Data Analysis

At our organization, we believe, Data analysis is a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.

Descriptive Analysis

Descriptive Analysis is the type of analysis of data that helps describe, show or summarize data points in a constructive way such that patterns might emerge that fulfill every condition of the data. It is one of the most important steps for conducting statistical data analysis.

Diagnostic Analysis

The diagnostic analysis is a special type of analytical technique using which the data is interpreted and analyzed properly to find out what happened or caused a particular cyber breach. There are various different techniques that are used for the sake of understanding or extracting data for it to be analyzed properly.

Predictive Analysis

Predictive Analysis is the use of statistics and modeling techniques to determine future performance based on current and historical data. Predictive analytics looks at patterns in data to determine if those patterns are likely to emerge again, which allows businesses and investors to adjust where they use their resources to take advantage of possible future events.

Prescriptive Analysis

Prescriptive Analysis is the Mix all the insights gained from the other data analysis types, and you have prescriptive analysis. Sometimes, an issue can’t be solved solely with one analysis type, and instead requires multiple insights.

Out pathway to establish complete Data Analysis include:

Understanding the Business Requirements
Understanding the business or activity that your data project is part of is key to ensuring its success and the first phase of any sound data analytics project.

Collection of Data
Data collection starts with primary sources, also known as internal sources. This is typically structured data gathered from CRM software, ERP systems, marketing automation tools, and others. These sources contain information about customers, finances, gaps in sales, and more. Then comes secondary sources, also known as external sources. This is both structured and unstructured data that can be gathered from many places.

Categorization of the Data
Data categorization is the sorting of data according to its type, sensitivity, and value to the organization if altered, stolen, or destroyed. It helps an organization understand the value of its data, determine whether the data is at risk, and implement controls to mitigate risks.

Enrichment of the Data
Enriching data is by joining datasets — essentially, retrieving columns from one dataset or tab into a reference dataset. This is a key element of any analysis, but it can quickly become a nightmare when you have an abundance of sources. manipulate it in order to get the most value out of it.

Data Interpretation
Data interpretation is the process of reviewing data through some predefined processes which will help assign some meaning to the data and arrive at a relevant conclusion. It involves taking the result of data analysis, making inferences on the relations studied, and using them to conclude.

Data Visualization
Data visualization gives us a clear idea of what the information means by giving it visual context through maps or graphs. This makes the data more natural for the human mind to comprehend and therefore makes it easier to identify trends, patterns, and outliers within large data sets.

Our tools to Analyze Data are:

Business Intelligence Tools.
Statistical Analysis Tools
General purpose programming languages
SQL Consoles
Spreadsheet Applications
Data Modelling Tools

Industry Specific Analytics Tools
Unified Data Analytics Tools
ETL Tools
Predictive Analytics Tools
Data Science Platforms
Data Visualization Tools