PH Deck

Companies struggle to derive valuable insights from data, especially when dealing with large datasets on platforms like Microsoft Azure and AWS. The old solution involves manual data processing and analysis, leading to inefficiencies and errors. The drawbacks include: manual data processing, inefficiencies, and errors.

Users experience data loss or corruption on their Windows devices like hard drives, SSDs, USB drives, and memory cards. The old solution is relying on standard data maintenance and backup practices, which can often fail. The drawbacks are that traditional retrieval methods are not sufficient when devices experience severe data corruption or mechanical failure, leaving users unable to recover critical data efficiently.

Users are at risk of data theft, leaks, and unauthorized access with the current solution.

Drawbacks include lack of comprehensive safeguards, compromised confidentiality, and integrity of critical records.

E-commerce brands struggle to utilize first-party behavioral data effectively for marketing and advertising purposes, leading to lower marketing efficiency and subpar advertising results.

Developers and testers face challenges in creating realistic mock data for applications due to time-consuming manual processes and the need for accuracy.

Drawbacks include difficulties in ensuring data realism and managing the complexity of dataset customization.

Healthcare professionals struggle with the inefficiency of traditional data management methods.

The users are facing challenges in making big life decisions, such as selecting an ideal place to raise a child, without clear information or a structured decision-making process.

The drawbacks of the old situation include making decisions without clarity, confidence, and proactive planning.

Currently, users needing to generate mock data for applications rely on manual entry or limited datasets.

This old solution has drawbacks like time consumption, lack of variety, and possible inaccuracies that can affect development and testing accuracy.

Users have difficulty accessing a wide range of data due to the scattered sources and lack of consolidation, leading to inefficient research processes and decision-making. The scattered sources and lack of consolidation are the main drawbacks.

Users dealing with data stored in S3 often face issues ensuring the data is complete and accurate upon ingestion, which can compromise data reliability and affect downstream applications.