The ways in which we store and manage data have grown exponentially over recent years – and continue to evolve into new paradigms. For much of IT history, though, enterprise data architecture has existed as monolithic, centralized “data lakes.” More recently, as the role of data evolves and changes, so too does where that data […]
Real-Time Data in Machine Learning: Challenges and Solutions
In today’s dynamic global marketplace, events outside of our control are constantly making the data we’ve collected erroneous and outdated. There are instances in which real-time decision-making isn’t particularly critical (such as demand forecasting, customer segmentation, and multi-touch attribution). In those cases, relying on batch data might be preferable. However, when you need real-time automated […]
Doing Cloud Migration Right
More than 15 years ago, Amazon launched Amazon Web Services. Just two years later, more than 100 applications had been built on top of the platform. Fast-forward to today, and you know where this story winds up – nearly every enterprise company in the world deploys applications in the cloud in some form or fashion. […]
Data Preparation and Raw Data in Machine Learning: Why They Matter
In our current digital age, data is being produced at an unprecedented rate. With the increasing reliance on technology in our personal and professional lives, the volume of data generated daily is expected to grow. This rapid increase in data has created a need for ways to make sense of it all. Machine learning is […]
The Importance of Managing Your Metadata
Businesses that realize the value of their data and make the effort to utilize it to its greatest potential are quickly outcompeting those that do not. But like any complex system, the architectures that utilize big data must be carefully managed and supported to produce optimal outcomes. One of the chief obstacles in this continual […]
How to Implement a Data Quality Framework
According to IDC, 30-50% of businesses experience gaps between their data expectations and reality. They have the data they need, but due to the presence of intolerable defects, they cannot use it as needed. These defects – also called Data Quality issues – must be fetched and fixed so that data can be used for successful business […]
Data Lakes Are Dead: Evolving Your Company’s Data Architecture
You know you’re not getting the most out of your data. How can your company redesign its data architecture without making the same mistakes all over again? The data we produce and manage is growing in scale and demands careful consideration of the proper data framework for the job. There’s no one-size-fits-all data architecture, and […]
A Root-Cause Framework for Trans-Atlantic Data Privacy
When the United States and the European Commission together announced a new Trans-Atlantic Data Privacy Framework earlier this year, the news didn’t raise too many eyebrows. After all, there’s nothing particularly objectionable about the new framework – the goal is to “foster trans-Atlantic data flows” and address concerns about the underlying EU-U.S. Privacy Shield framework, and who […]
Data Trustability: The Bridge Between Data Quality and Data Observability
If data is the new oil, then high-quality data is the new black gold. Just like with oil, if you don’t have good data quality, you will not get very far. You might not even make it out of the starting gate. So, what can you do to ensure your data is up to par and […]
Dear Laura: Should I Leave My Data Governance Job?
Welcome to the Dear Laura blog series! As I’ve been working to challenge the status quo on Data Governance – I get a lot of questions about how it will “really” work. I’ll be sharing these questions and answers via this DATAVERSITY® series. In 2019, I wrote the book “Disrupting Data Governance” because I firmly believe […]