Looking at the Crystal ball for 2023!

It has become cliché to be doing market predictions, but it certainly enables Enterprises to get a pulse on the market, get informed, evaluate and strategize for course correction. My post-pandemic 2021 Predictions, highlighted the coming out party for AI/ML Ecosystem across multiple regulated verticals. My 2022 Predictions discussed the rise of the Data Economy and Data becoming the new source code. This year 2023, at a macro level we are moving from an Inflation economy to a Recession and uncertain economy and the general theme is certainly going to be “Doing More with Less” and “Customer Experience is the King”. Let us examine what trends and technologies will play a lending hand in these circumstances.

2023 – Convergence, A Market perspective

2023 will continue to be a year bringing convergence in the market, as use cases become data-driven  – Business and IT Convergence, IT and Security Convergence, Networking and IT Convergence (driven by the rise of SASE and AIOps), all driven by a single source of truth. 

Operational domains – Observability, Security, and Automation will continue to converge as the focus will shift to gaining visibility with Observability, operationalizing with AIOps, and optimizing using FinOps.

Customers will be increasingly seen embracing

  • the Cloud Operating model to guide their multi-cloud journey,
  • the Modern Data Stack for data-centric Enterprise, Cloud Native and Data Applications
  • AIOps Operating Model to democratize, glean insights and operationalize at Scale
  • FinOps Operating Model to optimize budgets and bring Financial accountability for IT planning, ITOps, and DevOps.
  • With the holy grail goal of embarking on an Autonomous Enterprise Journey
New Data-centric Business Models

Gartner’s 2023 Technology predictions also highlight 3 themes – Optimize, Scale and Pioneer. Applied Observability would be a key building block to make an enterprise observable and data-centric. For e.g Gartner highlights Tesla’s use of Observability across sensors and its autopilot software to provide lower insurance premiums to drivers with higher safety scores. Large Telco’s will leverage Observability and AIOps to understand their Network Bandwidth consumption by end users for cross and upselling.

Technology Trends

2023 should see the rise, adoption, and traction of some key technology trends to maneuver the above macro scenarios. They are as follows –

Low Code / No Code Platform for Data Automation and Data Quality

Convergence needs a single source of truth which is challenged by the data-value gap

  • Quick data integration with disparate data sources,
  • Data quality issues – as 80% of time is spent on getting the data in right order – Ingesting different data types, transforming, aggregating, normalizing and correlating data
  • Data Skills gap – using Low Code / No Code Bots and
  • converge data silos with Data Fabric

This can be accomplished with Data automation and Intelligence platforms like Google Dataplex, Azure Data Factory or Robotic Data Automation Fabric.

Decoupling Data producers from Data Consumers – Observability Pipelines

Observability pipelines use Bots, to transform data across multiple schemas – Datasets, DataFrames, Opensearch PStreams, Open Telemetry, OCSF – Open CyberSecurity Schema Format, Parquet, Avro or Snowpipe, enriching with IT and Business context, preserving full fidelity copies in low-cost Observability and Security Data Lakes, redacting for compliance, in-place searching and visualizing the data at the edge, Data Lake, Log and Metrics stores.

They leverage existing data management, AIOps and Enterprise search tools making them more affordable and actionable by enriching and correlating the ingested data.

Data-centric AIOps – Adaptive AI

Data-centric AIOps is based on the notion put forth by AI leaders, that AI/ML models have reached a level of maturation and that the success or failure will be determined by engineering the data.

  • This will need a platform that can address the data issues outlined above, similar to a Robotic Data Automation Platform. Additionally, the platform should be able to leverage models from the best-of-breed AI/ML eco-system of OpenAI, Hugging Face, IBM Watson, AWS Sagemaker, etc., and be able to adapt to the problem at hand.
  • The platform should have an MLOps platform built in which supports Continuous ML where the system can be retrained and relearned from the changed context.

Enrichment – Context is the King!

AI/ML deployments and pipelines to be reliable and trustworthy, data needs to be enriched with the right context, in near real-time. Implementing full-stack contextual data models called Application Dependency Mapping and Impact Maps, become extremely important in pinpointing the root cause of a problem. Leveraging synthetic data with pipeline synthesis, builds trust.

ServiceOps and ChatOps

The Operational pipelines comprise of 3 stages – Detect, Investigate and Remediate. Observability and AI/ML can be very effectively leveraged for Detection and Investigation however remediation needs effective bi-directional automation with your choice of ITSM , notification, or collaboration tools. NLP/NLU-based Recommendation Engines based on real-time and historical Knowledge corpus are needed for quick remediation.

FinOps – Converge with ITAM and Cyber Security Asset Management

FinOps originated as the new framework to bring Financial Accountability for IT planning, ITOps and DevOps. It has somehow only been restricted to public cloud chargeback and optimization. It is a powerful framework and will evolve into ITAM – IT Asset Inventory Insights and CSAM – Cyber Security Asset Insights –  Utilization / Capacity, Change / Vulnerability, Lifecycle Analytics and Compliance/Contract Management metadata captured near real-time in a AI DB.

Composable Analytics for Self-Service personas and Platform Engg. Teams

Empowering operational personas with the right data and right workflows is important to meet the requirements of the Multi-cloud Era. Composable Analytics, refers to an analytics stack comprising of Dashboards, Visualization, Services, and Search which are based on Bots and Pipelines. This makes them modular, adaptable, and flexible to meet the changing business requirements in these uncertain times. Net – Enterprises will focus more on improving their Operational Efficiencies and Customer Experience and should evaluate these technology trends to maneuver macro trends. Let’s evaluate these when we get to the 2024 New Year!

Shailesh Manjrekar
Shailesh Manjrekar
Shailesh Manjrekar is responsible for CloudFabrix's AI and SaaS Product thought leadership, Marketing, and Go To Market strategy for Data Observability and AIOps market. Shailesh Manjrekar is a seasoned IT professional who has over two decades of experience in building and managing emerging global businesses. He brings an established background in providing effective product and solutions marketing, product management, and strategic alliances spanning AI and Deep Learning, FinTech, Lifesciences SaaS solutions. Manjrekar is an avid speaker at AI conferences like NVIDIA GTC and Storage Developer Conference and is also a Forbes Technology Council contributor since 2020, an invitation only organization of leading CxO's and Technology Executives.