Data vs. Metadata: What’s the Difference?

Business in the digital age involves massive amounts of data. In our research, 90% of business leaders say that being able to access the data they need directly in the flow of work would help them to perform better.

The solution involves interpreting and organising data using metadata. Managing data with metadata is where artificial intelligence (AI) and digital labour like AI agents excel. As noted by Srinivas Tallapragada, Salesforce's president and chief engineering and customer officer, data is the raw building material for AI and metadata is the blueprint that transforms it into a city.

To better understand how you can use — and trust — AI outputs, it's essential to know the difference between data vs. metadata and how they work together to produce reliable, accurate results.

Data is raw, unprocessed facts or figures that a person or organisation has collected or recorded. At its most basic level, data is any unrefined content.

Data has both benefits and drawbacks when tapped by AI. On the benefits side, raw data is the foundation for trend analysis and strategic planning. For example, combining historical and current sales data can help companies pinpoint market trends and take action to adapt.

When it comes to drawbacks, enterprises often face three challenges:

1. Data overload: Too much data can overwhelm organisations. Attempting to input and analyse massive (and constantly growing) datasets can leave teams chasing answers that are always a step ahead. The result is a lot of time and effort spent on minimal returns.
2. Rising storage costs: While solutions such as cloud and co-located storage may be cheaper than on-site servers, the costs add up.
3. Security concerns: Increasing data volumes make it harder for businesses to monitor and secure information access.

For AI tools, data is fuel. Intelligent tools use data to find connections and improve responses over time.

Data can be either structured or unstructured, meaning it may be in an organised format (like numbers and names in a spreadsheet) or exist in any format (like emails, images or conversations). There are three main types of data: structured, unstructured and semi-structured.

• Structured data is often the most familiar type to organisations. It's contained in databases or spreadsheets that follow specific rules about file types, formats and labels. Structured data is the traditional source for analytics because it doesn't need much adjustment or transformation before it can be used.
• Unstructured data is also common across enterprises, but because it's freeform content, it's harder to classify and manage without the help of machine learning and AI. For example, Slack conversations among sales teams can offer valuable insights into pricing, sentiment and trends when AI in Slack is used to search, extract and analyse the data. While teammates type to each other using natural conversation, AI is able to pull insights from that unstructured data.
• Semi-structured data doesn't fit neatly into a fixed schema but is still organised in some way, such as through tags or labelled elements. Humans and machines can understand the data, but extracting and analysing it may take more effort. JSON or XML files are some examples. They don't use tables but use markers such as key-value pairs and opening and closing tags for identification.

Metadata is descriptive information that provides context about other data. In other words, it’s data about data that helps people and computers categorise, manage and search data sources efficiently, even when the data types vary.

Using metadata for AI improves the relevance and accuracy of answers by providing context about the data' source, structure and importance. It helps AI index information and filter it for relevancy. Just like with humans, AI performs better when it can quickly skim, sort and narrow down what data is useful.

Metadata has a few key types that aim to organise and manage information optimally for indexing and searching. Three of the most common categories include:

• Descriptive metadata: Descriptive metadata provides context, including titles, authors or dates created. This makes it easier for users to search for specific files or records.
• Structural metadata: Structural metadata describes how data components are connected. Think of it like a map — it shows the relationship between records to help users and AI tools find relevant data sources.
• Administrative metadata: Administrative metadata provides details about records or files such as access permissions, format and version history.

Data and metadata have fundamentally different purposes. Data is the core information itself while metadata just describes the information.

For example, a company may have a wealth of information about its sales, including product types, dates sold, total sales values and changes in pricing over time.

That's the data. It doesn't need metadata to exist or be useful — but creating metadata can give the data context, such as which sales rep entered the data, when it was last modified or whether the sale was tied to a specific campaign.

Here are five areas to help distinguish between data vs. metadata:

Data is content. Metadata is context.

Data is raw content. Metadata helps organise this information into recognisable structures, sets or groups that make it easier for teams and tools to analyse.

Data provides information. Metadata describes the information.

A spreadsheet of data can tell you what products were sold and how much they cost, but the metadata describes where items were sold, on what date and who sold them.

Data may be raw or processed. Metadata is always processed.

Data can be unrefined information that hasn't been sorted, labelled or interpreted. It can also be processed and organised into a dataset. However, metadata, by definition, is processed data because it provides structure or meaning to the information.

Data is the primary content. Metadata is usually in the background.

The purpose of data is to be seen, heard and understood. Whether it's on your ecommerce site or an internal dashboard, you want pricing and product information front and centre. Metadata, however, is often accessible only to users and systems that want to know how to interpret, index or manage the data.

Data generally "lives" in a database. Metadata can "live" in multiple places.

Data tends to need space to exist and is typically contained somewhere, like a data warehouse or data lake. Because it's often smaller in size, such as an index or summary of the data, metadata might be kept in a database registry, a separate file or repository or embedded directly within a data file.

