More data is available than ever, challenging organizations to change how they interact with their data so they can get the most out of it. Ahmed Munir, Lead SAP Functional Technology Architect Manager at Whirlpool Corporation, has 16 years of SAP leadership experience. He joins us to share what he has learned about building great data teams, upcoming trends in data analytics to keep an eye on, and how data teams will evolve over the next 5-10 years.

In today’s competitive environment, organizations need to quickly and easily make decisions based on real-time data. That’s why we’re announcing Datastream for BigQuery, now available in preview, featuring seamless replication from operational database sources such as AlloyDB for PostgreSQL, PostgreSQL, MySQL, and Oracle, directly into BigQuery, Google Cloud’s serverless data warehouse.

We previously wrote how the data mesh architecture rose as an answer to the problems of the monolithic centralized data model. To recap, in the centralized data models, ETL or ELT data pipelines collect data from various enterprise data sources and ingest it into a single central data lake or data warehouse. Data consumers and business intelligence tools access the data from the central storage to drive insights and inform decision-making.

As organizations invest ever more heavily in modernizing their data stacks, data teams—the people who actually deliver the value of data to the business—are finding it increasingly difficult to manage the performance, cost, and quality of these complex systems. Data teams today find themselves in much the same boat as software teams were 10+ years ago. Software teams have dug themselves out the hole with DevOps best practices and tools—chief among them full-stack observability.

What’s harder than finding the right data architecture? Finding the right dedicated partner. Adverity gets both with Snowflake. Learn how the two organizations are moving into new markets and supplying even more reliable marketing data to Adverity customers. When a fast-growing SaaS business looks to expand its client base, it normally encounters two major challenges: In many cases, an external data solution provider can only help solve the scalability challenge.

Some time ago, we covered the key metrics that a Product Manager in a fintech organization should make a top priority when determining their KPIs, breaking them down into five groups: Session-based data, Customer Feedback, Technical Metrics, Action Stats, and Revenue. With that in mind, we conducted a series of surveys on LinkedIn, asking PMs in the fintech industry which of those groups were the most important for them while running digital product analytics.

July brings summer vacations, holiday gatherings, and for the first time in two years, the return of the Massachusetts Institute of Technology (MIT) Chief Data Officer symposium as an in-person event. The gathering in 2022 marked the sixteenth year for top data and analytics professionals to come to the MIT campus to explore current and future trends. A key area of focus for the symposium this year was the design and deployment of modern data platforms.

Successful teams blend the art of sales with a data-driven pipeline strategy.