Instana sale to advance IBM’s hybrid cloud and AI strategy
IBM is buying Instana in a bid to shore up its hybrid cloud and AI strategy.
It’s part of a transformation for IBM towards hybrid cloud management. Instana’s technology will help it to manage the performance of applications running across multiple cloud platforms.
The deal price has not been disclosed.
IBM’s recent/AI acquisition timeline
July 2019 – $34bn – open source software and solutions
June 2020 – $undisclosed – cloud cybersecurity posture management
July 2020 – $undisclosed – AI automation for enterprises
November 2020 – $undisclosed – financial platform migration
November 2020 – $undisclosed – cloud application management
‘Lost revenue and reputation’
Rob Thomas, senior vice president, cloud and data platform, IBM, said, "Our clients today are faced with managing a complex technology landscape filled with mission-critical applications and data that are running across a variety of hybrid cloud environments – from public clouds, private clouds and on-premises
“IBM's acquisition of Instana is yet another important step that we are taking to provide companies with the most complete portfolio of AI-automated solutions to tackle this enormous challenge and help prevent unforeseen IT incidents that can cost a business in lost revenue and reputation."
Mirko Novakovic, co-founder and CEO, Instana, said, "With the added responsibility of ensuring the build and run quality of the software they develop, DevOps teams need a new generation of application performance monitoring and observability capabilities to succeed.
"Instana's observability capabilities combined with IBM's AI-powered automation capabilities across hybrid cloud environments will give clients a full view of their application performance to best optimise operations."
The advantages and disadvantages of AI in cloud computing
Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.
We take a look at some of the benefits and drawbacks of AI in cloud computing.
The benefits of AI in cloud computing
A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.
AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.
Improved data management
AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments.
Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks.
With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly.
The drawbacks of AI in cloud computing
Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.
IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity.
While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.