Big Data & Analytics solutions
Helping our clients define and implement a Big Data infrastructure that supports business requirements for scalability, flexibility, security, and affordability. This includes on premise, cloud-based, or hybrid infrastructure solutions, infrastructure management and monitoring, and implementation of supporting databases and data search engines
Extract, Transform, Load (ETL)
Performing data ingestion, transformation, and integration, particularly for large amounts of data from multiple sources. Ness also develops a data governance framework to ensure data provenance, data integrity, and internal authorization processes meet customer requirements
Providing Business Intelligence (BI) and visualization solutions, including design of BI strategy, data mart and enterprise data warehouse architectures, smart dashboards, and other visualization techniques. Ness has particular expertise in helping clients identify practical applications for predictive, real-time, and cognitive analytics and creating Artificial Intelligence (AI) and Machine Learning (ML) solutions that operate very effectively in production environments
Client challenges that we solve
- What is the best way for the company to implement a data framework that is scalable and will evolve as we grow?
- How can the company effectively merge data from multiple sources to create new perspectives on our business?
- What opportunities for operational improvement can we surface with advanced data analytics?
- How can we visualize data in a way that is more meaningful to our stakeholders?
- What are practical ways we can leverage Artificial Intelligence (AI) and Machine Learning (ML) to improve the way we do business?
Work in Action
Enhancing a recommendation engine
A technology company asked Ness to help it enhance its recommendation features with the capability to crawl millions of web pages for content information. Ness created a scalable, cloud-based solution that combines web data extraction with real time information mapping and indexing. We defined an easy-to-use visual interface for onboarding new sites to crawl that reduces data source onboarding from 2 days to 2 hours.
Creating a 360-degree customer view
Ness significantly reduced the friction customers experienced when purchasing a client’s products. Ness created a “single source of truth” about customers by aggregating key client and partner data. Rules around data collection, cleansing, and standardization were established between client and partner systems, so that the client had a 360-degree view of customer data for operational and marketing purposes.
Building an enterprise data hub
We partnered with a client’s innovation team to create an Enterprise Data Hub that included ETL for real time and near real time ingestion and transformation of transaction data. This included precomputing data related to customer information for an Anti Money Laundering (AML) module. The solution enables faster data consumption by downstream systems and long-term data flexibility.
BIG DATA & ANALYTICS News
Machine Learning Workflow: A New Product Category Is Born
Ness’s Chief Technology Officer introduces a growing product category that provides a seamless Machine Learning (ML) operational environment
Moshe Kranc · 13 July 2018
BIG DATA & ANALYTICS Blog
What We Know We Don’t Know About Blockchains
Blockchain has a viable future, but like any newer technology, it’s likely not the solution to everything
Moshe Kranc· 27 March 2018
BIG DATA & ANALYTICS News
Data Ingestion Best Practices
Ness’s Chief Technology Officer discusses data ingestion best practices, which can be the difference between success and failure
Moshe Kranc · 16 January 2018