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A7. Real-time data acquisition and analytics will become increasingly important given decreasing value provided by historical #data that have been somewhat useless in supply chain planning

A7. Data will continue to grow at a nutty pace, organizations will continue to stockpile it, replicate it and lose it without any deep understanding of the relative value of it. Data Privacy and Data Security cannot be an afterthought.

A7. Data analytics moves to the edge. Today, most analysis is done in the cloud which requires massive amounts of data to be sent from the edge to the cloud. Data growth is primarily at the edge.

A7 Data will continue to be the shining star as model evaluation and tuning becomes a focus for many organizations. There is an essential need to continually train AI models. Right now, about 90% of models are updated at least monthly according to our report.

Q7. As mentioned, data will grow at the edge with IoT-powered use cases driving real time decisions and actions.

A7 Like low-code AI, AutoML will grow in the enterprise. ML is already a complex set of processes that can be labour intensive. Automated ML intros a set of prepackaged methodologies to make producing ML models easier + faster.

A7 Enterprise stakeholders w/o programming skills (data analysts, IT engineers, etc.) will be empowered to create accurate ML models to help automate various aspects of the business.

@unsolvedmistry Yes, data is still the "fuel" for things like AI and larger the relevant data set, the better the AI outcomes.

@larry_lunetta It's a new way of thinking for most IT departments. The cloud isn't the center anymore.

in volume, considerably higher the demand will be to determine more context and actionable insights.

Q6. We are seeing the democratization of tech with the popularity of low code + no code giving access to app creation + evolutions that were once complex.

Q6. The advent of low-code AI allows users to create friendlier tech solutions that are intuitive, easier to interact with, and integrate with large systems.

A6. The movement of AI/ML to the edge. No need to send data around the world to get value - do it locally. Take action fast.

A6. Many vendors will continue to overhype it, consumers will underestimate the power and presence of it and business will continue to find innovative ways to harness it.

A6. AI continues to get more grounded in productive use cases vs. theory. Also, IT teams will become more educated and savvy about how to best use AI and how to separate "AI Washers" from real solutions.

A6. #ML and #AI are essential for businesses transforming into #autonomousdigitalenterprises. With ML-driven IT Operations, businesses can obtain insights from ever-increasing data & drive cost and efficiency gains (1/2)

@Llanor_A A6 on low-code AI... does that raise issues of transparency, accuracy, governance if AI goes self-service?

A6. Businesses will increasingly use AI and ML-based alert noise reduction, root cause analysis, and #predictive recommendations to ensure service performance and optimization (2/2)

A6 AI lifecycle management will be a focus for organizations to strategically partner with companies that offer solutions. The process of build, train, validate, deploy, monitor is a continuous cycle and organizations who want to be industry leaders will focus on it

Also, there is an increasing recognition of the social and cultural implications of AI that will influence how the technology is used and how the results are interpreted.

@szisk A6 There is always some negatives when we go in this direction. I don't believe it will be completely self serve. There will be and has to be some governance.

@unsolvedmistry A6 agree on lifecycle management, but will organizations look to specialists (e.g. DataRobot) or hope for help from Einstein, Cortana and the like?

@larry_lunetta couldn't agree more, without fully operationalized #AI and #ML use cases the hype will fade fast

@larry_lunetta We are definitely seeing an increase in practical applications of AI and will continue to as teams look to improve efficiencies.

A5. Sustainability. We are getting to grips with how much data processing is fueling digital transformation and with it the amount of energy that it takes to accomplish that transformation.

@jamesmaguire A5. IoT & the proliferation of smart devices will also continue to push the demand on cloud computing.

A5. "Cloud First" will fade and be replaced with "Edge First". So many new use cases and value being created at the edge - that can't handle cloud latency and costs.

Q5. We've already talked about this. The cloud is not a place but an experience and we will see workloads move between the edge, the datacenter and the public cloud based on resource availability, performance requirements and economics.

@larry_lunetta So the data center lives on for the foreseeable future? Decades?

A5. Every time I hear "cloud" I think of my favorite Dad Joke "What are clouds made of Daddy?"...."Mostly Linux servers son, mostly linux servers".

The definition of the data center is also changing with new types of distributed architectures increasing the efficiency of that form factor. It keeps getting re-invented.

@Llanor_A You're saying "push" demand - meaning lower it?

