DATA Pill #034 - 2022 summaries & 2023 predictions

SUMMARIES 2022

• There are two reasons for the reduction in big funding rounds in 2022 compared to 2021. The most obvious reason is that the entire tech sector has cooled, partly fueled by concerns about inflation, interest rates, and the collapse of the crypto economy.
• One rule to follow when assessing a technology is that it is no longer “new” once IBM makes a television commercial about it. This means if there is no compelling use case for something by the time IBM starts advertising about it, then there will never be one.

This report has separate segments (in the links below you will find key takeaways from each of them):

• Mobile and IoT 
• DevOps and Cloud 
• Software Architecture and Design 
• AI, ML, & Data Engineering 
• Culture & Methods 
•  .NET
• Java

You will discover some individual trends listed on multiple reports, sometimes with differing adoption levels. For example, cloud-native development and gRPC are in the earlier stages of adoption within the .NET community, compared to software architecture and cloud development in general.

Data is a major component on several trend reports, beyond AI, ML & Data Engineering. The Architecture & Design report described "data plus architecture" as the need for software architecture to adapt to consider data. The DevOps and Clouds report had data observability as a critical need. Data mesh also showed up in multiple reports.

PREDICTIONS FOR 2023

• Data engineering teams will spend more time on FinOps / Data Cloud cost optimization
• Data team roles will further specialize
• Data gets meshier, but central Data Platforms remain
• Teams achieve a faster time to resolution for data anomalies
• … just see the article ;)

• A more holistic approach to AI across organizations
• From SaaS to “MaaS” - AI is likely to move in the direction of “Model-as-a-Service”, or MaaS, replacing the current “Software-as-a-Service” (SaaS) paradigm.
• Further democratization of AI

• Web 3.0 (the Semantic Web),
• The Internet of Things (IoT),
• Wireless Technologies,
• Deep Learning AI,
• Autonomous Vehicles,F
• Augmented Reality,
• Blockchain Technologies.

How much data and analytics governance platforms will rise, how important automation will be and what it brings to the table, data democratization, data mesh, and data fabric. Here you will find all the newest trends wrapped up in one article.

By 2025, public cloud solutions will account for 51% of IT spending in these markets. An article with the top Cloud Technology to learn about in 2023 will show you what you should be focusing on this year.

• Quantum Computing
• Cloud Region
• Edge Computing
• Secure Access Service Edge (SASE)
• Grid Computing
• Service-Oriented Architecture (SOA)
• Utility Computing
• Disaster Recovery and Backup
• Open Source
• Green Cloud
• Virtualization

DATA ODDITY

Fahri asked GPT (OpenAI Playground) what its top predictions for 2023 will be.

• In 2023, quantum computing will be more widely adopted, especially in the fields of drug development, material science and  finance and quantum cryptography. The increased availability of quantum computing technology will pave the way for incredible growth in machine learning, artificial intelligence and Internet of Things solutions.
• Blockchain technology will continue to revolutionize industries in 2023, driving innovation in areas like finance and cybersecurity. We will see more companies using blockchain for things like product tracking, smart contracts, digital funds and supply chain validation. Blockchain technology will become increasingly embedded in our everyday lives as companies embrace it.

PODCAST

Listen to the Super Data Science podcast episode with predictions on the future of AI. Sadie arrived prepared with some data science trends. You will learn:

• A recap of 2022 predictions
• Data science trend predictions for 2023
• Data as a product 
• Multimodal A.I. models
• The data mesh
• Privacy & AI Trust
• Environmental Sustainability

The hosts share the best moments from the year of podcasts and more:

• releases in Google software tools
• best podcast episodes along with facts and metrics that we don't know

A few takeaways:

• The architect is a technical leader. A vitally important part of the architect’s role is communicating decisions effectively. Architecture decision records (ADRs) are not a new concept, but increasingly we are seeing companies adopt them and make them standard practice.
• Creating effective software development and delivery platforms, using DevOps and platform engineering practices, can help reduce developer cognitive load and increase positive business outcomes (with regards to DORA metrics). Matthew Skelton’s and Manuel Pais’ book, "Team Topologies", is the gold standard here.