What’s the Challenge?
This leads to :
Inefficient Data Management
Managing cluttered data can be expensive because it requires additional resources, technology, and expertise to organise, process, and maintain it.
Unorganised data can lead to inaccurate insights or conclusions.
As organisations must comply with various data privacy and security regulations, cluttered data can lead to compliance challenges.
When organisations have unrelated data, they are unable to identify opportunities, trends, or patterns that could benefit them, resulting in lost revenue and missed opportunities.
How To Begin?
Data Transformation is the process of transforming or cleaning data to ensure consistency, accuracy, and usability.
Loading data into a data lake or data warehouse with encryption logic on critical or sensitive data is what Data Loading entails.
Data Mining and Analysis at various stages using various technologies allows for a deep dive.
Data Insights provides decision-making flexibility. And the continuous movement of data necessitates Data Governance, Data Security, and Compliance.
The Extraction process is aided by data selection and sourcing. To extract data from unstructured sources such as emails, documents, and images, natural language processing (NLP) and optical character recognition (OCR) can be used.
Further machine learning algorithms help to identify patterns, cleanse data, and perform Data Integration tasks.
AI can aid in data loading automation by utilising automated workflows and scheduling tools in a timely and efficient manner.
Using a training model and continuous tuning, Data Maintenance can be automated using predictive analytics and machine learning algorithms to identify data quality issues, anomalies, and errors, as well as perform automated data validation and reconciliation tasks.
It can immediately identify risks and take the necessary action to become Compliant with the registration of critical/sensitive data identification.