Four Reasons your Metadata is Broken

Overview | What you'll learn: 

Metadata is more important now than ever. New technologies have enabled businesspeople who have traditionally not been analysts to work with data. The consumerization of IT means people expect systems to be intuitive and require little training. With so many people using data to support so many kinds of decisions, it’s critical that your data is described, defined and understood.

But too many systems still require a slow, rigid approach to metadata. This approach decreases the flexibility of a business intelligence solution and ultimately reduces the benefit you can get from deploying self-service analytics. By changing the way you think about metadata, you can make it faster and easier for the business to make sense of their data.

Read this paper to find answers to four common reasons your metadata is broken.

We've also pulled out the first several pages of the whitepaper for you to read. Download the PDF on the right to read the rest.

Four Reasons your Metadata is Broken

1. Pre-defining metadata takes too much time and slows down a deployment.

In traditional business intelligence systems, organizations must model their entire enterprise as a first step. This is a time-consuming and complex process that pushes out an enterprise deployment by weeks or months. The start-up costs are high and the benefits of analytics are delayed.

A better approach is to look for a solution that can support analysis immediately. This not only has the benefit of delivering useful analytics more quickly, but also means that your metadata model can be built iteratively as you learn more about how people use the data. This more agile approach typically leads to a more robust and realistic metadata model.

A good way to get started is to leverage metadata from existing systems wherever available. For example, why take the time to define all your date fields as dates in your analytics solution when the database already has them defined that way?

2. Metadata isn’t as flexible as you need it to be.

Traditional metadata models are difficult and expensive to change. As a result, they don’t change often. This means they slowly fall behind in their ability to accurately represent the business data. IT may be responsible for keeping metadata up to date, but they may not have the information they need to respond to change.

In a world of fast-changing trends and opportunities, this is a severe competitive disadvantage for a business

New definitions and calculations are necessary on a fairly regular basis. Business users who are asking and answering questions with data are often the best source of new metadata. They may create a hierarchy of category → product, or group territories into a region. A flexible analytics solution will allow IT a way to evaluate and then promote new metadata objects to production so they can be shared by all users.

If you lack flexibility in your metadata, you lack the ability to evolve your understanding of your business.

3. Metadata isn’t discoverable in the flow of analysis.

Another issue facing IT managers who want to enable end users is that metadata may not be discoverable. If your field names look like this:


and your business users have to hunt for the meaning of a field by searching in an intranet or referencing some document, you might not be getting the benefits of self service analytics. Users are blocked by their inability to understand what these fields mean—they need metadata. And when metadata is hard to find or difficult to access in the flow of analysis, users may give up. Then you’re not using data to improve your business and your end users are frustrated.

Look for a system where you can expose metadata to users easily, through readable field names and field descriptions. Make sure users can get that information when they need it.

Want to see more? Download the whitepaper!

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