The "Before" and "After" of dealing with disorganized metadata

[jrineakter]

Metadata is considered to be the backbone of an organization. So what happens when that backbone breaks, or becomes completely inaccessible?

That’s the state in which many organizations find themselves. They can’t quite get a handle on their metadata. Particularly with the widespread use of AI, the potential for vast stores of information has expanded. In this post, we’ll walk through the “Before” and “After” of taming the chaos of organizational data and metadata, no matter how high the volume or how disparate the sources.

More data turns out not to be the answer to all of our problems. Today there is more information than we’ve ever had at any point in human history. Frankly, we're drowning in data. But more isn’t better. The real problem is our ability to manage and use our data and metadata effectively.

The “Before”
No single department or team member or process is at fault when your metadata is a mess. There are several factors at play.

You may be suffering from decision paralysis. Simple questions turn into three hour debates about the accuracy of data. Teams spend more time debating than making decisions based on it. Sometimes inquiries fall off a cliff, and the original question-askers just give up and develop workarounds because they need to get their job done.

You likely bump up against departmental silos. Marketing might guard campaign data like it's nuclear codes. Sales sits on a goldmine of customer insights that never make their way to other departments. IT and legal maintain a chokehold on permissions.

You live in a version-control hell. Spreadsheets multiply and no one is certain which is the single source of truth. Your data exists, but without proper context, it's meaningless. Business canada whatsapp number data users hesitate to make decisions because they don't trust the data they're seeing.

All of these are symptoms of metadata dysfunction. Here are some of the main costs:

Team Cost
Data analysts Waste up to 50% of time on finding and cleaning data, leading to delayed insights, increased risk of errors, and analyst burnout.
Executives Make delayed or poor strategic decisions based on conflicting or unreliable data, resulting in lost competitive edge and difficulty assessing true business performance.
Technical teams Struggle with increased infrastructure costs, security vulnerabilities, and technical debt while spending excessive time on data-related support issues.
Marketing/Sales teams Run ineffective campaigns and miss opportunities due to poor quality customer data, wasting budget and potentially damaging customer relationships.
Business users Suffer productivity losses from endless data searches, revert to "gut-based" decisions due to lack of trust in data, and miss innovation opportunities due to limited data access
The “After”
Now that we've painted a rather grim picture, let's talk solutions. Taming the data beast isn't easy, but it's not impossible. Here's a roadmap to guide you:

Admit there’s an issue
Have an honest and blameless conversation about the state of your data and metadata. Encourage open dialogue across departments and hierarchies.

Conduct an audit
Before you can fix the problem, you need to understand its scope. Conduct a comprehensive data audit to answer questions like:

What data do we have?

Is our metadata understandable by both humans and machines?

How is our metadata processed and structured?

Where is it stored?

Who has access to it?

How is it being used (or not used)?

What's our data quality like?

This audit will serve as the foundation for your metadata management strategy.

Establish data governance
Data governance isn't just a buzzword – it's the framework that will bring order. Establish clear policies and procedures for metadata creation, storage, access, and usage. Assign data stewards to oversee these processes and ensure accountability.

Invest in the right tools
While technology alone won't solve your problems, the right tools can make a world of difference. Look for solutions that offer:

Data catalog: A centralized inventory of all your data assets, making it easy to find and understand what data you have.

Data discovery: Tools that help users explore and visualize data, uncovering insights faster.

Business glossary: Define common terms and concepts to ensure everyone speaks the same data language.

Lineage: Track the origin and transformation of data as it moves through your systems.

Intelligent search: Implement powerful search capabilities that help users find data and its context 10x faster.

Remember, the goal is to make data more accessible and usable, not to add another layer of complexity.

Implement a data-driven culture
Technology and processes are important, but culture is key. Encourage a data-driven mindset throughout your organization. This means:

Promoting data literacy at all levels

Rewarding data-driven decision making

Encouraging collaboration and data sharing

Leading by example – use data to inform your own decisions

Unlock self-service data
Empower business users to access and analyze data without constantly relying on IT. This not only reduces bottlenecks but also encourages innovation and faster decision-making. With the right tools and governance in place, you can.