5 Reasons Why Google Sold Motorola, and 5 Reasons Why Lenovo Bought It
Google sold the remnant of Motorola's mobile phone operations to Lenovo yesterday, getting the world's dominant mobile OS company out of the business of making mobile phones. This is a good move for Google and a good move for Lenovo, although it may not be a good move for consumers. Here's why Google and Lenovo are both walking away from the deal happy.
Why Google Sold Motorola1. Google's mobile strategy is to get Android onto as many phones as possible, as almost all of the company's revenue comes from advertising,including on mobile devices. This is very different from Apple, BlackBerry, and Microsoft, which are all now integrated hardware-software businesses.
2. Google wanted Motorola for the patents, not for the manufacturing. Apple's patent attack on Android licensees was slowing down and worrying Google's customers. Motorola had a massive patent library that can be used defensively.
3. Once Google bought Motorola, some of its major licensees started hedging their bets and developing or buying their own non-Google OSes: Samsung with Tizen and LG with WebOS, for instance. They were worried Google would compete directly with them.
4. Motorola never made Google any money.
5. By ditching Motorola, Google can be a neutral, honest broker of operating systems to the world and make money doing so.
Why Lenovo Bought Motorola1. Lenovo is one of the world's top five smartphone makers, but its market share in the U.S., one of the world's largest smartphone markets, is zero. Motorola has an 85-year history in the U.S. and, at this point, pretty poor distribution elsewhere.
2. The company's biggest business is still PCs, and it's the world's No. 1 PC maker. But PCsales aren't growing. If Lenovo is going to be a technology leader in the late 2010s, it needs to be a mobile tech leader. Assembling a global smartphone business is key.
3. Lenovo has experience integrating and running U.S.-based technology companies. It bought the ThinkPad business from IBM and led it to success, and it now has amanufacturing plant in Whitsett, N.C. making Think-branded products.
4. Getting into the U.S. market is all about relationships with U.S. carriers. Motorola's relationships are really, really good: it has the ongoing Droid deal with Verizon Wireless, and it's placed the Moto X on three out of our four national carriers.
5. Lenovo doesn't just want to be a consumer smartphone maker. It has very strong enterprise relationships with its ThinkPad and ThinkCenter products, and it just bought IBM's low-end server business. By now also offering phones, it can deliver full technology packages to its U.S. business clients. (PCMAG)
WHAT IS DATA ANALYSIS??
I. INTRODUCTION
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Data Analysis is a
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process
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of
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inspecting,
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cleaning,
transforming, and
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modelling data with
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the goal
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of
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discovering
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useful information,
suggesting
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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.
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)
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
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;
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
CA Final
CA Final/IPCC RTP May 2014
CA Final and CA IPCC Revision Test Papers (RTP) for May 2014 exams is not yet released by the ICAI. Every year ICAI release the RTP in the last week of February.
This year also we are expecting to get the RTP in LASt week on FEB. AS soon as it will be released we will update on our website.
This year also we are expecting to get the RTP in LASt week on FEB. AS soon as it will be released we will update on our website.
The RTPs will be made available at all regional branches of the ICAI. Students can also purchase the RTPs online through ICAI website.
CA Final and CA IPCC November 2014 RTP's are Released by ICAI. To Download the RTP Click on below links:
CA Final November 2014 RTP
CA IPCC November 2014 RTP
CBDT releases Explanatory Notes on Finance Act, 2013
CBDT has vide Circular NO.03/2014 Dated, the 24th January, 2014 issued EXPLANATORY NOTES TO THE PROVISIONS OF THE FINANCE ACT, 2013. Explanatory Notes cover the following Topics / Provision.
To Download the Notes Click Here.
To Download the Notes Click Here.
E TDS -TCS return filing procedure changed :27A Mandatory through FVU w.e.f 01.02.2014
A Major Change has been took place in filing the TDS Return. With effect from February 1, 2014, it is mandatory to submit form 27A generated by TDS/TCS FVU (File Validation Utility) duly signed, along the TDS/TCS statement/(s). Any other Form 27A submitted will be treated as invalid submission and the same will be rejected by TIN-FC branches.
Earlier Form 27A was generating from any Software or could be prepared manually but now it should be renerated by TDS File Validation Utility. Automatically generation of Form 27A has been incorporated in RPU 3.8. If you are using the TDS RPU 3.8 then form 27A will be Generated through TDS RPU 3.8
Earlier Form 27A was generating from any Software or could be prepared manually but now it should be renerated by TDS File Validation Utility. Automatically generation of Form 27A has been incorporated in RPU 3.8. If you are using the TDS RPU 3.8 then form 27A will be Generated through TDS RPU 3.8
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