Experts from all industries admit the need to use and analyze data, especially generated in real-time. Therefore, business decision-makers need to know how these processes occur, under whose control they are and how to optimize them. But what technologies can help companies better cope with masses of data?

New research by Inmarsat, the world leader in global mobile satellite communications, reveals that investment in the Internet of Things (IoT) is set to overtake cloud computing, next generation security, big data analytics and other digital transformation technologies in the near future. Respondents drawn from multiple industries reported plans to invest the greatest proportion of their IT budget on IoT projects over the next three years.

The digital transformation that many companies underwent in 2020 continued in 2021 at a rapid pace as the pandemic continued to impact the world. While many of my digital Top transformation trends predictions from a year ago were proven correct, others didn’t quite come to fruition and were displaced for more urgent and strategic needs as the pandemic persisted. What does this mean for 2022?

Integration is now the #1 IT expense category at many enterprises and new complexities increase the burden on Service Delivery, CI/CD, IBM MQ administration, and other “integration professionals” every day. Your enterprise has Microservices, Mobile, Mainframes, Cloud, and more applications and application updates than you can count and it takes the routing of transactions, messages and more through a rapidly growing integration infrastructure layer to make it all work together.

Financial institutions and their service providers have traditionally owned and run their technology infrastructure on their own premises or in their own data centres. However, that’s changing with the advent of powerful public cloud services, such as Amazon Web Services, Google Cloud and Microsoft Azure.

With the rise and advancement of technology and artificial intelligence, there is no question that new rationalists are joining this group to enforce their ideas. However, most Rationalists and highly successful people do not hold back anything, and maybe this is because the technological and financial industry embraces disruptors of the norm. One rationalist concept adopted by the technology and financial sector is Fintech.

For decades, the development and operations teams within companies were siloed. Developers created the software. Operations tested and deployed it. But in 2009, IT consultant Patrick Debois coined the term “DevOps,” a merging of development and operations to improve communications, establish best practices and create feedback loops for organizations to keep improving the overall process.

One of the questions I get asked a lot by customers, prospects, and partners is, “Will AIOps make them irrelevant?” To them, AIOps is often equivalent to automated remediation; an AIOps system automatically detects an incident and kicks off a remediation process in response to this incident, knowing exactly what process will solve the problem. IT is out of the loop, data centers and NOCs just keep humming along unattended, end users are none the wiser.

How do you determine whether one multifamily deal is better than another? Let me start by saying that a deal is only as good as the assumptions you’re making as an investor, and assumptions are not guaranteed to materialize. As a real estate investor, operator and syndicator through my company Blue Lake Capital, a significant part of our analysis when considering properties to invest in is looking at how the property performed in the past.

Machine learning has become an important component of many applications we use today. And adding machine learning capabilities to applications is becoming increasingly easy. Many ML libraries and online services don’t even require a thorough knowledge of machine learning. However, even easy-to-use machine learning systems come with their own challenges. Among them is the threat of adversarial attacks, which has become one of the important concerns of ML applications.