What is Metadata Management? Insights into Frameworks, Governance & Tools
Good metadata management is the key to addressing the challenges and seizing opportunities associated with the rise of big data, sprawl of data fabrics, and complexities of data governance.
At its most basic level, metadata management organizes vast, growing data stores (both created and collected) and ensures every data asset is discoverable, measurable, and shareable across the enterprise.
There are countless additional, far-reaching benefits of metadata management. We’re going to focus on how metadata management enables end-to-end measurement and how it can meet consumers’ increasing demands for personalized and curated content.
Plus, how it achieves the sometimes insurmountable task of making reliable ROI insights and data-driven business decisions.
But first, we’ll provide an introduction to metadata management — what it is, why organizations need it, why you need a tool, and best practices for implementation.
You’ll also find videos featuring two data management experts — Chris Comstock and Nathan Woodman — to aid in understanding how the state of privacy, tracking, and data collection in 2022 requires a fresh approach to metadata management for reliable end-to-end measurement.
So let’s get started.
What Metadata is (with Examples)
First, let’s define metadata.
Metadata is the data that describes and gives context to an information asset. Like the “5 Ws and H” writing approach, metadata represents the who, what, where, when, why, and how about a digital asset. For example:
- Who created the asset?
- What is the business definition of the asset?
- Where is the asset stored?
- Why is it being stored?
- When was the asset created?
- How is the asset formatted?
Generally speaking, there are three main types of metadata. Each can be created manually or automatically.
Indicates how an information asset is organized for seamless navigation. Examples include a table of contents, sections, chapters, indexes, and pages. Structural metadata also documents the relationship between assets.
Relates to the technical source of an information asset. Examples include file type, creation date, usage rights, asset owner, archival rules, and information related to decoding and rendering files, and other technical metadata.
This information is documents everything that happens to your data e.g. change history and other events related to how it’s stored and used.
Information that enables users to discover and quickly find the digital assets they’re looking for. Examples include author, title, keywords or phrases, file type, and other business metadata.
These little bits of data about your data assets can easily overwhelm an organization — especially since it just continues to grow. But if it’s well structured and organized, metadata is incredibly valuable to the business.
That’s where metadata management comes in.
What is Metadata Management?
Metadata management establishes policies and procedures that enable organizations to derive maximum business value from their digital assets.
Good metadata management ensures digital assets are properly maintained and can be discovered, integrated, linked, shared, and analyzed across the organization.
Why is Metadata Management Challenging?
Enterprise metadata management can be challenging to implement for several reasons. It may require many solutions, including metadata repositories, data modeling, data integration, and a data governance framework, to name a few.
It involves distributed architectures such as a data fabric, big data, and cloud platforms.
It may entail a start-from-scratch approach to data management, including a shift in the enterprise’s data culture.
As Tracking & Measurement Changes, Metadata Management to the Rescue
Data-driven organizations make better decisions, acquire more customers, and keep their customers longer. As a result, they drive more revenue and profits than businesses that make decisions based on speculation.
But to be data-driven, an organization must implement good metadata management so business users can tap into the resources needed for analytics-based decisions. But, of course, that’s easier said than done.
The inability to quickly find digital assets is a core problem that prevents many organizations from gaining a competitive advantage. And when you can no longer rely on third-party data and its in-depth tracking data on your users, businesses are turning inward rapidly and developing their own first-party data strategies.
But if that means just stuffing new, directly collected data into the same old unorganized shelves, it’s going to be a losing battle to find the data teams need, let alone rely on its accuracy.
Many data architectures resemble old, overstuffed used bookstores. There’s no rhyme or reason, or method of organization. You could spend hours sifting through titles and never find what you’re looking for. While that may be a nice way to spend a Sunday afternoon, it doesn’t fly on the job.
Finding that specific digital asset you need is especially challenging within today’s distributed environments. It’s even more difficult if there’s no established consensus or business glossary to support the different functions and teams within the organization.
Context is Key to Discoverability
Context is essential, even for the most seemingly straightforward business terms. For example, consider how different teams may define the term “customer” or perceive its related data.
Their objective is to fill the pipeline and drive revenue for the organization. They may view the term customer as the company’s name rather than people-level data. They may view customers as users of the product they sell instead of one name within the company’s entire customer roster.
Sales is less concerned with where customer data is stored. They care about whether they can access their data through their dashboard to move prospects through the funnel and meet their sales quotas.
Information technology (IT)
They’re tasked with providing data intelligence, ensuring proper data storage, and establishing data governance for the organization. So the term customer will change according to the business function and their specific requirements. For example, it might represent new customers successfully onboarded for the professional services organization. Or it might represent customers that haven’t renewed their maintenance contract for customer service.
And while data access is important, IT is most concerned with the technical aspects of storing and managing metadata.
They’re focused on ensuring the business adheres to privacy rules and regulations. Their granular focus may mean they consider customer and the related data on a people level instead of company level. Compliance is concerned with who has access to customer data and where and how it is stored and managed.
