Intelligent Data Management with Machine Learning and Artificial Intelligence

The next step of your digital transformation – Intelligent Data Management

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.

Useful Links

White Paper: Why Organizations Need an Intelligent Data Strategy

Shows opened padlock to represent security threat

Why automation will become the most reliable way of preventing, detecting, and mitigating security threats

Modern organisations are taking advantage of new and innovative technology, transforming their business operations, continuously improving efficiencies, delivering high levels of customer service, and unearthing new opportunities for products and services that wouldn’t have been conceivable 5-10 years ago. This transformation comes at a price however, and the same technologies used to drive businesses forward are also being deployed maliciously, primarily for financial gain (71% of data breaches were financially motivated, according to Verizon’s 2019 Data Breach Investigations Report) or to gain a strategic advantage. Businesses face greater numbers of security related events more frequently and in different guises than they did five years ago, with attacks on individuals using social channels becoming more prevalent. Alongside this, workforces are hypermobile, well used to downloading applications and accessing, storing and transmitting corporate data anywhere and on any device. In order to keep this edge data secure, businesses must now do more than simply protect against attacks, they must try and prevent them from happening in the first place, wherever the user happens to be and whatever device they happen to be using.

So how do you do that?

The first step is to identify genuine threats from the vast swathes of security incident data that is collected for analysis from a myriad of different sources. They are deliberately not easy to spot, and attackers will use next generation technologies such as AI to hide amongst legitimate traffic. However, some AI and machine learning driven security solutions can analyse massive amounts of data from across any number of data sources, using the power of the cloud to process the analysis right across the organisation, from the edge to the core.

Oracle is one such security solution, enabling businesses to secure modern hybrid clouds with a set of security and management cloud solutions, which draw on data gathered from logs, security events, external threat feeds, database transactions and applications. It uses AI and machine learning technology to detect malicious intentions, then automates the process of finding available security patches and applying them, and all without downtime.

In addition, Oracle’s automated services encrypt production data and enforce user controls, so you don’t have to do it manually.

As we’ve mentioned, to protect data from edge to core, organisations must implement a multi-layered strategy, and when using the cloud, don’t assume that all data protection responsibility lies with the cloud provider. Most cloud providers assume a shared responsibility model, where they offer assurances around the security of the data held on their infrastructure, but access to that data and SaaS data is usually the responsibility of the customer. Consider layering your security solutions to protect every layer of data and each access point, including a Cloud Access Security Broker and Identity Access Manager which will monitor, detect threats, automate the identity management process, sending alerts if anomalous behaviour occurs and remediate wherever possible, without the need for human intervention. Making this work across heterogenous technology on different platforms, on-premises, in the public cloud and in private clouds, is the trickiest part, but Oracle has got it absolutely spot on. Consider the manual alternative, thousands or even millions of security alerts coming into different management consoles, to be sifted through, users to be authorised and behaviour to be monitored and analysed, patches sought and applied and data to be encrypted. It doesn’t bear thinking about.

WTL offer a range cybersecurity solutions which employ next-generation features to ensure you remain one step ahead of the cybercriminals.

Useful Links

Verizon’s 2019 Data Breach Investigations Report

Oracle Cloud Essentials – Secure and Manage Hybrid Clouds

Oracle’s Top 10 Cloud Predictions 2019