2022

How to Scale Your Data Culture with Data Mesh

Two decades into the twenty-first century, the majority of enterprise organizations continue to struggle to derive true value from their ever-growing stores of data. Bottlenecks created by centralized data teams, monolithic architectures, and restrictive governance...

Why It’s Time to Invest in a Data Catalog

It’s 2022, and data is the lifeblood of business. Gone are the days when executives made decisions based on “business savvy” or “gut instinct.” The most successful modern enterprise businesses — including Netflix, Amazon, Spotify, and Uber — have created highly...

3 Enterprise Data Management Trends CDOs Can’t Stop Talking About

When it comes to building a winning data culture, everyone’s looking for the new process, technology, or methodology that is going to help them turn the corner. Meeting with customers, prospects, and analysts, we hear about a lot of them, but there are three...

5 Questions with Jans Aasman

We all strive to be data-driven. And yet we all instinctively know that we’re not very good at it. In fact, if you believe any number of recent surveys, it seems we may actually be getting worse at data. One reason for this is a misalignment on what success looks like. How do you define and measure it? What is the actual value of your data?

Last-Mile Governance is More than a Query Workbench

Enterprises adopt data catalogs for a variety of purposes. One of the most popular is data discovery. But in today’s governance-focused world, connecting data consumers with the assets and analysis needed to make informed business decisions requires more than a simple...

Is Your Data Catalog Extensible?

Modern data catalogs must be able to scale up as demand for data and knowledge grows within the enterprise. But not every catalog has that capacity – many can only scale out to multiple instances.  Data and analytics ecosystems are evolving at an amazing pace. New...

How to Write a Problem Statement for an Open Data Project

There are literally millions of datasets on the internet that are open and freely available to use. Whatever your focus, with so much data at your fingertips, it can be tempting to utilize these resources to improve your data processing skills, uncover new...

3 Ways To Prevent Data Engineer Burnout

Data engineers — The reluctant heroes of your data team Does Superman ever feel burned out? Does he feel he has to constantly meet unrealistic expectations? Does he resent the lack of work-life balance that comes with the title “hero”?  And if so, is he likely to hang...

Web3 – but Who’s Counting?

In any long-term technology trend, there comes a point, or points, if the trend goes on long enough, where there seems to be a sort of ‘sea change’ in how the world views the technology; a change in expectations in the general population. Sometimes we even give those...

Top 5 Trends for Data Management in 2022

2022 is here! If the last two years are any indicator, maybe we’ll end up calling this the “decade of data,” with next gen data observability, data catalog, data integration platforms, cloud data warehouses, and more making big news and bringing in big funds. We’ve...

Anatomy of an Agile Data Governance Sprint

So you’ve persuaded your data leaders and key stakeholders that the only way to achieve a data-driven culture is by adopting an Agile Data Governance methodology. Great! You’re on your way to becoming an organization where data producers and data consumers work...

Why Your Enterprise Data Catalog Needs to be Cloud Native

When it comes to data catalogs, not all solutions are created equal – and the tell is in how they define their cloud infrastructure. Many claim to be cloud-based or cloud-enabled, but that just means their applications were built traditionally (on-prem) and then...

5 Questions with Lars Albertsson

We all strive to be data-driven. And yet we all instinctively know that we’re not very good at it. In fact, if you believe any number of recent surveys, it seems we may actually be getting worse at data. One reason for this is a misalignment on what success looks like. How do you define and measure it? What is the actual value of your data?