Data Challenges
As organisations scale up to meet customer requirements and deploy multiple applications to support results in some of the keys:
- Silos of data
- Exponential growth of data
- Multiple sources of data
This leads to the following challenges:
- Data Integrity issues due to silos and inconsistencies
- Data integration issues
- Complex business rules
Valuekreators’ analytics services and solutions can help any organization grow and differentiate itself from the competition. We identify use cases that can deliver against your business priorities and create analytics solutions with the right talent and technologies to suit your needs.
Our Data and Analytics Capabilities
Valuekreators works with customers in developing the data fabric, and have the following service components:
- Data Transformation or Harmonisation – defines the approach and strategy to transform or harmonise the data from all sources and this may involve defining transformation rules.
- Data Migration – bringing all silos together is key to the success of the data journey.
- Data Integration – our data engineers work with businesses to create the data map for integration among various sources.
- Data Visualisation – Based on the initial assessment data engineers recommend the data visualisation tool.
ValueKreators Bigdata Data Engineers work with customers to build the framework for AI as it’s a differentiator for the business to manage the different forms of data structured and unstructured.
How we partner with your business?
Consulting & Assessment
Our Data Engineers and Analysts will work with your team to define the data journey or review existing data solutions and recommend Data Road Map which focuses on operational efficiencies and scalability.
Managed Services
We not only develop and implement data solutions, but we also support our customers in managing the deployment. The support is governed by SLA (Service Level Agreement).
Process Improvements
Our data engineers focus on achieving operational efficiencies by working with business teams in defining the data transformation rules.