It’s challenging to develop a single definition or perception for the term customer. However, good metadata management ensures each team member can access and leverage the data they need in a meaningful way that’s consistent throughout the organization.
The Game-Changing Benefits of Metadata Management
Similar to data democratization initiatives, metadata management lessens the burden on IT to define and manage access to enterprise information.
Instead, marketing, content, analytics, and ad ops teams can form their own understanding of their digital assets, how to use them, and the potential value they represent.
Here’s a sampling of how managing metadata benefits the organization as a whole.
With aligned metadata management processes and definitions, users across different functions can easily access the right data assets, so they can accomplish more.
Sales is perhaps the most obvious beneficiary of good metadata management as 84% of sales executives cite content search and utilization as the top productivity improvement area. With streamlined search and retrieval, reps can devote more time to activities that generate revenue.
Data scientists also benefit significantly from good metadata management. They’re able to spend less time — up to 80% by some estimates — cleaning data or fixing inconsistencies between data source formats and systems utilizing the information. Instead, they can spend the bulk of their time analyzing data and uncovering insights for decision-making.
Those are just two examples. Virtually every team member in the organization will be more productive, resulting in faster project delivery, speed to insight, and more performance gains.
Higher marketing ROI
If you’re spending time and resources creating marketing assets, you want them to be discoverable. But storing digital assets in a content management system doesn’t necessarily mean they’re easy to find and share.
Fortunately, businesses can bring structure and organization to their digital content with a content taxonomy or a classification framework for content. Taxonomies require a well-managed metadata repository to streamline the discoverability of marketing assets across the web and other digital channels.
Together they help you get the return you expect on your marketing investment.
Improved customer experience
To improve upon or measure customer experience, businesses used to rely on mailed customer satisfaction surveys or follow-up phone calls.
The introduction of digital marketing introduced easier ways to accomplish that. And for years, marketers were able to leverage third-party data to create personalized experiences for prospects and customers from the get-go.
Of course, brands don’t have that luxury now that data privacy regulations forced the deprecation of third-party cookies. They’ve had to return to collecting and relying on first-party data to support their marketing activities. Managing the associated customer metadata is now critical to creating the personalized experiences customers still expect.
Organizations have had to rethink data management with the advent of regulations like the Health Insurance and Portability Accountability Act (HIPAA), General Data Protection Regulation (GDPR), and The California Consumer Privacy Act (CCPA).
It’s motivated businesses to invest in data governance programs to oversee their enterprise data’s usability, availability, security, and integrity.
Metadata and metadata management support data governance by providing the basis for identifying, defining, and classifying data. And with data quality standards in place, metadata management ensures the proper regulatory controls are applied to corresponding data components.
OK, So What is a Metadata Management Tool?
Metadata management tools provide meaningful context and insight into the digital assets stored across the enterprise so that non-technical users can easily discover information.
With the return to first-party data collection, centralized data management is even more critical today. Metadata management tools have become an increasingly important component of organizations’ overall technology stack. Indeed, the metadata management tools market is exploding, projected to grow from $6.3 billion today to $15.1 billion by 2026.
Metadata management solutions are typically multifunctional, including some or all of the following features.
- Metadata collection and transformation: Automated data discovery and metadata and metatag generation for connected assets and conversion into user-friendly formats
- Metadata repository: An indexed reference library of data assets from different repositories (data warehouses, cloud applications, data lakes, and more); fuels your content taxonomy
- Business glossary: A list of data assets with proper business context, precise definitions, and relationships between assets from disparate sources
- Data catalog: A repository that ties the business definitions to the physical data assets and their location
- Data profiling: Understanding metadata structure, content, and its relationship to other data assets
- Data lineage: A step-by-step map of the data journey and any changes to support data reuse and auditing processes
- Impact analysis: Promotes data visibility to support assessment of how potential changes can impact existing business structures
- Tagging and content taxonomy and classification: Organizes data assets within your content management system (CMS) to streamline discoverability and compliance with regulations; one such solution is a content metadata management platform
6 Crucial Steps for Effective Metadata Management
Implementing processes that ensure metadata is accessible, searchable, and usable across the organization is a gigantic undertaking. Knowing where to begin is the first big hurdle.
Here’s a roadmap for getting started.
1. Get the executives onboard
Metadata management is an enterprise-wide initiative that requires executive support. Without it, you can’t procure the necessary resources to ensure success.
Most business leaders understand the importance of data management and its role in gaining a competitive advantage. However, the complexity and magnitude of implementing the metadata management component — not to mention the cultural changes it requires — often prevents organizations from getting started.
Collecting metrics related to pain points that proper data management can help address are key to obtaining buy-in. Here are issues to consider:
- Customer retention issues
- Employee attrition
- Decreased sales
- Failure of marketing campaigns
- Once you’ve obtained executive sponsorship, you can move forward.
2. Establish a metadata admin team
It’s essential to have a dedicated team to develop your metadata management process and overall strategy. Be sure to select team members who understand the enterprise data landscape and can connect the metadata management strategy with the organization’s broad data and business goals.
