Overcoming Big Data Integration Challenges

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Enterprise Challenges

Hadoop is a good example of a database technology emerged as one of the leading forms of technology to help businesses solve problems leveraging their data. However, to better solve these business problems, companies need to use several tools to bring it all together.

The components in a Hadoop stack are focused on infrastructure and built to solve a specific set of problems. A managed data lake, for example, requires putting together different technologies to come up with the right solution the company needs.

Another huge challenge presents itself in the form of talent gaps by experts who know how the various technologies work when they are put together. Specifically, developing a Hadoop-based data lake requires combining aspects from legacy data platforms with modern technologies; a rare skillset to find in industry today. Combining these issues will often result in companies not realizing the maximum value from their big data investments.

Sometimes the struggle of turning a company’s data assets into actionable insights can become complex because of the belief that if they make the data bigger (i.e. volumes of data), then they are effectively increasing the surface area of the amount of data that will then need to be governed.

Another challenge a company may face is the overall quality of the data. Unstructured data, for example, can make integration a lot more difficult when combining it with structured data. The technology needs to become increasingly more accessible, and for Big Data to go mainstream, its management skills also need to be more widespread.

Market Needs an Enterprise Ready Big Data Platform

The market needs an enterprise-ready big data platform integrating the underlying infrastructure and providing a self-service tool to bridge the IT/LOB gap by combining the platform capabilities with product usability.

Skills are needed to support Big Data lifecycle needs from data ingestion, standardization, and metadata management for new use case development. Certain foundations should also be in place to help with the data integration.

The Foundations Needed for Governed Data Integration

Specific foundations need to be in place when your enterprise begins a data integration project.

  • Automated Ingestion: the ability to automatically ingest data from a variety of sources, including structured and unstructured, without having developers write a single line of ‘ETL’ code
  • Standardization: the solution should handle different data standardization mechanisms based on how users want their data consumed
  • Different Modes of Delivery: handle Direct to Database, Batch and Streaming data sources
  • Timely Access to Data: provides quick access to LOBs and also adds new feeds to data lakes which makes the information available sooner
  • Enterprise and LOB-Centric Use Cases:  the solution should support not only enterprise-wide use cases, but also use cases tightly coupled to specific LOBs
  • Data Governance: data management efforts should always comply with security, data quality and data governance requirements

A Unified Data Management Platform

At Next Pathway, we help companies overcome their data management problems by providing a self-service, metadata-driven big data platform to solve several data use cases.

Next Pathway’s Cornerstone tool caters to the wide range of use cases that include Risk and Regulatory reporting, Anti-Money Laundering, Real-Time Fraud Detection, Data Science, Machine Learning, Customer 360, among many others.

The tool meets the data security and governance requirements and also provides the following:

  • Authentication
  • Authorization
  • Audit Logging
  • Metadata, and
  • Lineage

Cornerstone provides a solution for end to end big data integration initiatives. Request a demo to see it in action and how it can alleviate your business challenges.

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