1. Network monitoring will be essential for moving forward in the digital landscape.
This article in Techwire Asia outlines why network monitoring is essential for the digital landscape which
is hastening at an unrelenting pace with new technologies, platforms, and business models continuing to reshape the industry. In the face of this disruption, organizations will need to look toward new, digitally enabled business models that fully leverage innovations.
Organizations worldwide must consider how they can reduce their digital waste – and IT monitoring will play a huge part in this endeavor. To manage IT waste the teams in charge need to be well-informed, therefore businesses should focus on monitoring strategies to ensure equipment maximization and longevity.
Data governance, cybersecurity, compute infrastructure, and holistic and cohesive technology architectures are all key focus areas this year and beyond. IT and OT must work together, which is becoming even more significant due to the accelerating digitalization. Digitalization and remote working arrangements will remain very relevant topics for organizations in the digital landscape. Monitoring these decentralized infrastructures will continue to be a challenge that many companies will have to contend with going forward.
2. Organizations are transitioning from manual to automated incident responses.
This piece from The New Stack provides instruction for moving away from manual actions with automated incident response.
As companies ramp up their digitization efforts, there’s a lot of extra pressure from growing incidents, which in turn puts team health at risk with potential burnout and attrition. This extra work only increased attrition in an era commonly referred to as the Great Resignation. As teams lose people and look to hire for these open positions, their workloads only grow. Something must change, and IT leaders are looking for a few strategic initiatives to help.
Gartner introduced the term automated incident response last year as an evolution of the long-standing definition of incident response. According to Gartner, “Automated incident response (AIR) solutions automate incident response processes by enabling centralized alert or incident routing.” This change reflects the growing need for teams to adopt automation and stems from the increasing complexity in our technology environments. When it comes to automation, it’s important to focus on reducing operational loads to get more done while at the same time increasing organizational speed and innovation. Teams can do this with a crawl-walk-run approach. The key is starting where your organization is today and having a plan for continued maturity.
3. Here are five ways to improve spreadsheets for business workflows.
A recent publication from Infoworld provides insights for ways to improve business workflows with spreadsheets.
Given that spreadsheets are the Swiss Army knives of office tools, the suitable alternatives for modernization span several different types of platforms. For larger-scale and more complex workflows, development teams might consider workflow automation platforms. Depending on the data types, workflow, presentation requirements, security considerations, and collaboration required, different platforms are worth considering.
Here are five approaches to replacing spreadsheets that draw on different platform types:
- Build a data visualization or dashboard.
- Migrate to a SaaS or no-code database.
- Enable departmental collaborations and workflows.
- Hyper-automate integrations and data flows.
- Build a low-code or no-code application.
Although there are many options for replacing spreadsheets, keep in mind that apps can’t easily replace the openness and all the flexibilities of using them. People love the versatility of spreadsheets and the ability to add columns, change data, and create formulas on the fly—something that you’ll likely control when migrating the data and workflow to other platforms.
4. Understanding the differences between AIOps and MLOps can be your secret weapon.
This article is from CIO Insight describes the differences between AIOps and MLOps.
AIOps and MLOps are both essential components of an AI-powered business. Artificial intelligence for IT operations, also known as AIOps, is a category of tools and strategies that allows organizations to take advantage of big data and machine learning. Machine learning operations (MLOps) is a framework that supports a continuous, AI-based learning system for various processes and methods. It combines both people and tools, covering three aspects of machine learning: design, training, and deployment.
AIOps uses artificial intelligence to automate and optimize tasks in enterprise IT infrastructure. This process is called self-optimizing and the goal is to minimize downtime and maximize performance. AIOps is capable of helping organizations solve these problems:
- Anomaly detection;
- Compliance auditing;
- Noise reduction;
- Regulatory reporting; and
- End-to-end process monitoring.
MLOps solutions make it possible to collect, analyze and interpret large amounts of data. Then, with various machine learning algorithms, these systems can detect anomalies in your infrastructure or applications that might be causing performance issues. MLOps is able to help companies solve these type of problems:
- Deployment flexibility;
- Repeatable workflows;
- Risk mitigation;
- Regulatory compliance;
- Automated development; and
- Rapid threat response.
If you want to improve efficiency by streamlining certain operational tasks and processes, you might find that using MLOps technology is better suited than using AIOps technology. Alternatively, suppose your main goal is to automate machines or identify potential risks or issues before they become actual problems. In that case, you might find that using AIOps technology is better than MLOps technology.