WHAT IS DATA ANALYSIS??

I. INTRODUCTION
Data   Analysis is   a	process	of	inspecting,	cleaning,   transforming,  and
modelling data with	the  goal	of	discovering	useful information,  suggesting

conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. The Analysis of Data is the most skilled task in the research process. it calls for the researcher's own judgement and skill in all cases of businesses and sciences.

DATA MINING

Data mining, used synonymously with Data Analysis, is a particular data analysis technique that focuses on modelling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical or structural models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.

DATA INTEGRATION

Data integration is a precursor to data analysis, and data analysis is closely linked to visualization and data dissemination.

Data analysis is a process, within which several phases can be distinguished; Processing of Data refers to concentrating, recasting and dealing with data in such a way that they becomes as amenable to analysis as possible.

DATA CLEANING

The need for data cleaning will arise from problems in the way that data is entered and stored. Data cleaning is process of preventing and correcting these errors. Common tasks include record matching, deduplication, and column segmentation.

There are several types of data cleaning that depend on the type of data. For Quantitative data methods for outlier detection can be used to get rid of likely incorrectly entered data. For textual data spellcheckers can used to lessen the amount of mistyped words, but it is harder to tell if the word themselves are correct.

II. INITIAL DATA ANALYSIS

The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that are aimed at answering the original research question.

The initial data analysis phase is guided by the following four questions:

i. Quality of data

The quality of the data has to be checked as early as possible. Data quality can be assessed in several ways, using different types of analyses:

o   frequency counts

o   descriptive statistics (mean, standard deviation, median)

o   normality (skewness, kurtosis, frequency histograms)

variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable.

ii. Quality of measurements

The quality of the measurement should only be checked during the initial data analysis phase when this is not the focus or research question of the study. It is to

be checked as to whether the structure of measurement corresponds to structure reported in the research or performance.

iii. Initial transformations

After assessing the quality of the data and of the measurements, it to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase.

iv.     Did the implementation of the analysis fulfil the intentions of the performance design?

It is to be check as to the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across groups.

III. CHARACTERISTICS OF DATA SAMPLE

In any analysis report, the structure of the sample must be accurately described. It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis phase.

The characteristics of the data sample can be assessed by looking at:

Basic statistics of important variables Scatter plots

Correlations and associations Cross-tabulations

IV. FINAL DATA ANALYSIS

Several analyses can be used during the final data analysis phase:

a)     Univariate statistics (single variable)

b)     Bivariate associations (correlations)

c) Graphical techniques (scatter plots)

It is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level:

Nominal and ordinal variables

Continuous variables

In the main analysis phase, analyses aims at answering the performance question are performed as well as any other relevant analysis needed to write the first draft of the performance report or audit report.

V. STABILITY OF RESULTS

It is important to obtain some indication about how generalizable the results are. While this is hard to check, one can look at the stability of the results. Are the results reliable and reproducible?

There are two main ways of doing this:

Cross-validation: By splitting the data in multiple parts we can check if analyzes based on one part of the data generalize to another part of the data as well.

Sensitivity analysis: A procedure to study the behaviour of a system or model when global parameters are (systematically) varied.

VI. APPLICATION OF DATA ANALYSIS

Data analysis assumed application in various prospects of daily world, both in businesses and social sciences.

The following instigate wide range of applicability:

a.     Management analysis

b.     Forensic accounting

c.      Fraud detection in accounting and auditing

d.     Trend analysis

e.     Strategic analysis

f.       Decisive analysis

g.     Economic analysis

h.     Investment analysis

i.       Earnings disposition analysis

j.       Leverage analysis

k.     Dynamic growth analysis

VII. APPLICATION IN FINANCE

Applications of this Data Analysis in the profession of finance is vast and usually called as Financial Analysis or Accounting Analysis.

Financial analysis refers to an assessment of the viability, stability and profitability of a business, sub-business or project.

It is performed by professionals like we Chartered Accountants who prepare reports using ratios that make use of information taken from financial statements and other reports. These reports are usually presented to top management as one of their bases in making business decisions like:

Continue or discontinue its main operation or part of its business; Make or buy certain materials in the manufacture of its product;

Acquire or rent/lease certain machineries and equipment in the production of its goods;

Issue stocks or negotiate for a bank loan to increase its working capital; Make decisions regarding investing or lending capital;

Know about the financial stability and worthiness;

Ensure the validity of accounting and internal control system;

Other decisions that allow management to make an informed selection on various alternatives in the conduct of its business.

VIII.  OBJECTIVE OF FINANCIAL ANALYSIS

Financial analysts often assess the following elements of a firm/company:

1.     Profitability - ability to earn income and sustain growth in both the short- and long-term. A company's degree of profitability is usually based on the income statement/statement of profit and loss, which reports on the company's results of operations;

2.     Solvency - ability to pay its obligation to creditors and other third parties in the long-term;

3.     Liquidity - ability to maintain positive cash flow, while satisfying immediate obligations;

Both 2 and 3 are based on the company's balance sheet, which indicates the financial condition of a business as of a given point in time.

4.     Stability - firm's ability to remain in business in the long run, without having to sustain significant losses in the conduct of its business.

Assessing a company's stability requires the use of both the income statement/statement of profit & loss and the balance sheet, as well as other financial and non-financial indicators. etc.

IX. METHOD OF FINANCIAL ANALYSIS

Financial analysts often compare financial ratios (of solvency, profitability, growth, etc.):

Past Performance - Across historical time periods for the same firm/company (the last 5 years for example),

Future Performance - Using historical figures and certain mathematical and statistical techniques as stated above, including present and future values, this extrapolation method is the main source of errors in financial analysis as past statistics can be poor predictors of future prospects.

Comparative Performance - Comparison between similar firms/companies

These ratios are calculated by dividing a (group of) account balance(s), taken from the balance sheet and/or the income statement, by another, for example :

Net income / equity = return on equity (ROE)

Net income / total assets = return on assets (ROA)

Stock price / earnings per share = P/E ratio

Comparing financial ratios is merely one way of conducting financial analysis. Financial ratios face several theoretical challenges:

They say little about the firm's/company's prospects in an absolute sense. Their insights about relative performance require a reference point from other time periods or similar firms.

One ratio holds little meaning; that is to say, calculating and analysing only one ratio cannot disclose anything about the position of the firm/company.

As indicators, ratios can be logically interpreted in at least two ways. One can partially overcome this problem by combining several related ratios to paint a more comprehensive picture of the firm's performance.

Seasonal factors may prevent year-end values from being representative.

A ratio's values may be distorted as account balances change from the beginning to the end of an accounting period. Use average values for such accounts whenever possible.

Financial ratios are no more objective than the accounting methods employed. Changes in accounting policies or choices can yield drastically different ratio values.

Financial analysts can also use percentage analysis which involves reducing a series of figures as a percentage of some base amount. For example, a group of items can be expressed as a percentage of net income. When proportionate changes in the same figure over a given time period expressed as a percentage is known as horizontal analysis. Vertical or common-size analysis, reduces all items on a statement to a “common size” as a percentage of some base value which assists in comparability with other companies of different sizes. As a result, all Income Statement items are divided by Sales, and all Balance Sheet items are divided by Total Assets.

Another method is comparative analysis. This provides a better way to determine trends. Comparative analysis presents the same information for two or more time periods and is presented side-by-side to allow for easy analysis.

This article has been shared with us by S. Omkhar.

S. Omkhar

SRO 0292620

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