From our previous blog on Model Lifecycle Manage, we concluded that: Data & Model Management will become a key consideration of Pricing Officers as it is critical to ensure quality, speed, and reliability of AI-based pricing solutions in operations. 

To maintain efficiency and quality ‘Data engineering’ is the aspect of data science that focuses on practical applications of data collection and analysis which is critical to ensure efficiency and quality.

Issue: Companies lack in building expertise that would allow understanding the data and collecting data from multiple sources and merging them into one scenario. Managing cross deployments has become a hassle and this can be solved by building simple data engineering expertise.

What is Data Engineering and Why is it a must?

The key to understanding what data engineering lies in the “engineering” part.  Engineers design and build things.

In the recent decade, most organizations have accomplished a digitalized change. This has created unfathomable volumes of new data or information and substantially more muddled information at a higher recurrence. While it was already obvious that Data Scientists were expected to comprehend everything, it was less clear that somebody needs to sort out and guarantee this current information’s quality, security, and accessibility for the Data Scientists to carry out their responsibilities.

Earlier with big data analytics, Data Scientists were all the time expected to build the necessary infrastructure and data pipelines. The outcome was that information displayed would not be done effectively. There would be excess work and irregularity in the utilization of information among Data Scientists. These sorts of issues kept organizations from having the option to remove ideal incentives from their information ventures, so they fizzled. It likewise prompted a high pace of Data Scientist turnover that despite everything exists today.

Today with digital transformation, the Internet of Things, and the race to become AI-driven, it is completely clear that organizations need Data Engineers in wealth to give the establishment to effective information science activities. 

Data Engineering Entails:

Create and maintain optimal data pipeline architecture, assembles large, complex data sets that meet functional / non-functional business requirements. The process of identifying, designing, and implementing internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability. Additionally, build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources.

  • Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
  • Work with stakeholders including the Executive, Product, Data, and Design teams to assist with data-related technical issues and support their data infrastructure needs.
  • Keep our data separated and secure across national boundaries through multiple data centers.
  • Create solutions for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
  • Work with data and analytics experts to strive for greater functionality in our data systems.

In our opinion, data engineers should have a good understanding with scientists to understand the requirement to build a structured pipeline.