Gartner’s 2022 Top Strategic Technology Trends. Old Problems. Old Trends. New Names. – Forbes
Every year the Gartner Group releases their list of top strategic technology trends. The three themes for 2022 are “engineering trust, ” “sculpting change” and “accelerating growth. ” At the outset one must be impressed with how Gartner neologizes so creatively 12 months after yr. But are usually the trends really new or just renamed old ones? And are they all just aspirations?
The Gartner 2022 Trends
- Trend 1: Data Fabric
- Pattern 2: Cybersecurity Mesh
- Tendency 3: Privacy-Enhancing Computation
- Craze 4: Cloud-Native Platforms
- Development 5: Composable Applications
- Trend 6: Decision Intelligence
- Pattern 7: Hyperautomation
- Trend 8: AI Engineering
- Trend 9: Distributed Enterprises
- Trend 10: Total Experience
- Trend 11: Autonomic Systems
- Trend 12: Generative AI
Let’s look at each of them, noting their age, overlap and real nature.
Trend 1: Data Fabric
Data has always been essential to operational and strategic effectiveness. “Making data available everywhere” has been a priority for decades. Accessing the right information at the right time across multiple platforms and applications has always been a problem. “Analytics” is how we decided to brand both the problems and solutions. Old issues; new names. While “data fabric” is cute, it’s just the particular latest way we describe data nirvana, but in the proprietary technologies world, it remains a pipedream.
Should we continue to clean, integrate plus present structured and unstructured data? Of course , through whatever cost-effective means we can. But we should also recognize there are limits to what we may cost-effectively achieve, especially when all of us pursue best of breed data and applications strategies? Gartner furthermore argues that data fabrics “can reduce data management efforts by up to 70%. ” How in the world is this a knowable number?
Tendency 2: Cybersecurity Mesh
Everyone knows that the particular number plus nature of cyberthreats will be increasing. We also know that you will find no perfect solutions – and never will be. All of us know that will most companies do not fully understand the threats and therefore underspend in cybersecurity or spend the wrong method. Statements like these are what you hear from vendors:
“Cybersecurity mesh is a flexible, composable architecture that integrates widely distributed and disparate security services.
“Cybersecurity mesh enables best-of-breed, stand-alone security solutions to work together to improve overall protection while moving control points closer in order to the assets they’re designed to protect.
“It can quickly and reliably verify identity, context and policy adherence across cloud and noncloud environments”
These are normative plus prescriptive statements, not actionable ones. Of course we want “ composable structures that combines widely dispersed and disparate security solutions. ” Who doesn’t? There’s always an ocean between what we want and how to get this. This the not trend, just an old aspiration.
Craze 3: Privacy-Enhancing Computation
How long have we all been talking about “privacy”? Why is privacy still just a great bar or party conversation? Because too many business models only work when there’s no privacy. Worse, how many Americans really worry about digital privacy? (I understand that Europeans be concerned more about privacy than People in america and that personal privacy itself is usually an issue/non-issue depending upon where countries sit throughout the democratic/authoritarian spectrum. ) Americans say they’re concerned about privacy, but “ Despite Privacy Concerns, Consumer-Level Inaction Reins Supreme . ” While there’s lip-service to privacy, retail and other vendors continue to bask in their access in order to personal information everywhere, all the time. They need it. They pay for this. They sell it. Gartner’s “privacy-enhancing computation” is definitely a lovely name for an old problem that no one cares enough about to change their online behavior. (Oh, and where’s almost all this legislation everyone keeps talking about? )
Trend 4: Cloud-Native Platforms
This important trend can be well underway. Cloud-native offers real traction among companies still struggling to repurpose their applications portfolios as well because the use of cloud-native systems to build applications built upon “ containers, microservices, serverless functions plus immutable infrastructure, deployed via declarative code are common elements of this architectural style … these techniques enable loosely coupled systems that are resilient, manageable, and observable. ” Yes. Is this particular the future? No question. So what’s brand new here?
Trend 5: Composable Applications
“Composable” programs (and the particular larger composable enterprise architecture) is since much regarding developing a good API- and event driven- culture as anything else. It’s also about legacy apps preservation, because more plus more businesses try in order to migrate away from aged architectures all the way to cloud-native status without too much upheaval – which has been the goal for years. More recently, we added low code/no code with regard to the development of microservices-based applications while reusing APIs as much as possible, ideally within a “modern” governance model.
New-est name to have an old goal.
Trend 6: Decision Intelligence
How is certainly this not a homage to analytics, circa 2010? In 2021 we think about automaton. This is an old trend along with a few slightly new twists.
