What is data Analysis?

What is Data Analysis

Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informingg conclusions, and supporting decision- making is called Data Analysis.

 

How is data analytics used in business?

Data analytics is used in business to help organizations make better business decisions. Whether it's market research, product research, positioning, customer reviews, sentiment analysis, or any other issue for which data exists, analysing data will provide insights that organizations need in order to make the right choices.

Data analytics is important for businesses today, because data-driven choices are the only way to be truly confident in business decisions. Some successful businesses may be created on a hunch, but almost all success  business choices are data-based.

 

What are examples of Data Analysis?

 

1.  Predictive Analysis: Predictive data analysis predicts what is likely to happen in the future. In this type of research, trends are derived from past data which are then used to form predictions about the future. For example, to predict next year's revenue, data from previous years will be analysed. If revenue has gone up 20% every year for many years, we would predict that revenue next year will be 20% higher than this year. This is a simple example, but predictive analysis can be applied to much more complicated issues such as risk assessment, sales forecasting, or qualifying leads.

 

2.  Prescriptive Analysis: Prescriptive data analysis combines the inform the previous 3 types of data analysis and forms a plan of action for th face the issue or decision. This is where the data-driven choices are
   Prepare your data for analysis in five steps using Excel

 

Import Data

Split Data along delimiters (e.g. Comma or Semicolon)

 o Extract parts from data entries

o Remove leading and trailing spaces

Format Adjustments

Standardize formats (e.g. dates, currencies, units)

 o Store data in the correct format (e.g. Value, text, date)

 o Replace unrecognized or corrupted characters (e.g. ĀY)

 o Check for truncated entries (i.e. entries that have been cut off)

 

Correct Inconsistencies

 o Check for inconsistent entries using custom rules (e.g. age > 0)

o Numerical Data: Check for Outliers

o Categorical data: check for wrong categories (e.g.,no"vs.,o") o Missing values: Add data or remove rows

Format Adjustments

o DE duplicate your data considering fuzzy duplicates

 

 Combine Datasets

o Lookup values from other tables

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