AIOps, or artificial intelligence for IT operations, is critical for optimizing IT operations. AIOps combines big data and machine learning to automate IT operations processes, according to Gartner. The most common application of AIOps is the automation of big data management. Event correlation and analysis, performance analysis, anomaly detection, causality determination, and IT service management are some of the other use cases. Smart, connected devices with sensors that generate large amounts of operational data in real time are referred to as IoT.
With the ever-changing data landscape, information is becoming significantly harder to manage. A business's planning, forecasting and reporting needs to be effective and efficient to keep up. AI and ML can take much of the hassle out of these often overwhelming business processes, and Jedox's AIssisted planning solution has proven on all fronts to be a notable example of this technology that provides a clear path to enterprise planning and management success. The strength of a company's AI and ML solutions can be instrumental in determining the success of its business operations, and Jedox has pulled together its extensive resources to focus on superior outcomes for enterprise customers. One of the biggest focuses for enterprises today is forecasting.
Let's face it, if there's anything data scientists (DSs) or machine learning engineers (MLEs) would shy-away from it'll be data that's not yet a Numpy array or a pandas DataFrame. Without insinuating at the fear of databases in data scientists, let's try to understand some of these concepts. Companies that serve millions of customers collect copious amounts of structured and unstructured data every second of the day. To serve these customers with millisecond latency while enabling analytics teams to do their job is challenging, and require carefully designed data architectures. Most companies would have data-engineers to do this for us.
For five years as a data analyst, I forecasted and analyzed Google's revenue. For six years as a data visualization specialist, I've helped clients and colleagues discover new features of the data they know best. Time and time again, I've found that by being more specific about what's important to us and embracing the complexity in our data, we can discover new features in that data. These features can lead us to ask better data-driven questions that change how we analyze our data, the parameters we choose for our models, our scientific processes, or our business strategies. My colleagues Ian Johnson, Mike Freeman, and I recently collaborated on a series of data-driven stories about electricity usage in Texas and California to illustrate best practices of Analyzing Time Series Data.
As a result, companies have gone through a decade's worth of digital transformation in just a matter of months, with the pandemic forcing them to refresh archaic processes with AI, machine learning, and data science technologies. Such technological advancements will continue to evolve and further establish themselves as a critical component to managing complex logistical landscapes – from improving efficiency and mitigating the effects of a global labour shortage, to identifying more robust and dependable ways to move commodities. In a world where uncertainty is the only certainty, AI-enabled order and inventory visibility across shipments will also be vital to'keep the wheels in motion.' Most importantly, to provide real-time updates on changes to arrival times and to identify potential disruptions before and as they occur. Take the recent congestion issues at the Port of Los Angeles, for example.
Artificial intelligence for IT operations, mainly acknowledged as AIOps, is the talk of the town these days, but people talk less about the way to implement AIOps. However, to implement AIOps successfully, businesses must know the process and tools needed at each stage. And, yes, AIOps will help businesses optimize their IT operations. Today, IT companies operate in complicated and extensive environments, often while connecting on-premises and private and public clouds legacy setups. IT leaders, managers, and teams are usually under pressure to serve the business with their end-to-end IT operations and services. The enterprise's core focus is to prevent the most significant instances and any downtime.
In this week's real-time analytics news: HPE launched HPE Swarm Learning, a privacy-preserving, decentralized machine learning framework for the edge. Keeping pace with news and developments in the real-time analytics market can be a daunting task. We want to help by providing a summary of some of the important news items our staff came across this week. Hewlett Packard Enterprise (HPE) announced the launch of HPE Swarm Learning, an AI solution to accelerate insights at the edge, from diagnosing diseases to detecting credit card fraud, by sharing and unifying AI model learnings without compromising data privacy. HPE Swarm Learning is a privacy-preserving, decentralized machine learning framework for the edge or distributed sites.
With the right technology solutions, companies can aim to relieve rising levels of burnout among health care workers. More than two years into the pandemic, depleted health care workers have been pushed to their limits. In the U.S., we're experiencing what Becker's Hospital Review has described as "an unprecedented nursing shortage." Overworked and risking their own health -- both physical and mental -- to provide care throughout multiple surges of COVID-19, nurses are in crisis. Many are leaving the profession -- and the problem is global.
Oxford Languages defines AI as the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. For those of us working in the realm of digital marketing, the impact has become even more clear over the last few years. To put things into perspective, 61% of marketers say AI is the most important aspect of their data strategy, according to MemSQL. Have you ever searched for a particular product and then all advertisements you see after that search are for similar products? The power of DMPs (Data Management Platforms) allows AI to gather data from across the Internet – not just a particular website.
At GOAT Group, the Engineering team is an integral part of our dynamic company. By joining the team, your skills will be front and center, working alongside other passionate individuals to solve problems and build software. From launching compelling new consumer experiences, tackling global logistics challenges to scaling infrastructure to facilitate our rapid growth – technology is essential to driving our vision forward. The work you do will change the way the world shops, while also empowering entrepreneurs, including individual sellers, brands and boutiques. The Data Engineering team is responsible for building and maintaining data solutions that deliver value to our internal and external stakeholders.