Collaborative Intelligence and Data Engineering: AI and Humans are Joining forces.

Artificial intelligence is becoming good at many “human” jobs - diagnosing disease, translating languages, providing customer service and it’s improving fast. What comes naturally to people can be tricky for machines, and what’s straightforward for machines (analyzing gigabytes of data) remains virtually impossible for humans.

What Is The Role of a Data Engineer?

A data engineer is like a swiss army knife in the data space; there are many roles and responsibilities that data engineers are capable of, typically reflecting one or more of the critical pieces of data engineering from above.

The role of a data engineer is going to vary depending on the particular needs of your organization.

It’s the role of a data engineer to store, extract, transform, load, aggregate, and validate data. This involves:

  • Building data pipelines and efficiently storing data for tools that need to query the data.

  • Analyzing the data, ensuring it adheres to data governance rules and regulations.

  • Understanding the pros and cons of data storage and query options.

Benefits of AI & Data Engineering

With the additional data and platform provided by AI & Data Engineering, you can achieve these benefits faster and more reliably. This is because AI & Data Engineering helps you bridge what’s commonly called the “AI Death Valley” – the development delays that prevent the majority of AI projects and proofs of concept from making it through to production.

How does AI & Data Engineering work?

In order to take advantage of data and turn it into actionable insights, organizations need to move towards a unified infostructure that manages the cloud-based platform together with all necessary data and then acts as the mechanism for delivering insight to users and applications. This is what AI & Data Engineering provides. AI & Data Engineering consists of foundation services that enable you to use your existing data estate to deliver reliable AI solutions – not just demos or departmental applications, but production systems that work at scale. These services can be understood in terms of five main layers, all underpinned by secure data:

• Platform Foundation: Helps you build an industrialized data and AI platform supporting innovation.

• Data Trust Foundation: Helps you develop a data governance strategy that supports your journey to trusted data.

• Data Foundation: Addresses data lakes, hubs, and warehouses, and provides data ingestion.

• AI, Analytics & BI Foundation: Offers services, work products, frameworks, and accelerators to organize your data optimally for enterprise AI working at scale.

• AI, Analytics & BI Execution: Provides minimum viable products (MVPs) and accelerators to help you develop AI-based or AI-enabled applications efficiently.

Conclusion

The main purpose of Artificial Intelligence is to aid human capabilities and predict the far-fetched consequences that the human brain cannot process. Artificial Intelligence has the potential to reduce the hardships of human labor and make a possible pathway for the human species to thrive in a beneficial way.

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