In my last two blogs (Get to Know Your Retail Customer: Accelerating Customer Insight and Relevance, and Improving your Customer-Centric Merchandising with Location-based in-Store Merchandising) we looked at the benefits to retail in building personalized interactions by accessing both structured and unstructured data from website clicks, email and SMS opens, in-store point sale systems and past purchased behaviors.
2021 is the year of the Data Cloud. Powered by the Snowflake platform, the Data Cloud will be the place where organizations across industries can converge to mobilize their data. Snowflake estimates that there are still hundreds of millions of data sets isolated in cloud data storage and on-premises data centers globally. The Data Cloud eliminates these silos, allowing you to seamlessly unify, analyze, and share your data to reach deeper insights and even open new revenue streams.
When a business application slows down, bad things happen. Your customer support gets slammed with service requests. Your boss calls an emergency meeting to talk to the product and developer teams. Everybody’s asking the same question: what happened? Diagnosing a slow application and finding the cause of the problem is something developers need to do quickly. Performance-related problems are in the top five SaaS user churn, which is a major preventable loss of revenue.
Artificial intelligence and machine learning are relentlessly revolutionizing marketplaces and ushering in radical, disruptive changes that threaten incumbent companies with obsolescence. To maintain a competitive edge and gain entry into new business segments, many companies are racing to build and deploy AI applications.
Once, dashboards and reports were limited to being embedded into our apps as standalone modules - essentially, as separately accessed product features. This limitation sometimes meant analytics is forgotten by users, and more often than not, underutilized in its potential. Today, contextual analytics makes it possible to embed analytics directly into the core workflow of your software.
One of the best ways to stay current in the fast-evolving field of artificial intelligence and machine learning is by following thought leaders, evangelists, and influencers in the industry. In this article, we’ve selected 10 of the most influential thought leaders (listed alphabetically) that are helping drive the field forward.
The field of data analytics is rapidly evolving alongside advances in technologies such as AI and machine learning. There are many valuable resources online that can help you stay up-to-date with the industry — from news sites, industry analysis, and the latest scientific research. We’re listing the top 10 websites and blogs (listed alphabetically) for anyone interested in keeping up with recent industry developments.
Cumulative Layout Shift or CLS, Web Vitals and Core Web Vitals, these terms seem complicated, but it’s actually not that hard to understand what they stand for.
Kong is a popular open-source API gateway to help manage your APIs. With Kong, you can handle authentication, rate limiting, data transformation, among other things from a centralized location even though you have multiple microservices. Kong is built on NGINX at it’s core, one of the most popular HTTP servers. Being open-source, Kong is very easy to deploy on-premises usually in just a few minutes without requiring the installation of many components other than a Postgres or Cassandra store.
GKE is a Google Cloud service that offers a managed Kubernetes cluster, the nodes of the clusters are running on Google Cloud VM instances, the control plane and network is fully managed by GKE. GKE offers a sandboxing feature (https://cloud.google.com/kubernetes-engine/docs/concepts/sandbox-pods ), based on gVisor (https://gvisor.dev/docs/ ) it protects the host kernel from untrusted code.
