Jul 29, 2020

Automating IT infrastructure amid COVID-19

AI
covid-19
Alessandro Perilli, the GM of ...
4 min
man at laptop
The pandemic and the resulting economic downturn has made the business case for IT automation clearer than ever...

 The pandemic and the resulting economic downturn has made the business case for IT automation clearer than ever and it continues to prove especially important for building resilience in our supply chains. Without extensive automation, it wouldn’t be possible for essential goods from online retailers to get into the hands of consumers at the scale and speed we currently need.

Likewise, automation has supported businesses across many industries in transitioning to remote working, which has proven essential to supply chain continuity amid the crisis. For example, it has allowed operations and security teams to install VPN clients across millions of remote worker’s devices, enabling a smoother and safer transition to a work-from-home model.

New contexts for automation

One of the first and most common use cases for IT automation is in the configuration of computing resources and operating systems, and the provisioning of line-of-business applications. From there, IT automation has evolved to address a significant broader spectrum of processes and tasks. For example, today, some automation engines can be used to set up network devices like routers and switches, rapidly cutting down the time it takes to integrate hardware into business functions like warehouses digitalisation or freight tracking.

Automation can also serve the needs of cybersecurity professionals in unexpected, new ways. Today, CISOs can use certain IT automation solutions to integrate a variety of security products in their portfolio, and to orchestrate how those products jointly perform a triage investigation or an attack remediation. The increased speed in addressing a cyber attack that IT automation can provide is invaluable for a security operations team, often understaffed and overwhelmed by the amount of alerts that a large IT enterprise environment normally generates.

How to approach automation

 Automating business processes and operations that have been carried on for years or decades in a manual way can be intimidating or discouraging. One way to address that complexity is to break a big process down into multiple small, more manageable tasks, and focus on those ones that are the easiest to automate; the proverbial low hanging fruit.

Adopting this approach in a disciplined way, an IT organisation can eventually build the foundation necessary to automate its operations. It’s not just a matter of having the right pieces in the right place. This approach helps build the experience in automation and the team’s confidence, which is necessary to succeed.

As an additional benefit, by the time you approach the largest projects, your team should be at the point where they realize and appreciate the value of infrastructure and process standardisation, which enormously helps any automation project. In fact, while standardisation is a critical building block for automation at scale, attempting a massive standardisation before learning how to automate even the simpler tasks can often lead to project failure.

Appointing an internal chief automation architect to have overall responsibility of the automation project is paramount to success. Without someone very familiar with the many processes that govern your organisation, you will lack the oversight necessary to use automation strategically, rather than just as a tactical tool.

How to develop your expertise

If your organisation doesn't have a great deal of experience in automating IT processes, it’s perfectly appropriate to turn to your industry peers for knowledge. Many automation platforms have online marketplaces, which host a vast array of workflows to automate common tasks and applications. Take the time to review the workflows that others have already contributed, and evaluate whether they’re applicable to your IT environment.

Some automation platforms, especially if they derive from popular open source projects, also play host to large communities, which can provide a great network for support and advice. Interact with these communities to discover what is the real total cost of ownership and learning curve for these platforms, and what are the best practices to integrate them into your operations. There are many other organisations in your position, so pooling knowledge is a very effective way to help make this transition.

Businesses have continued to adopt automation during the pandemic as a way to support business continuity, and this has been particularly crucial for logistics and supply chain companies. But it’s important to note that this is far from a temporary trend; automation has been one of the propellers of digital transformation well before COVID-19 and will continue to serve as a vital technology for the industry’s IT operations well after the crisis ends.

By Alessandro Perilli, the GM of Management Strategy at Red Hat

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

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

 

Lower costs

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.

Deeper insights 

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. 
 

Intelligent automation 

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. 

Increased security 

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

 

Data privacy 

 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.

Connectivity concerns 

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.

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