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Retail Media's Business Case for Data Clean Rooms Part 1: Your Data Assets and Permissions

It’s hard to have a conversation in adtech today without hearing the words, “retail media.” The retail media wave is in full force, piquing the interest of any company with a strong, first-party relationship with consumers. Companies are now understanding the value of their data and how that data can power a new, high-margin media business. The two-sided network that exists between retailers and their brands turns into a flywheel for growth.

Building and Evaluating GenAI Knowledge Management Systems using Ollama, Trulens and Cloudera

In modern enterprises, the exponential growth of data means organizational knowledge is distributed across multiple formats, ranging from structured data stores such as data warehouses to multi-format data stores like data lakes. Information is often redundant and analyzing data requires combining across multiple formats, including written documents, streamed data feeds, audio and video. This makes gathering information for decision making a challenge.

3 Ways to Monetize Your Application Data with Embedded Analytics

Data is one of the most valuable commodities an organization has. Every company stores and manages a substantial amount of information. But how do you gain revenue from it? Here, we discuss three ways you can monetize data with an embedded analytics investment.

Improve Product Stickiness and User Adoption with Embedded Analytics

You’ve heard of throwing ideas at a wall until something sticks–as a product manager, you may find you’re doing the same with application features. For application teams, creating sticky applications that customers can rely on and continue using for years to come is key to maximizing revenue. Elements like intuitive interfaces, personalized experiences, seamless integrations, and valuable core functionalities all contribute to this stickiness.

Databricks Mastery: Speed, productivity, and efficiency for Lakehouse

80% of data teams are facing challenges related to availability of tooling. Why? Modern data engineering is difficult and testing data engineering solutions is generally an ad-hoc, manual process. The good news – data teams that use DataOps practices and tools will be 10 times more productive. With this in mind, Unravel is hosting a live event to demonstrate how enhanced visibility and data-driven observability help you streamline your workflow, accelerate your data pipelines on the Databricks Data Intelligence Platform.

The Sliding Doors for the Essentials

As I have explored in this “Sliding Doors” blog series, identifying the right door to create value with data can prove quite challenging - and once that door is opened, the journey ahead can seem daunting. But what if there was a way to make that journey a bit easier? Maybe it’s time to get back to basics… Who needs to hear this?

Data Engineering for AI at Scale with Qlik and Databricks

For data engineers, the Generative AI (Gen AI) era is a transformative shift in how we approach data architecture and analytics. Professionals at the forefront of this shift will be gathering in San Francisco, at the Data+AI Summit June 10-13. Attendees will be exploring tools that integrate with Databricks Intelligent Data Platform that decrease data management costs and improve data's impact on business outcomes.

Discover Financial Services Automates Data Ingestion for Real-Time Decision-Making at Scale

Making operational decisions in a tight timeframe is critical to the success of an organization. Real-time data ingestion enables quicker data availability, in turn enabling timely decision-making. Real-time ingestion is foundational to our digital transformation at Discover Financial Services. As a senior manager leading the streaming and real-time data platforms at Discover, I don’t want to be in the data replication business manually.

The Rise of AI in FP&A: How insightsoftware Empowers Your Team

Despite the transformative potential of AI, many financial planning and analysis (FP&A) teams are hesitating, waiting for this emerging technology to mature before investing. According to a recent Gartner report, a staggering 61% of finance organizations haven’t yet adopted AI. Finance has always been considered risk averse, so it is perhaps unsurprising to see that AI adoption in finance significantly lags other departments.