No country can afford to be left behind by the AI industrial revolution. With the global AI market set to be worth over $126 billion by 2025, it has become the new technological battleground. Looking to maintain its strong position as a leader in AI development, the UK cannot afford to rest on its laurels. To ensure this doesn’t happen, the UK Government has announced an ambitious project; the National AI Strategy. With a goal set of having the benefits and impact of AI felt across the country in just 10 years, the National AI Strategy aims to help the UK meet global challenges such as net-zero, health resilience and environmental sustainability by promoting the use of artificial intelligence.
To achieve these aims, the National AI strategy picks out several areas the UK should be focusing its efforts on. These areas include attracting the right talent and development of AI-related skills, involvement in international research initiatives, creating and promoting specialised finance and VC programmes as well as access to and data policy frameworks. Crucially the plan promotes the need for both public and private sector involvement, to make the most of the latest AI tools, systems, and processes if the UK is to become a true AI powerhouse on the global stage.
How to start the right way
A glance at Gartner’s recent 2021 Hype Cycle for Emerging Technologies shows you how pervasive AI is expected to become in the coming years. AI-augmented design, physics-informed AI, and AI-driven innovation may one day soon be areas discussed at a boardroom level, but right now for most businesses, these are areas unlikely to be delivering immediate impact.
Instead, organisations need to identify the areas where AI supports and empowers their workforce, while also fitting in with existing processes and systems without causing or adding complexity. Consequently, and with the guidance provided by the National AI Strategy, enterprises should look to start their AI journey, via its use in IT operations (AIOps) to improve the process of limiting alert fatigue, proactively detecting performance problems, and avoiding outages too.
Nonetheless, while AIOps has a lot of promise, so far there are precious few examples of its successful implementation. A core reason is the lack of high volumes of quality data to develop and train models. Deploying AIOps requires access to vast amounts of operational data yet making sense of all this data is not a simple task. Using a one size fits all approach to automate processes, detect anomalies, and determine causality simply isn’t practical and is more than likely to fail. Data is a central factor in how well AI performs, from basic operations to how fair a given model reacts in real-world scenarios.
Observability: Making AIOps a success
What businesses need to do instead is invest and focus on tools that provide the flexibility to ingest and index data from multiple sources. These sources include infrastructure, networks, applications, existing monitoring tools, and deployed software agents. Once collected, data from these sources needs to be normalised before it can be passed on to real-time analytics over data in flight or for historical analysis over larger datasets at rest.
To successfully deploy AIOps into the enterprise means managing three core constraints of volume, accuracy, and precision. To do this, businesses need to understand the challenges each one presents.
Starting with volume, the problem begins with the fact that vast amounts of operational data flow out of systems in hundreds of different formats and over dozens of protocols. Unfortunately for AIOps, much of this data is ineffective and lacks the quality needed. Even with the terabytes and petabytes of telemetry data, there is still a lack of the high quality, representative data that AIOps needs.
The issue of high-quality data, therefore, causes issues with the next challenge that needs to be tackled, accuracy. Thanks to the large variety of data sources consumed, businesses face a challenge with consistent data quality and integrity – two areas that are vital to model performance. In
truth, it is not just AIOps that has a data quality issue. All AI projects need not only quantity but quality in the data they use.
The final challenge enterprises face with AIOps – precision – arises with the iteration process of AI models. Successful iteration not only requires multiple tests with the same parameters and data sets to be run but also involves evaluating the variability between tests to ensure the ongoing precision of the tools being put in place. Failure to manage the volumes and accuracy of data being used will mean that AIOps tools can never achieve the levels of precision that a successful deployment requires.
Unlocking the value of AIOps with observability pipelines
Accessing the full potential of AIOps needs organisations to solve these three key challenges and gain a better grasp of the data needed to enable it.
One of the best ways to do this is with unified observability pipelines. By getting the right data, in the right format, to the right location, observability pipelines drive operational efficiencies that are needed for successful AIOps deployments. Consequently, businesses benefit from more effective digital transformation programmes, cost reductions and overall improved performance.
With an observability pipeline in place, organisations benefit from the much-needed control over data that will enable AIOps to be a success. By unlocking data from silos and providing a single point of data enrichment, filtering, refinement, and routing to any AIOps platform, observability pipelines allow organisations to access the accuracy and precision they need while managing huge volumes of data too
