Enabling customers and users to quickly find the value within a product is critical for many organizations and at the heart of being a product manager. The approach to driving user growth involves a growth mindset, combining qualitative and quantitative research methods, and driving impactful solutions.

There is an urgent need for banks to be nimble and adaptable in the thick of a multitude of industry challenges, ranging from the maze of regulatory compliance, sophisticated criminal activities, rising customer expectations and competition from traditional banks and new digital entrants. As banks find their bearings in this landscape, what appear to be insurmountable odds are in fact opportunities for growth and competitive differentiation.

In the first part of this series , I outlined the prerequisites for a modern Enterprise Data Platform to enable complex data product strategies that address the needs of multiple target segments and deliver strong profit margins as the data product portfolio expands in scope and complexity: With this article, I will dive into the specific capabilities of the Cloudera Data Platform (CDP) that has helped organizations to meet the aforementioned prerequisite capabilities and fulfill a successful data prod

Over the last year, perhaps unsurprisingly, increasing numbers of companies have made the jump to the cloud. It’s become a necessary move for so many businesses. But, as I discussed with Joe DosSantos on the latest episode of Data Brilliant – the rewards are abundant, but the journey is not always straight forward.

In the most recent season of BigQuery Spotlight, we discussed key concepts like the BigQuery Resource hierarchy, query processing, and the reservation model. This blog focuses on extending those concepts to operationalize workload management for various scenarios.

So far in this series, we’ve been focused on generic concepts and console-based workflows. However, when you’re working with huge amounts of data or surfacing information to lots of different stakeholders, leveraging BigQuery programmatically becomes essential. In today’s post, we’re going to take a tour of BigQuery’s API landscape - so you can better understand what each API does and what types of workflows you can automate with it.

If the COVID-19 pandemic has taught us anything, it is that speed and intelligence are of the essence when it comes to making business decisions. Organizations must find ways of keeping ahead of competitors and disruptions by continually leveraging data to make smart decisions. The problem? Data may be everywhere, but it’s not always available in a form that businesses can use to generate analytics in real time.

As enterprises seek to accelerate the process of getting insights from their data, they face numerous sources of friction. Data sprawl across silos, diverse formats, the explosion of data volumes, and the fact that data is spread across many data centers and clouds and processed by many disparate tools, all act to slow the progress.