@brucekornfeld meaning increase the demand

A5: Like we have said, organizations have realized the importance of a hybrid work model and will need to continue digital transformations to enable more access to cloud services and products.

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@unsolvedmistry Ah someone used "digital transformation" -- we needed that! :)

@unsolvedmistry Agree, the cloud scale and flexibility is a critical component of hybrid work solutions.

Cloud is part of the hybrid IT landscape extending to the edge - cloud AND edge NOT #cloud OR #edge

@RamChakravarti2 "cloud AND edge" So true!

A4. New technologies to better manage edge computing systems - namely OTM. It's not just about the data, but gaining insights of the data, how it's being used, improve security, AI, predictive failure analysis.

A4: Generative AI is an ML method of data collection that we should see a lot more of in the next few years. Models that can learn from themselves and generate new data. Synthetic data is similar and will become increasingly popular when initially training a model.

A4. I think we've jumped on this already, so I guess we see it coming. AR/VR will become common place vs. that "cool thing the kids have". You will have virtual factory tours, virtual product demos, home tours, executive briefings, board meetings all fully immersive.

A4. Home/remote work connectivity, security and management is undergoing a tremendous renaissance due to hybrid work. The user experience and IT services will be indistinguishable between the home and office.

@brucekornfeld What is OTM?

A4. Extent to which low code/no code platforms will proliferate in enterprises. Tech talent shortage will force organizations to adapt (1/3)

@larry_lunetta agreed 100%, amazing what IT has accomplished for their companies and teammates over the past 18 months.

Just how much AI has become a part of our everyday & business lives. From improved chatbots to workplace automation, AI will continue to evolve how we live.

A4. Adapt with democratization of #AppDev with LC/NC platforms as quick-win for business functions, versus waiting for high-demand techies to build solutions for them. (2/3)

A4. Part of a bigger theme “democratization of tech” with the combo of LC/NC & IPA (3/3)

A4. The surprise there will be how subtle that integration will be accomplished + how we’ll adapt to accommodate it. Specialization in AI-driven tech will see a greater demand for certain kinds of jobs, inc. data scientists, analysts, & tech marketers.

A3. I'd like to say "social media", but we're doing this on twitter, so I think I cannot.

@ktcoppins Ha! Social's getting bigger, I'm afraid....

A3. Historical data analysis & insights as a source of value & differentiation. For decades, global supply chains relied on historical data for forecasting & planning. (1/2)

A3 Cloud Storage. Organizations are realizing that sending data from edge to cloud is costly and in many cases technically difficult. Analyze data first, only send to cloud what is absolutely necessary.

A3. With supply chain disruption due to COVID-19, historical data is increasingly unreliable & not trusted source of value now (2/2)

A3: Data collection that is not opt-in and unethical will soon die off as the social responsibility of organizations plays a big role in how they collect and source data.

A3. Cloud-only solutions. Customers are looking for the right mix of edge, datacenter/on prem and cloud-based infrastructure options.

@brucekornfeld So what will replace Cloud Storage? Storage at the edge?

@larry_lunetta 100% agree.

A3. Also virtual meetings & events. Overall fatigue in the past 2 years with all things virtual. A return to office/hybrid return to office format will put ceiling on use & growth of these technologies

@RamChakravarti2 Agreed. Technology needs to be continually updated as historical data is no longer relevant.

Analyze and "thin" the data first so there isn't as much to store. Send some to cloud, store some at the edge.

@RamChakravarti2 You don't think Zoom has changed the world forever? People don't like to fly....

@RamChakravarti2 I agree, but I also think there will be more AR/VR as per the earlier exchange. Hybrid is here to stay, but people are social creatures. Question is, Q1 for back to the event circuit? Q2?

@RamChakravarti2 its true - there really is nothing like a face to face meeting with a whiteboard and being able to "read the room"

@jamesmaguire some things have changed, but there is serious fatigue to the omnipresent virtual setting

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so many colleagues I've talked to are dying to get out of the house (maybe away from their spouses : )

@RamChakravarti2 I 100% agree, but, I'm finding bigger companies are not yet ready to take the risk of putting people on planes - or letting outsiders "in". It remains a challenge.

@ktcoppins I think it's more like Q2 - but agree!

@brucekornfeld Ah, absence makes the heart grow fonder!