Data, Artificial Intelligence and Cloud Trends for 2020 and Beyond

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 are 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 cloud native framework. 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 Reference section below

Edge Analytics/Edge AI

With billions of IOT devices around the world and many playing part in core business process, 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 huge pile of IOT data will be moved on closer to 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 case in reference section towards 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 enterprise will embark investment onto Hybrid Cloud to leverage best of private and public cloud and help organization keep infrastructure cost under control at the sametime help them accelerate and scale innovation 

Prescriptive Analytics (From What to why)

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

Prescriptive analytics is going beyond predicting future. It provides 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 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 technique 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/anomalies in real time

There have been patches of implementation already happening in this space within 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