Digital transformation projects are intended to help businesses improve efficiency by using data to drive strategic and operational decision making. But while efforts are focused on generating actionable insights, much less attention is being given to the underlying infrastructure. Or more specifically, the management of the infrastructure.
Which is why you need an Intelligent Data Management Strategy to support your digital transformation efforts.
Generating insights – and administrative headaches
Currently, Machine Learning (ML) capabilities are directed towards linking disparate data sets and extracting previously unknown insights. Similarly, Artificial Intelligence (AI) is turning those insights into action, accelerating decision-making, automating low-level tasks and flagging anomalous data for review by human operators.
ML and AI are helping to make sense of unstructured data. But at the same time, corporate computing environments are becoming increasingly complex. The exponential growth of data coupled with the use of a disparate set of hardware, applications and services is creating a data estate that requires a disproportionate amount of administrative intervention and oversight.
Under the current paradigm, data is easier to use but increasingly difficult to manage. Unless the administration can be simplified and automated, businesses will begin drowning in data again.
Widening the scope for ML and AI
An Intelligent Data Management Strategy seeks to apply ML and AI technologies to virtually any problem – including systems management. Some vendors, like HPE, are building these capabilities into their hardware stacks, creating an intelligent data platform.
Machine Learning can be used to establish a baseline for normal operations for instance. By monitoring network traffic, server activity, application usage and other variables, infrastructure gains an understanding of what “normal” looks like.
Using the insights generated by ML, AI can then be applied to solving common network management challenges. Where an excessive load is detected, AI can automatically offload processing to reserve servers – or even to the cloud. If a system begins generating suspicious network activity, AI will throttle bandwidth, or even disable the system, until an engineer can resolve the issue.
Automated actions are not limited to problems either. AI can be trained to take proactive steps to ensure the entire stack is performing optimally. This relieves systems engineers of another important but time-consuming responsibility and ensures infrastructure continues to deliver value.
Because AI can make these adjustments in real-time, administrators can focus on other strategic tasks. Automated detection and remediation are also much faster than a similar human response, helping to ensure the entire infrastructure stack is functioning optimally.
To avoid being overwhelmed by unmanageable system complexity in the near future, your business must consider how ML and AI can be applied. Your Intelligent Data Strategy needs to be rebalanced to consider infrastructure overheads alongside analytics and insights.
Contact us today to learn more about adding automation and intelligence to your data strategy – and what you will gain in the process.