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A1: Data & AI governance, data fabric, applied observability, decision intelligence, MLOps, Synthetic data, Trustworthy AI, Augmented Analytics, Democratize AI, AI engineering are the 2023 trends. #eWEEKchat

A1:Three trends have momentum. 1) Data analytics are increasingly being asked to drive greater data freedom (freedom to use and to be used) in the multicloud. 2) Data democratization will be the number one competitive advantage in 2023.

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A1 (1 of 4) Organizations will continue to look for ways to reach greater operational efficiencies with #dataanalytics – including how they collect, curate, and activate their #data – to drive business in 2023.

A1 (2 of 4) Data integration will expand to include OT stream ingestion, creating a richer near-real-time analytics ecosystem. Organizations are looking for a single pane of glass approach across structure, semi-structured and unstructured #data categorization.

@Vamsipaladugus1 Data democratization is a big one!

A1. Availability and maturity of tech and skills – in 2023, easier than ever to start using analytics is up, but availability of skills is getting tighter every day

A1 (3 of 4) Data trust will be key with #data catalogs continuing to grow in importance. They provide a centralized intelligence function across the entire organization and are fundamental to our journey of #DataGovernance, quality and curation.

A1. (cont) Increasing heat to adopt analytics in more accessible areas for existing techos – like AIOps, MLOps, etc.

A1 (4 of 4) Automating decision-making will reduce manual decision-making, mitigate bias and speed up business processes that may have been stalled by human decision-makers.

A1. (cont) Business leaders – sales, marketing, even product – definitely hungry for the hype, but use cases for IT and Engineering will grow much faster in 2023

A1: (1/2) #EdgeComputing will shift analytics activity from the core to edge; increased use of AI/ML will deliver actionable real-time insights from data & we’ll see more focus upon the delivery of agility through adoption of #DataOps to improve access, quality and transparency

A1: Data governance - organizations are working to be more productive by have purposeful accountabilities and quality across data. This is especially critical because it's not just the data, it's the logic and applications that turn data into insights

@KatySalamati Great topics Katy - the governance of ML, AI, Analytics in general is still well behind the use cases. Many issues there with compliance, privacy, etc.

A1: (2/2) Combined, these trends will support the growing availability of self service and collaborative use of data.

@jeffhollan Do you see resistance to governance? Or not really?

@AndiMann And I absolutely agree on Trust. One of the preeminent challenges in tech generally, but esp. in Data Analyrics

@AndiMann Trust is key! More and more decision makers find them not trusting the data they are served.

not as much resistance as just not getting overwhelmed. The scope of what organizations expect from data is accelerating - AI / ML / applications. One reason we see a ton of momentum around the #datacloud to bring more together in a single platform

A1: I think the big one is repatriation of workloads from major cloud providers. Companies should be able to run workloads whenever they make the most economic sense and deploy where it can serve the greatest part of the company.

@jeffhollan Jeff, defo seeing a lot of this as a challenged. Great add! The rise of #DataOps & #MLOps is in response to just this. Still a looooong way to go!

A1: Working to move beyond data management, being led by data science, and providing actionable department-level business intelligence

@namessanti I still find repatriation of workloads from major cloud providers to be mildly shocking.

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AI and Data governance, but also we'll see more of a push with privacy and users wanting more protection of their data

@vamsipaladugus1 A1: AI ethics will be a key consideration for DevOps leaders.

@AndiMann Yes, there is a lot of work that needs to be done, especially around privacy, never enough!

@Vamsipaladugus1 great point on ethics coming up fast and urgently

@madhoop I say everything starts with data Data Governance!

@jeffhollan well said!

@KatySalamati So true, we see this every day with leaks - ML/AI turbocharges the damage to privacy if we will allow it to.

@KatySalamati For sure, this is a super challenging balance. Unfortunately we are leaving it mainly to individual engineers to make the call on how much is too much. That has to change, IMO.

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