Data-Driven Decisions for Fortune 500 using ML– Overview

• The majority of businesses nowadays heavily rely on data-driven decision-making. In order to fully understand their customers and products, businesses acquire a lot of data. This allows them to plan their future product development, growth, and marketing tactics.
• Unstructured Data to Structured Data Conversion is necessary since, in the Big Data era, businesses produce huge amounts of unstructured data.

ETL Process

• Extract, Transform, and Load, or ETL, is a Data Warehousing technique. Using an ETL tool, data is retrieved from several systems that serve as data sources, converted in a staging area, and then fed into a data warehouse system.

Extraction:

• That the very first step in the ETL process is extraction. This users are provided transferring data into the staging area from features from multiple systems, which may be relational databases, No SQL, XML, or flat files.
• It is necessary to initially keep the extracted data in the staging area before transferring it to the data warehouse because it is in diverse types and can be destroyed.

Transformation:

• Conversion is the second step of the ETL process. In order to transform the retrieved information into a single standard format, a number of rules or functions typically applied to it at this stage. Examples included sorting, connecting, cleaning, and filtering.

Loading:

• The third and last step of the ETL process is loading. To finish the process, the data extracted is now loaded into the data warehouse.
• The data is updated by either regularly feeding it into the data warehouse and maybe less frequently but more frequently, depending on the circumstance.
• The volume and frequency of loading differ with respect to system and are completely controlled by the needs.

Data visualization

• Visualization of data is the practise of placing information into a visual framework, such as a map or graph, to make the data easier for the human brain to interpret and draw conclusions from.
• The primary goal of data visualisation is to make it easier to see patterns, trends, and outliers in large data sets. Statistics graphics, information visualisation, and informational graphics are all phrases that are sometimes used similarly.

Why is data visualization important?

• Using visual data, visualization tool offers a rapid and efficient approach to transmit meaning to all audiences.
• Further, the process can help companies identify problem areas or those that need extra attention, uncover elements that influence customer behaviour, make data more memorable for stakeholders, determine the best times and locations to sell particular products, and anticipate sales volumes.

Benefits Of Data Visualization

• Information is easily communicated and easily understood.
• Helpful for processing enormous data sets.
• Simple method for making decisions.
• Helpful for spotting trends.
• Useful for boosting engagement

How we Helped Fortune 500 company with our Data-Driven Decisions Making Tech.

Solution Overview

• Downloaded e-Manifest files uploaded to EPA site at regular intervals using a scheduled job
• Built a pipeline to extract the downloaded file and upload it into multiple tables. Uploaded data files are moved to an archive folder
• Designed cubes to store aggregate data based on multiple dimensions
• Data uploaded to base tables are aggregated and populated into various cubes
• Built PowerBI reports and trained marketing team in using it and creating new ones

Technology Stack

Market size: Data visualization Tool

The global data visualization tools market size was valued at $7.4 billion in 2021, and is projected to reach $19.5 billion By 2031, growing at a CAGR of 10.2% from 2022 to 2031