Top Trends in Data and Analytics — Trend # 12: Expansion to the Edge @ Gartner 2022


By 2025, more than 50% of enterprise-critical data will be created and processed outside the data center or cloud.


This is an excerpt of the publication below, with the title above, focusing on the topic in question. For the full version of the article, please, refer to the original publication.


Gartner
Top Trends in Data and Analytics, 2022

By Rita Sallam, Ted Friedman, and 37 more
11 March 2022


Excerpt by


Joaquim Cardoso MSc.
The Health Revolution

Multidisciplinary Institute for Better Health for All
May 27, 2022


They have the potential to transform your enterprise, and will accelerate in their adoption over the next three years.

You should decide whether to proactively monitor, experiment with or aggressively invest in key trends based on their urgency and alignment to strategic priorities.


The top D&A trends and technologies do not exist in isolation; they build on and reinforce one another.

We have selected our trends for 2022 in part based on their combined effects. Taken together, our top data and analytics technology trends for 2022 will help you meet your organization’s top strategic priorities to anticipate, adapt and scale value.

This, in turn, will enable you to encourage innovation and put in place success metrics and incentives that emphasize learning and reward innovation.

  1. Group 1: Activate Dynamism and Diversity
  2. Group 2: Augment People and Decisions
  3. Group 3: Institutionalize Trust

Group 3: Institutionalize Trust

9.Connected Governance
10.AI Risk Management
11.Vendor and Regional Ecosystems

12.Expansion to the Edge



12.Expansion to the Edge


Analysis by: Ted Friedman, Pieter den Hamer, W. Roy Schulte, Paul DeBeasi

SPA:


By 2025, more than 50% of enterprise-critical data will be created and processed outside the data center or cloud.


By 2025, more than 50% of enterprise-critical data will be created and processed outside the data center or cloud.


Description:


D&A activities are increasingly executed in distributed devices, servers or gateways located outside data centers and public cloud infrastructure, closer to where the data and decisions of interest are created and executed.

For example, the memory, storage and compute capacities of hardware built into various types of endpoint devices continue to expand, making larger, more sophisticated workloads possible.

Despite these advances, edge environments bring substantial constraints on resources and flexibility, and the functionality they offer for D&A workloads is different from that offered by data centers or cloud environments.

This means D&A leaders and their teams must enhance their skills and rebalance their architectures.


D&A activities are increasingly executed in distributed devices, servers or gateways located outside data centers and public cloud infrastructure, closer to where the data and decisions of interest are created and executed.


This means D&A leaders and their teams must enhance their skills and rebalance their architectures.


  • Data, analytics and the technologies supporting them increasingly reside in edge computing environments, closer to assets in the physical world and outside IT’s traditional purview.
  • A diversity of use cases is driving the interest in edge capabilities for D&A, ranging from supporting real-time event analytics to enabling autonomous behavior of “things.”
  • Edge-generated data is growing dramatically in terms of volume and diversity. Since it often won’t be desirable or possible to collect and process this data centrally, organizations need to support distributed data processing and persistence models.
  • Digital business solutions increasingly demand faster data distribution and reduced latency, requiring a shift of data and data processing away from the cloud and traditional data center environments.
  • Data sovereignty and solution reliability concerns are generating interest for data to be stored “locally” in edge environments.

Implications:

  • By placing data, analytic workloads and AI capabilities at optimal points ranging out to endpoint devices, interesting new applications of analytics and AI are coming into focus for D&A leaders and their teams, including more real-time use cases.
  • By using distributed computing resources and spreading the load across the ecosystem, D&A teams can more broadly scale their capabilities and extend their impact into more areas of the business.
  • Pushing D&A capabilities toward edge environments can also bring benefits in the form of greater fault tolerance, remote monitoring, remote operations and autonomous behavior.
  • With the distribution and complexity of edge environments comes a greater challenge from a D&A governance perspective — ensuring quality, security, privacy and consistency definitions/models are all more difficult.

Recommendations:


Data and analytics leaders should:

  • Evolve their strategies and practices to de-emphasize centralized approaches and architectures, while developing new capabilities that can be deployed, executed and administered in various locations along the cloud-to-device continuum.
  • Develop skills and experience in edge environments, including complex solutions and governance. Communicate and collaborate with stakeholders and teams that have historically been outside the sphere of influence of traditional D&A, e.g., operational technology (OT) teams.
  • Provide support for data persistence in edge environments by including edge-resident IT-oriented technologies (relational and nonrelational database management systems), as well as small-footprint embedded databases for the storage and processing of data closer to the device edge.
  • Optimize distributed data architectures for their use cases by balancing the latency requirements against the need for data consistency (between cloud/data center and edge, as well as across edge environments).
  • Use edge topology to address data sovereignty and availability requirements while minimizing risk by extending D&A governance capabilities to edge environments and providing visibility through active metadata.

Changes Since Last Year


The main change in this trend compared to last year is the degree of acceleration. 

Deployment of edge D&A solutions has continued to grow due to two main forces: 

  • the demand for automation and control of remote environments to mitigate health, safety and resourcing constraints, and 
  • the increasingly complex regulatory landscape emphasizing data sovereignty.

Evolve their strategies and practices to de-emphasize centralized approaches and architectures, while developing new capabilities that can be deployed, executed and administered in various locations along the cloud-to-device continuum.


Originally published at https://www.gartner.com


About the authors


By Rita Sallam, Ted Friedman, Erick Brethenoux, Donald Feinberg, Soyeb Barot, Lydia Clougherty Jones, Malcolm Hawker, Eric Hunter, Jason Medd, Robert Thanaraj, Melody Chien, Mark Beyer, Ehtisham Zaidi, Mayank Talwar, Pieter den Hamer, W. Roy Schulte, Paul DeBeasi, David Pidsley, Sumit Pal, Joe Maguire, Yefim Natis, Shaurya Rana, Guido De Simoni, Afraz Jaffri, Alan D. Duncan, Julian Sun, Gareth Herschel, Avivah Litan, Bart Willemsen, Svetlana Sicular, Farhan Choudhary, Sumit Agarwal, Mike Fang, Adam Ronthal, Andrew White, Carlie Idoine, Jorgen Heizenberg, Peter Krensky, Sally Parker

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