Time spent managing data does not an exceptionally data-driven organization make. And data maturity models are key for helping decision-makers understand why.
Common data quality issues aren't to be ignored, especially since each compounds under the pressures of big data. But data teams can beat them to the punch.
Serving as the "back of the house" in the restaurant that is the data-driven organization, data quality management (DQM) is the secret to serving up success.
Despite all the upsides, the prospect of automated data governance can be daunting. For good reason. However, data contracts can make all the difference here.
Learn why data leaders are weighing the benefits of federated vs. centralized data governance, in addition to one way of ensuring either can excel in practice.
In theory, tracked data lineage is better than none at all. But in practice, automated data lineage processes are proving essential for modern organizations.
Data engineering best practices are bordering on essential. That said, it's important to understand how data contracts make these best practices even better.
AI's been revolutionizing enterprise-scale organizations for a while. But it took the advent of data supply chains to bring the party to mission-critical ERPs.
In modern organizations, data lineage is starting to involve a lot more than just what data’s been up to. And for good reason.
The story before the story—data provenance is a key aspect of data management. But what story does provenance tell? And how is that different from data lineage?
Frameworks function by acting as essential supporting structures. To ensure yours do, adopt a pragmatic approach to developing data management frameworks.
Data quality tools are essential for ensuring optimal quality during an org’s data lifecycle. This is why the right tool should always trump the “best” tool.
Quality control becomes crucial for products produced at scale. For data-driven organizations, this is why data testing is becoming a major priority. Learn why.
Understanding the difference between DataOps vs DevOps hinges on appreciating what they have in common—the needs for high quality data being key.
Fueled by a potent combination of current trends, investment in DataOps is projected to skyrocket. Learn why, and everything else most of us need to know.
Data quality rules are important. And, while nothing can make them unbreakable, the right data contract can certainly help them be iron-clad.
The data must flow. And data migration testing makes sure that flow doesn't compromise quality. So it's wise to set testing tools up for success.
There are many advantages to distributed data center architecture. But they don't matter much when said architecture can't scale with your business. Learn why.
Data pipelines make your strategy more efficient and avoid outdated information, and other vulnerabilities. Data contracts help automate and safeguard them.
Database schemas establish and maintain database form and function, which is why a schema deserves its own support structure to ensure it can evolve as needed.
Searching OLTP vs OLAP is a good way to learn about two complementary processing systems. But it's also a window into how professional POVs can be problematic.
Semantic data models are increasingly vital—helping ensure both humans and machines can understand big data. And data contracts can help them work better.
Data engineers are increasingly responsible for ensuring data collaboration translates to business success. And the right POV makes all the difference.
Centralizing data off-site to increase data quality, scalability, and operational efficiency, DaaS can be a huge advantage—if implemented correctly.
Data transfer objects benefit a wide variety of organizations. But it's important to understand their ubiquity comes with issues (plus how to mitigate them).
Data contracts hinge on the age-old idea that preventing data disasters is a lot easier (and cheaper) than fixing them when they happen. Learn why.
Discover why now, more than ever, data quality needs to take precedence over the sheer quantity of data we rely on.
What is data governance? Find out the definition, its framework and pillars, along with key roles and responsibilities. Create an effective governance solution.
Take your business’ decision-making to the next level by following the best practices for data producers. They can ensure your data is accurate and consistent.
Class is in session as we break down the fundamentals of data modeling, its different forms, and why it's often a source of contention in the data space.
The ins and outs of data consumption seem obvious. But business leaders need to appreciate that data consumers are as varied as they are voracious. Learn why.
Understanding data debt is key to keeping modern organizations out of the deep end. Learn what causes data debt, and what you can do to stay ahead of it.
While simple in theory, treating data as a product can produce transformative benefits. Learn why, and why some still struggle to treat their data as an asset.
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