Digitalization has significantly accelerated ever since the 2019 pandemic. Fortunately, Ecommerce is a flexible industry that is capable of constantly evolving with changing digital trends and global developments. Customers today use multiple devices like smartphones, laptops, and desktops, and employ all these channels to gather product information. Ecommerce businesses offer omnichannel shopping for these customers to seamlessly shop between all these devices, with multiple payment options.

Machine learning is more accessible than ever, with datasets available online and Jupyter notebooks providing an easy way to explore and train models. In building a model, we often forget that it will be incorporated into an application that will provide value to the user. Therefore, we wanted to demonstrate how we can "use" the models we build in an application.

As companies continuously adapt to shifting data regulations, many have struggled to find a balance between keeping up with lightning-speed regulatory obligations and cultivating organizational success. Uday Kamath, Chief Analytics Officer at Smarsh, expertly navigates the precarious field of data regulations. Serving as a leader of the company’s highly collaborative and productive data and analytics team, Uday offers over 20 years of expertise and experience.

33 experts share their leading dashboard design challenges as well as the dashboards they find are the most difficult to create.

Learn how to format your Google Sheets to ensure accurate data visualization in your shareable dashboards.

The dynamic and interconnected world of global ecommerce, crypto currencies, and alternative payments places increased pressure on anti-financial crime measures to keep pace and transform alongside these initiatives. Consumers worldwide are projected to use mobile devices to make more than 30.7 billion ecommerce transactions by 2026, a five-fold increase over the 6.1 billion predicted for 2022.

BigQuery is Google Cloud’s fully managed serverless data platform that supports querying using ANSI SQL. BigQuery also has a data lake storage engine that unifies SQL queries with other open source processing frameworks such as Apache Spark, Tensorflow, and Dask. BigQuery storage provides an API layer for OSS engines to process data. This API enables mixing and matching programming in languages like Python with structured SQL in the same data platform.

The sheer quantity and diversity of data sources make today’s landscape strikingly different — which requires a new set of tools.