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Data, Artificial Intelligence and Cloud Trends for 2020 and Beyond

Srivatsan Srinivasan
Nov 6 · 4 min read

As we rapidly near 2020, let us see some of the key Data, Artificial Intelligence and Cloud trends to look forward

Data Platforms today are re-architected for Cloud Native Era, Cloud has become core of Data Strategy to accelerate from idea to experimentation and Artificial Intelligence has started delivering reasonable value to some of the early adopters

Below are my top 5 predictions for 2020

Disclaimer: If you are looking for jazzy keywords like quantum, AI Explainability, AI Bias, Blockchain, etc then you are going to be disappointed :)

Cloud-Native Data Applications

Cloud-Native technology is rapidly moving from stateless applications to support complex data-driven applications. Frameworks like Apache Spark, Kafka, SQL Engine and few databases are already ported or getting ported to cloud-native technologies like docker and kubernetes

We will be seeing more and more of data application getting seamlessly ported to the cloud-native framework. The architecture of these technologies have to re-imagined rather doing a lift to shift, for it to work efficiently with Docker and Kubernetes

Why is it important?

  • Hybrid and Multi Cloud-ready
  • All parts of enterprise application converged on to single infrastructure (Data and Applications)
  • Better dependency management of packages and seamless migration from development to production

You can see my short video on dependency management and how cloud-native technology help solve it in the reference section below

Edge Analytics/Edge AI

With billions of IOT devices around the world and many playing part in the core business process, the enterprise has been capturing tons and tons of information. 2020 can see these tons of dark data lying untapped, being analyzed and used to fuel new data products

Algorithms/Models out of the huge pile of IOT data will be moved on closer to the edge of devices to reduce latency of decision process

If you are looking for details around edge analytics below is a short video on it

You can also look at my other related video (Reducing model size for Edge) and one of the insurance IOT use cases in the reference section towards the end of the article

Hybrid Cloud

Hybrid Cloud development was slower than expected in 2019 in-spite of rapid investments by major cloud vendors and product organization. In 2020 we will see adoption accelerate in Hybrid Cloud and products that enable seamless connectivity, centralized monitoring, security, load balancer’s among others

Many enterprises will embark investment onto Hybrid Cloud to leverage the best of private and public cloud and help the organization keep infrastructure cost under control at the same time help them accelerate and scale innovation

Prescriptive Analytics (From What to why)

While predictive analytics will continue to be the core of many data-driven decisions in 2020 we can see matured enterprises prototyping and deploying prescriptive analytics into their decisions

Prescriptive analytics is going beyond predicting the future. It provides the best course of action among various options that can enhance decision outcome

Prescriptive analytics prescribes “why an outcome will happen” and the real-world results are fed back to the model to learn and re-prescribe, improving prediction accuracy and prescribing better decisions over time

Augmented Analytics for Data and Model management

We will see increased adoption of statistical and machine learning techniques for data and model management. Some of the areas where Augmented analytics will play a major role are data quality, data security, smart missing value imputation, master data management, metadata management, model monitoring among others

Augmented analytics will also play a key role in data operations allowing apps to burst out before possible spikes or automatically spot and react to failures in real-time.

There have been patches of implementation already happening in this space within the enterprise today. We can see new products in the market that can automate many of the data management, data preparation, and model monitoring task and help enterprise accelerate on their analytics journey

References

Docker and Kubernetes (Cloud Native technology) for Data Science

Model Quantization to reduce model memory and CPU need when deploying on low power/compute edge devices

Usage Based Insurance — IOT business case

Data Driven Investor

from confusion to clarity, not insanity

Srivatsan Srinivasan

Written by

Data Scientist | Data Engineer

Data Driven Investor

from confusion to clarity, not insanity