify the operations of the
server. For example, insurance may categorise its clients by employment, age, payout and form
of claims.
Convolution Network- Neural networks are another successful technique that is used primarily
for individuals. For early knowledge mining, this phase is widespread. Because they are built, it
is easy to get machine learning and users do not have to be technologically or tech-savvy. An
artificial intelligence group developed the neural algorithm.
Association Rule Methodology: A relation is formed between objects in an image. The
relationship between various factors within the server is also significant (Kalelkar, Churi and
Kalelkar, 2014). The secret pattern database presents evidence for the classification of variables
and the consistent presence of environmental stimulation at the same quantities.
3. Advantages and Disadvantages of SPSS
Benefits over Excel
 SPSS is customer-friendly and easy-to-use apps for pupils, but also assists with study,
even if data collection is wider. The statistical method is a software package used to
analyse logical batch processing and non-batching in mathematics.
 This software is among the most popular concise programs that can perform hugely
complex data study and measurement with simple instructions.
 SPSS will capture and then use details from every format to create tabulated files,
distributed and trend graphs and charts, factor statistics, and even to analyse data.
Drawbacks
 For all those that need advanced procedures, procedural elements may be more stringent,
as in R number, not really on SPSS, but not as robust and useful as R as well as other
software can be needed.

 Statistical Package for Social Sciences that can then be used for forecaster only. Among
other bundles, that will require extra sales (Abdullah and Ahmad, 2015).
 Normal graphics are rather different from publishers' performance. Using other graphics
programmes is typically smoother.
 Awareness regarding appropriate doses and sampling sizes is missing with such
approaches.
CONCLUSION
On the basis of above project report this can be concluded that excel functions are useful for
companies in order to reduce complexity of data set as well as for finding a particular outcome in
an effective manner. In the further part of report detailed analysis of given data of customer
consumption has been done in accordance of various kinds of SPSS functions.

REFERENCES
Psenka, M., Booty, M.D. and Duncan, B., Ethority Inc, 2015. Enhanced user interface and data
handling in business intelligence software. U.S. Patent 9,165,044.
Vossen, G., 2014. Big data as the new enabler in business and other intelligence. Vietnam
Journal of Computer Science, 1(1), pp.3-14.
Grossmann, W. and Rinderle-Ma, S., 2015. Fundamentals of business intelligence. Springer.
Kimble, C. and Milolidakis, G., 2015. Big data and business intelligence: Debunking the
myths. Global Business and Organizational Excellence, 35(1), pp.23-34.
Kasemsap, K., 2015. The role of data mining for business intelligence in knowledge
management. In Integration of data mining in business intelligence systems (pp. 12-33).
IGI Global.
Sherman, R., 2014. Business intelligence guidebook: From data integration to analytics.
Newnes.
Kimball, R. and Ross, M., 2015. The kimball group reader: Relentlessly practical tools for data
warehousing and business intelligence remastered collection. John Wiley & Sons.
Kalelkar, M., Churi, P. and Kalelkar, D., 2014. Implementation of model-view-controller
architecture pattern for business intelligence architecture. International Journal of
Computer Applications, 102(12).
Abdullah, M.F. and Ahmad, K., 2015, August. Business intelligence model for unstructured data
management. In 2015 International Conference on Electrical Engineering and
Informatics (ICEEI) (pp. 473-477). IEEE.