Industries — IT & Technology

Data Evolution

Aishore uses Cloud Transformation, Data & AI expertise to solve business inefficiencies with scalable solutions.

In today’s environment, the company has grown into a complex enterprise with a diversified portfolio of core businesses, however, the Enterprise Data Strategy has not evolved causing numerous pain points and inefficiencies across the organization. In addition, the global pandemic has seen more and more companies accelerate their move to the cloud, reinventing their offerings, and becoming more cost-efficient, agile, and innovative in how they operate their businesses. Aishore prides itself with its expertise in Cloud Transformation, Data & AI. We use our cross-functional domain and engineering expertise to understand specific business problems and build generic solutions that can be scalable to multiple customers/business units with minimal incremental work.

Cloud Data Empowerment

Aishore enables you to build an effective cloud based data & analytics ecosystem

AI powered Cloud platform

we leverage our expertise in Cloud transformations across all major Tech Stacks (AWS, GCP, Azure), Enterprise Data Insights and AI to help our customers unlock the power of their data, providing them with an incomparable strategic business edge.

Cloud/Digital Transformation & Engineering excellence

we are continuously improving our customer’s eng infrastructure focusing on data quality, availability, security and supportability to scale development and velocity

Trusted and secure Enterprise Data and Advanced Analytics

Producing insights and enabling leaders to make the right decisions as part of their everyday user journeys.

Use Case

ML-powered Mater Data Management

The company uses multiple disconnected systems for engaging external entities like partners, suppliers, and customers, leading to fragmented and inconsistent data, which hampers visibility into corporate relationships.

Fragmented Data Systems

Lack of integration between systems results in inconsistent and fragmented data, hindering visibility into corporate relationships.

Manual Data Stewardship

High reliance on manual intervention to verify and resolve duplicates, making data management costly and error-prone.

Missed Market Opportunities

Limited ability to gain insights into market opportunities and cross-selling prospects due to fragmented and incomplete data.

Compliance and Legal Risks

Risk of costly fines due to non-compliance with data privacy regulations like GDPR, DSA, NDMO, and DPSA.

Solution

We evaluated 3 ML-based matching tools and determined which option would perform better under the criteria defined below while mitigating Legal & Compliance Risk as a strategic advantage.

Key decision criteria:

Quality: use pairwise comparison between different solutions to see how similar the clusters are to each other

Maturity: How mature these tools are and how many issues we face as part of the implementation

Support: How quickly the tool owner resolves the issues

Compliance: Meet compliance requirements for privacy, legal and security – Yes/No based on privacy and Legal review

IT & Technology