With contributions by Anwar Haq and George Alifragis Ransomware has grown to become a significant threat to organizations today, no matter the size or industry. Cybercriminals are exploiting vulnerabilities in small businesses and enterprises alike, creating short-term and long-term damage that can impact everything from your employees’ productivity to your relationship with customers.
With the shift to distributed workforces and digital business models, cloud infrastructure and tools have become indispensable to the modern enterprise. But this growing reliance on the cloud also comes with a corresponding increase in security risks and breaches. The question is: When it comes to protecting public, hybrid, or multi-cloud environments, who should take ownership, the organization in question or the cloud service provider (CSP)?
It’s been over a year since enterprises around the world had to pivot and transition to work-from-home setups. While some employees are slowly trickling back into the office, majority of organizations have people working both onsite and offsite. This modern workforce has brought out an increasing reliance on cloud infrastructure, an essential tool for collaboration and business continuity. Technology like this isn’t without its risks though.
Machine learning models are often used for decision support—what products to recommend next, when an equipment is due for maintenance, and even predict whether a patient is at risk. The question is, do organizations know how these models arrive at their predictions and outcomes? As the application of ML becomes more widespread, there are instances where an answer to this question becomes essential. This is called model explainability.
As organizations refocus and restrategize this year, machine learning projects seem to be on the top of IT priority lists. Innovation is more important than ever, and this has led to higher spending, increased hiring budgets, and a wider range of ML use cases. Despite this, organizations are facing challenges in actually deploying machine learning models at scale. A lot of models are never operationalized, or if they are, the process to production takes too long.
The new normal brought digital commerce to the forefront, with customers preferring remote sales, online ordering and payments, and contactless purchases. The question is, are organizations equipped to cater to these constantly evolving buyer habits? Let’s see how the right solution and strategic application development can help fuel growth in the digital economy.
Unless you've been living on Mars for the past few years, I'm sure you’ve heard the buzzword “microservices”, also known as microservices architecture. A distinctive development approach, this natural evolution in software engineering came about due to the ever-increasing complexities of enterprise applications. Traditional applications are usually monolithic in design, which makes them bulky and very difficult to adapt to the changing needs of the business.
In 2020, companies across countries and industries scrambled to stabilize operations, ensure the health and safety of employees, and find ways to continuously deliver. But in the wake of all this uncertainty, one thing remains crystal clear: customer engagement is more crucial than ever. After all, with uncertainty comes doubt. Customers may be on edge, and they need reassurance that organizations will continue to meet—and exceed—their expectations.
Service Level Agreements (SLAs) are commitments given to customers in relation to the product or service being provided. If breached, not only are organizations expected to compensate through penalties and credit fees, but they can also face a significant dip in brand reputation and loss of customer trust. This is why preventing SLA breaches is a top priority for any customer-facing organization. To stay on top of breaches, agents traditionally check the ticket status of each incident manually.
