Analytics

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.

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.

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.

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.

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.

During data analysis in SQL, one will often need to look at multiple tables and join them to get the desired results. Joining tables for analyzing data is a required skill for a data scientist or data engineer. Selecting the right join will optimize query performance.

It’s no secret that businesses are undergoing a dramatic digital acceleration during the pandemic. A recent Gartner CFO Survey characterizes this acceleration as “from the pace of a multi-year marathon to a 12-month sprint,” while a McKinsey survey estimates that organizations have shortened the digitization of their customer and supply-chain interactions by three to four years. Lagging behind in turning data into answers is no longer an option.

The main differences between Alooma, MuleSoft, and Xplenty: Data-driven organizations pull data from multiple locations such as in-house databases, SaaS, and cloud-based apps, making it difficult to determine accurate business insights. Moving all this information into a single location makes data analytics easier. This is where Extract, Transform, and Load (ETL) comes in.