Why is metadata important?

Metadata explains what data means, where it comes from, and how to use it. It simplifies finding, organizing, and managing data, boosting trust, compliance, and decision-making. Like a roadmap, metadata gives teams clarity and confidence to work efficiently.

Why is data lineage important?

Data lineage is important because it provides visibility into the origin, movement, and transformation of data. It enables regulatory compliance, faster root-cause analysis, improved data quality, and trust in analytics. By mapping data flows, organizations enhance transparency, streamline audits, and support accurate, AI-driven decisions, making it a cornerstone of effective data governance.

Why is AI governance important?

AI governance is crucial because it ensures that artificial intelligence systems are developed and deployed responsibly. Without proper governance, AI can perpetuate biases, compromise data privacy, and make opaque decisions that affect individuals and society. Effective AI governance establishes frameworks and policies that promote ethical use, transparency, and accountability in AI applications.

Why is data governance important?

Data governance brings clarity and consistency, ensuring everyone uses and understands data the same way. It’s not just about control—it fosters collaboration, trust, and smarter decisions, turning data into a strategic asset that fuels innovation and growth.

Why is value governance important for organizations?

Value governance is crucial because it ensures that data and AI investments are not just technically sound but also strategically relevant. By focusing on business-driven prioritization, transparent value metrics, cross-functional ownership, agile delivery, and continuous improvement, organizations can maximize the ROI of their data initiatives.

How to extract value from data with proper data management

8 September 2022 | 3 mins read | Business Intelligence
by Jessica, Tech writer

How to manage the abundant influx of data
Data management to enhance the value of data
Make data management sustainable
FAQ

Summarize this article with AI:

ChatGPT Perplexity

Three elements characterize big data: Volume, velocity, and variety. Of the three, volume is becoming a greater concern for companies – The amount of data collected is only growing!

IT experts constantly have to adopt new terminology to describe the massiveness of data. It’s no longer surprising to hear about petabytes, exabytes, or even zettabytes!

How to manage the abundant influx of data

This influx of data is flooding businesses. All these information resources are valuable for business, but how do we collect, organize, and store them? Data management is the solution to managing a colossal volume of data.

The real challenge of data management is to transform the raw material into useful information for the company – bearing in mind the “garbage in, garbage out” principle: Raw data is worthless and will only produce waste if it is not properly cleaned up before being used.

Data management to enhance the value of data

Data management does not stop at managing the volume of data entering the databases. It is also useful for managing the other two V’s: Variety and velocity.

Variety: with the multiplication of sources and formats, learning to know, prioritize, and control data flow has become necessary.
Velocity: to fully understand the challenges of velocity, you need to understand the rate of change of the data, i.e. the frequency of creation of new data, the means to store it, the time to process it, the target audience…The three dimensions of data management:
- Technical management
- Controlling the cost of data
- Pedagogy and team support

Be careful not to be satisfied with a posteriori management of incoming data or focus on only one department at a time. It is essential to federate all the technical and business players around a collaborative approach.

Why? If all the players contribute to the purification and enrichment of data knowledge, they can take full advantage of it. This is the principle of data management: Transforming data and making it usable by all through a joint effort.

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To do this, you can use methods that have already proven their worth: collaborative data mapping, interactivity, and incrementality, as well as the use case approach.

Make data management sustainable

Beyond data knowledge, you have another challenge (and not the least important one) to take up: Making your company’s data management sustainable. You must integrate it into everyone’s daily practices!

Setting up data maps to make data management issues permanent is not enough: Business units must be regularly connected to a data catalog and update the information as soon as necessary.

All this requires good communication and successful change management: Make sure that the businesses understand the approach’s benefits and take ownership of it!

The challenge of data management is to control the high volume of data coming in, but also to study the data to understand it well and to make better use of it. It’s about setting up a system to manage the influx of data as well as possible, transforming it to make it accessible to everyone, and changing the very culture of the company.

FAQ

Why is metadata important?: Metadata explains what data means, where it comes from, and how to use it. It simplifies finding, organizing, and managing data, boosting trust, compliance, and decision-making. Like a roadmap, metadata gives teams clarity and confidence to work efficiently.
Why is data lineage important?: Data lineage is important because it provides visibility into the origin, movement, and transformation of data. It enables regulatory compliance, faster root-cause analysis, improved data quality, and trust in analytics. By mapping data flows, organizations enhance transparency, streamline audits, and support accurate, AI-driven decisions, making it a cornerstone of effective data governance.
Why is AI governance important?: AI governance is crucial because it ensures that artificial intelligence systems are developed and deployed responsibly. Without proper governance, AI can perpetuate biases, compromise data privacy, and make opaque decisions that affect individuals and society. Effective AI governance establishes frameworks and policies that promote ethical use, transparency, and accountability in AI applications.
Why is data governance important?: Data governance brings clarity and consistency, ensuring everyone uses and understands data the same way. It’s not just about control—it fosters collaboration, trust, and smarter decisions, turning data into a strategic asset that fuels innovation and growth.
Why is value governance important for organizations?: Value governance is crucial because it ensures that data and AI investments are not just technically sound but also strategically relevant. By focusing on business-driven prioritization, transparent value metrics, cross-functional ownership, agile delivery, and continuous improvement, organizations can maximize the ROI of their data initiatives.

About the author

Jessica Sandifer

With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.