Reconsidering your Data Ingestion Strategy to Support the Unforeseen


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March 16, 2020 1:00am


Now Is The Time To Modernize Your Data Ingestion Strategy

As COVID-19 (coronavirus (COVID-19) has taken hold globally, tech and healthcare industries have combined forces to work quickly towards a solution. AI has been instrumental in this process with supercomputers working on a coronavirus vaccine as well as AI forecasting outbreaks by analyzing news reports, social media and government documents.

In a global pandemic of this scale, employing machine learning and AI accelerates the timeline towards a solution beyond what humans alone could perform. The more data AI models have to process, the more accurate they are. BUT if the lifeblood of AI is data, how can traditional decentralized and multi-source architectures effectively deliver enough data for AI models to succeed?"

The answer...they can’t.
Architectures need to modernize.

Organizations Need To Employ Predictive Analytics To Stay Competitive

Much like the coronavirus outbreak, organizations need to be prepared to make short and long term adjustments to their strategy, employ predictive analytics for better business decisions and have quick access to daily tracking of organizational stability. However, most organizations keep data siloed and, as a result, it can be challenging to know what internal data is residing in different departments and systems, let alone to collect it. This doesn’t include any third-party cloud application data that may also be floating around.

Using predictive models in times where businesses operate normally is important but in times of crisis, is an absolute necessity. For example, retailers need to identify the likelihood of customers keeping patterns of over-provisioning in the next few months based on a real-time product consumption and completely new external data that was not originally considered by enterprises at the beginning of 2020. Knowing these answers will help you serve customers better and can be transformative for enterprises.

What Should I Consider When Building My Optimal Data Ingestion Strategy

There is no doubt that new data models need to emerge but also the data ingestion infrastructure that feeds those models must evolve. When looking for the characteristics of these new data ingestion models, they should consider the following capabilities:

  • Comprehensive data replication: Ability to support different workflows, including Change Data Capture (CDC), initial data capture, schema evolution, and batch processing
  • Event streaming by replicating only the data changes in real-time from any data source to any target
  • Unmatched performance, low-latency, and scalability
  • Comprehensive data transformations/manipulations on streaming and batch data pipelines
  • Easy to use, an intuitive user interface that requires zero coding experience
  • Support for a broad range of data sources and targets
  • Structured and semi-structured data formats (JSON, XML, CSV, etc)
  • Fully orchestrated OSS foundation including Kafka, Spark, and other innovative open-source components
  • Fully monitored and highly available
  • Ability to run on-premises, public clouds or hybrid environments

While there are some solutions on the market that have managed “business as usual” workflows, it is obvious that a better, faster data ingestion platform is needed to support unexpected data workflows. The continuous flow of company and external information into a consolidated real-time analytical environment will ensure you are making informed decisions based on accurate, up-to-date insights.

Interested in learning about how Equalum’s Platform can exceed the needs outlined above?

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