In addition, it’s essential to designate data stewards to oversee metadata governance and the rollout of processes and policies. They’ll also be responsible for evaluating and selecting a metadata management tool.
3. Create a metadata strategy
To set yourself up for success, you’ll need to develop a strategy that will transform the metadata that’s captured and stored into a valuable asset for the business.
- A metadata strategy should consider the following:
- The organization’s overall data goals
- The metadata required to achieve the goals
- How the metadata will be collected and how sensitive data will be handled
- Where the metadata is located
- What metadata management solution will be used
- How to address technical or infrastructure issues
- How the metadata will be stored and accessed
- Who will be responsible for metadata maintenance
What this metadata strategy leads to is actually your organization’s entire data democratization strategy.
4. Adopt data standards and schemas
Data standards establish a common understanding of data elements to ensure proper use and interpretation. Metadata schemas organize data assets so they’re easily accessible and usable by all roles in the organization.
Both are essential for unlocking the value within collected metadata. Click the image at right to learn more.
5. Deploy a metadata management tool
Although some businesses still use spreadsheets to manage metadata, it’s exponentially more effective to implement a dedicated metadata management tool (MDM tool).
More robust solutions provide easy access and storage of metadata, leverage machine learning and artificial intelligence for capture and categorization, and support metadata processes, policies, and data governance requirements.
6. Rollout and monitor active metadata management
Once best practices are in place and you’ve established some successful metadata management use cases, it’s time to roll out your strategy to the rest of the organization.
As the new strategy is adopted, it is important to continuously monitor the performance of your overall metadata strategy and make improvements, adjustments, and updates as necessary, aka active metadata management. This is the cutting-edge approach to metadata management – a system that is always on, allowing for manual and automated process to keep the metadata you are storing and using, up-to-date.
You Can’t Have End-to-End Measurement Without Metadata Management
Until recently, marketing operations focused on individual channels and third-party attribution tracking to measure campaign effectiveness. The introduction of internet privacy laws have limited marketers to the amount of data they’d been accustomed to capturing and using to inform their marketing strategy. It’s forced them to explore other ways to target users effectively.
End-to-end measurement offers a different, more holistic approach for determining overall marketing effectiveness. It’s based on the metadata you can collect yourself — first-party data. The process involves using metadata management software to parameterize digital assets in order to identify the characteristics and overall value of the creative.
For example, you might collect the following metadata for a video ad:
- Information about the target audience
- The best practices the ad adheres to
- The overall emotional response to the ad
This same process could be applied across marketing channels, paid and owned ads, and content to create metadata stores to leverage for measurement and optimization.
Here’s just one list of marketing metadata that can be collected for a single point of data.
Ready to dive deep into metadata management and end-to-end measurement with Chris and Nathan? Check out the videos below.
End-to-End Measurement: A Mission-Critical Initiative for Data-Driven Enterprises
Claravine’s Chief Product Officer Chris Comstock and ad-tech industry leader Nathan Woodman recently recorded a three-part LinkedIn Live series to discuss end-to-end measurement and metadata management.
Listen in as they discuss why data-driven enterprises embrace end-to-end metadata measurement and why it’s key to improving the customer experience.
Part 1: The loss of observation and rise of experimentation
The deprecation of third-party cookies upended traditional observational methods of tracking consumers. It left many companies flat-footed, without a Plan B to track and measure their marketing activities’ effectiveness. Chris and Nate discuss how experimentation factors into end-to-end measurement and optimization of marketing activities.
Part 2: Organizing data to enable end-to-end measurement
Ensuring you’re measuring metadata effectively from start to finish is one of the biggest challenges of metadata management. Chris and Nate discuss strategies for organizing your data for maximum impact and how brands and agencies can overcome the most significant challenges.
Part 3: Aggregate data management and optimization
Any brand trying to manage campaigns across multiple platforms understands how challenging it can be to aggregate all the data, let alone use it for optimization. Chris and Nate discuss digital marketing automation, translating aggregate data, and getting started with the process.
The Bottom Line: With End-to-End Measurement, First-Party Data Reigns Supreme (Again)
Without access to Google and Apple data sets, advertisers have had to rethink how they target buyers. That means shifting their focus to the information they can collect, organize, analyze, and act on themselves: first-party data.
Claravine commissioned an independent market research firm to learn how brands are responding to internet privacy changes and the obstacles they face.
- Here are a few key insights from a survey of 400 marketing and advertising leaders.
- Most advertisers don’t have the right technology to collect first-party data.
- Advertisers are devoting more budget to first-party data collection.
- Growing brands recognize the value of first-party data.
- Most advertisers don’t maximize first-party data.
It’s clear that those quickest to embrace and adjust to the new reality — by going back to data collection basics — will be best positioned to thrive in today’s digital marketplace.
Next Steps: Master & Deploy Metadata Management
To stay competitive, brands must embrace metadata management — not only to improve data access within the organization but to provide prospects and customers with the experience they expect.