Organizations using Amazon Web Services (AWS) cloud traditionally leveraged Reserved Instances (RI) to realize cost savings by committing to the use of a specific instance type and operating system within the AWS region. Nearly 2 years ago, AWS rolled out a new program called Savings Plans, which give companies a new way to reduce costs by making an advanced commitment of a one-year or three-year fixed term.

The cloud is today one of the most expensive resources for any modern organization, second only to employee salaries and overhead. According to recent research by Gartner, end-user spending on public cloud services will reach $396 billion in 2021 and grow 21.7% to reach $482 billion in 2022. By 2026, Gartner predicts public cloud spending will exceed 45% of all enterprise IT spending, up from less than 17% in 2021.

Digital trade and eCommerce companies are generating transactions in more significant quantities than ever before. In 2020, eCommerce sales made up 19% of all worldwide retail transactions, representing $26.7 trillion in revenue. The cornerstone of any eCommerce company is providing a seamless, reliable experience where customers can log into a clean interface, browse products, and make purchases quickly and on-demand. Increased digitization after the pandemic has only heightened the stakes.

We couldn’t be more excited here at Anodot at the announcement of the acquisition of Pileus. Acquiring a company is a very special event, a moment that is the culmination of months of thought and deliberation. Is there a strong synergy between the two entities? Do we share the same DNA and culture? Is the additional product aligned with our long-term vision?

Anodot recently took part in the 2021 Data Agility Day, an event dedicated to examining how organizations are extracting value from data. CEO and Co-Founder David Drai was joined by David Ashirov, VP of Data at Freshly, where he has worked to build a data stack that departments across the company could leverage to drive business. Ashirov is a senior executive with two decades of experience in data engineering, business intelligence, and marketing.

It’s the second holiday season since the pandemic broke out but, with many brick-and-mortar stores reopened, will it be more like 2020 or 2019? Will shoppers stick to online behemoths like Amazon or will they shop in-store? And how are retailers planning to offer a competitive experience amid ongoing supply chain issues? We partnered with Researchscape to survey thousands of eCommerce companies and consumers in the U.S. about their plans for this holiday season.

On October 4, Facebook and its family of apps, including Instagram and WhatsApp, suffered a global outage of its services that lasted approximately six hours. The massive outage has been blamed on configuration errors in backbone routers that are used to connect network traffic to the company’s data center. Facebook apologized to its 3.5 billion users who were unable to access any of the company’s services during the downtime.

In the first four parts of our series on correlation analysis, we discussed the importance of this capability in root cause analysis in a number of business use cases, and then specifically in the context of promotional marketing, telco and algorithmic trading. In this blog we walk through how to leverage correlation analysis to address the challenges in ensuring a seamless online payment experience by the end-user.

For modern businesses faced with increasing volumes and complexity of data, it’s no longer efficient or feasible to rely on analyzing data in BI dashboards. Traditional dashboards are great at providing business leaders with insights into what’s happened in the past, but what if they need actionable information in real time? What if they want to use their data to estimate what may happen in the future? Companies are taking notice.

When organizations track metrics by the thousands, millions, or even billions, it’s helpful in many ways to understand which metrics have close relationships, meaning when one metric behaves in a certain way, one or more additional metrics can be expected to behave in a similar or opposite way.