Development 7: Hyperautomation
This 1 speaks to automation styles. If a single tracks the 4IR and the Future of Work , a person already understand all about automation. Hyperautomation, according to Gartner, is “a disciplined, business-driven approach to rapidly identify, vet and automate as many business and IT processes as possible. ” This pattern has already been in play forever. Under the umbrella associated with robotic process automation (RPA) where processes are modeled, mined, eliminated, modified, or even automated, the goal has been a trend — and an ongoing aspiration — for some time.
Trend 8: AI Engineering
AI engineering is about best practices for repeatable design, development and deployment. OK. But is this really a brand new trend? Or just one that’s been around for a while now, at least for five years?
Pattern 9: Distributed Enterprises
This is where digital transformation meets edge computing.
Trend 10: Total Experience
This has been aspirational forever. Just Google this. Who’s doing it? There are usually pockets — such as returning unboxed items from Amazon to Whole Foods — but it is still illusive. Who wouldn’t want seamless, easy and fun TX?
Trend eleven: Autonomic Techniques
Wow. Sure. Obviously: “ Autonomic systems are self-managed physical or software systems that will learn from their own environments plus dynamically modify their own algorithms in real time in order to optimize their particular behavior in complex ecosystems. ” Why not? But a trend?
Trend twelve: Generative AI
Absolutely a long-term tactical objective. If only generative AI was the discernible tendency.
Let’s pretend that Gartner invited us to describe the particular trends we see. What would they look like? Not what should they look like, yet what they actually are. First, let’s reorganize the Gartner trends into baskets and then assess how well they’re doing out there.
Three baskets jump out:
- Decision Intelligence
- AI Engineering
- Autonomic Systems
- Generative AI
- Data Material
- Cybersecurity Mesh
- Privacy-Enhancing Computation
- Cloud-Native Platforms
- Composable Applications
- Dispersed Enterprises
- Total Experience
So what’s happening away there that might be described since trends? Survey data suggests that there’s still some skepticism about how quickly executives believe these people have to ramp up their AI plus machine learning (ML) investments . That said, there is a steady increase in AI/ML pilots. Therefore that’s a trend that will accelerate over period so long as impact is quantifiable. We perform know that will trends within RPA are usually positive. We know that there are new “Centers of Excellence” being created and we also know that the adoption of AI/ML is vertically driven: some industries are piloting applications more than others. There’s also a rank-ordering associated with AI/ML programs, like chatbots (“conversational AI”) and selection applications (such as selecting the best/worse candidates regarding loans, “admission, ” and any binary decision). We can say that adoption rates are dependent upon strategy, which has yet to fully embrace intelligent systems as the future that will define profitable growth. Deloitte reports that the particular distribution across “path seekers, ” “transformers, ” “starters” and “underachievers” in AI is challenging, suggesting that more than two thirds of companies are not really “high achievers. ” Deloitte also reviews that businesses are bullish on AI/ML, that eventually they expect AI/ML to really impact their own business. So the real trend is cautious optimism.
Data developments? Analytics is now mainstream – at least by name and intent. But thoroughly clean, integrated, accessible data remains elusive. The reason why? The old standbys, like competing data formats across apps, “lost” data, bad information, duplicate data and no enterprise data architecture, are among the problems that have haunted us for many years. So smooth, integrated plus accessible data is an ongoing aspiration. But trend? Yes, if by craze we mean investment tendencies. Make zero mistake this is a good ongoing slog.
Applications architecture is finally getting some grip. Companies are shifting toward microservices-based applications as they remove their legacy applications through life support. “Applications rationalization” is the real thing. It saves money and moves the ball toward the microservices goal line. Container technology has evolved nicely plus, perhaps most importantly, the major cloud vendors offer a variety of options here. Perhaps this will be the true trend. Edge computing is part associated with this, and total experience applications will always be what everyone strives in order to build.
The main problem with Gartner’s (and other) trends lists is they will cannot help but end up being repetitive plus overlapping, especially with prior years. Whilst things move fast in technology land, major trends – like applications structures and AI – will be “trends” for a long time. “Updates” are usually interesting but not that useful. Sure, a lot more people are thinking about microservices and containers than they were last year, and more companies are investing in AI pilots. It is hard to describe truly new styles, so technologies prognosticators rename old ones with catchy names. Yet some of the names are just too obvious. Maybe next year the developments will become new or the (re) names a lot more creative. Or maybe they’ll just be called dreams.