Data makes AI results possible, metadata lets AI work more effectively and efficiently.

With metadata, AI can more accurately interpret the meaning, relevance and quality of data — leading to faster and more precise results. This is due to the nature of AI and digital labour.

Digital labour mimics aspects of human cognition to answer user questions, identify trends or predict outcomes. Using digital workers, it extends an organisation's capacity to complete tasks at speeds and scales that a human-only workforce typically can't match.

For example, imagine a financial services firm that uses customer-facing AI agents. Customers can log in to their account and ask the agent questions about their current investment performance and broader market trends.

If the AI agent only has access to raw data, it must search all databases for relevant matches and may be unable to connect some content to the current context. This happens because it is not properly labelled or lacks a label altogether. However, using metadata makes it easier for autonomous agents to find, compile and compare relevant data.

Metadata has four key advantages for AI tools:

Context enhances the accuracy and relevancy of AI outputs.

For example, get better sales forecasts: Companies might use AI automation to create sales forecasts. Data combined with rich metadata provides more context about sales volumes and their impact on business operations. Consider a car dealership that has raw data that shows an overall increase in sales over the past six months. Metadata can add context by analysing information beyond simple unit sales, such as financing details, accessory add-ons and warranties. This can help to provide a better picture of profit margins, opportunities and challenges broken down by rep. Managers can then recognise and reward top performers and provide additional training for salespeople who need it.

Metadata streamlines decision-making by providing additional information.

For example, generating customised commerce promotions: Customers want personalised commerce promotions and marketing materials. AI tools can search user purchase histories, social interactions and other data sources to generate tailored offers. Metadata makes it easier to connect these sources by associating them with specific customers.

Adding contextual metadata allows AI to better understand and respond to user queries.

For example, personalised customer service interactions: Traditional chatbots were the first iteration of user-facing AI tools. While they could answer basic questions, they would escalate even slightly complex queries to human reps. However, next-generation conversational AI agents can handle multi-part questions and learn from each interaction to deliver better service. Metadata can help these AI chatbots personalise customer service by adding context to unstructured data. It can include descriptors like sentiment analysis using natural language processing (NLP). This allows AI agents to go beyond the surface of a query and gather deeper meaning.

Metadata helps ensure that AI tools are using high-quality, accurate data.

For example, reduced errors in data entry and processing: Better data typically leads to better AI responses. The process of creating and applying metadata involves identifying and reducing duplicate and inaccurate data. This gives AI higher-quality inputs to work with, which helps humans make better-informed decisions.

Data and metadata are both valuable tools for information analysis and AI agents, but they do come with potential pitfalls. Here are some common challenges of managing both data and metadata.

Collection and entry errors: The increasing volume and speed of business data — including customer details, transactions and logistics — raises the risk of data collection and entry mistakes. This is especially true when companies rely on manual processes. Small errors can lead to significant issues later if they're not caught and fixed.

• Solution: Address errors by using automated data collection and review tools.

Duplication and redundancy: These are notable challenges any time you manage data. Duplication occurs when the same data is entered at different points by one or more users, often unknowingly. Redundancy happens when data is copied or transferred to a new application or database but isn't removed from the original source. Both situations can skew data analysis.

• Solution: Minimise duplication and redundancy by having clear data handling policies and having automation and AI tools use metadata to help detect repetition.

Storage: More data means more storage, which usually costs more money. This problem becomes worse if data isn't regularly reviewed, updated or deleted to make better use of storage.

• Solution: Reduce storage expenses by frequently assessing data storage costs and merging databases when feasible.

Visibility: When data is stored in multiple locations, both on- and off-premise, it can be difficult for companies to have a complete, updated view of information. Without a reliable overview, businesses might rely on old or inaccurate data sources or overlook relevant information.

• Solution: Improve visibility by using metadata tags and categories and using data management solutions such as AI CRM tools.

Data privacy and security: Because metadata provides context, it may unintentionally de-anonymise personal or financial data, which could lead to privacy and security issues.

• Solution: Enhance data privacy and security by drafting detailed data governance policies.

Integration with other systems: Differing metadata types can cause conflicts that make data sources inaccessible or difficult to interpret. This can reduce the effectiveness of sorting, indexing and searching tools.

• Solution: Enhance integration using an incremental approach centred on common metadata components.

Data and metadata both play vital roles in data management, analysis and digital organisation. They're also essential in the development, application and output of agentic AI solutions. Data is the fuel while metadata is the filter that ensures AI tools access the most relevant, timely and accurate data for analysis.

In practice, this means that data vs. metadata isn't a competition; it's a combination that helps organisations by making it easier for AI agents to analyse data and suggest actionable solutions.

The bottom line is that context is the hidden superpower of content. Consistent and comprehensive metadata strategies make it possible for any business to tap into this superpower and strengthen their AI efforts.