Search should start with business data instead of metadata for your users 

The AirQuery approach to dealing with business metadata 

Businesses today must rely on data to make informed moves and stay ahead of the competition. As companies keep using more applications and systems for routine, mundane tasks, the amount of data stored for examination and generating insights increases. The alternative is almost unimaginable to make business decisions and enjoy growth if you're not on track with the different details influencing day-to-day operations. 

Generally, business data as a concept is extensive and includes everything from basic analytics details to in-depth examinations of corporate performance. 

Let's try and understand data before we delve into business metadata. Let us also touch upon structured data, one of the most often used data that companies use as proof to make decisions. 

Present almost everywhere. Think of reports, excel sheets, reports, dashboards, graphics, etc., that are structured data. Structured data typically requires three dimensions of metadata to be structured and be of any use in reports. The three dimensions are- what, where, and when. When it comes to business, these aspects look like this: 


  • What exactly does the data signify? 
  • What type of revenue is being generated, and from what products? 


  • What part of your enterprise is the data relevant to?
  • Can everybody use it? 
  • Or, is the data apt for one unit or department within a company?
  • Or, can certain groups across multiple companies in an industry segment use it?


  • When was this data gathered? 
  • Is it the latest? 
  • Or from the last month or may two years ago.

These aspects (what, when, and where) constitute metadata. Metadata is data about data. 

Nothing is complex about these aspects, and most businesses are already acquainted with them. Tables and charts in reports or dashboards commonly depict two of the three factors, and the chart itself often designates the third. 

For instance, you can have a graph displaying a business unit–' where,' the axes reflect revenue–' what' across a given period–'when.' Or the diagram illustrates a reporting period–' when,' and the axes exhibit profit–' what' per business unit–' where.' 

Dealing with metadata 

With their growing significance and incredible interactive visualizations, analytics can now display these extra metadata fields. But if we examine these tools closely, it becomes apparent that they are designed to answer very few specific questions. And the more in-depth one looks at a problem, the greater detail required for its solution.

For example, a hypothetical shoe company might have product IDs, manufacturing cost, size, and color data for each pair of shoes they sell. With these details, the business knows how many shoes to make and in what sizes. Manufacturers can infer the data about the seasonality of sales, which helps them to prepare and handle production runs (what to manufacture, by color/model/size). Examining their data, they know consumers' preferences for size and color.

The way the data is collected itself offers an additional level of granularity—in this case, where and when dimensions. We know from the period a specific date belongs to, which caters to when; meanwhile, looking at geographical areas gives us some insight into where.

How is business metadata different? 

Although metadata can provide essential details regarding where (geography), what (products or services), and when, what about the why? And who and how do aspects allow us to understand its actual value? Here's where business metadata comes into the picture. Business metadata provides context to the data constructed or used by business people (the who). This information exists all over the company—in policy and procedure manuals, employee records, etc.

Dealing with business metadata 

In conventional business intelligence applications, metadata is another kind of data. It often gets relegated to annotations in reports that may or may not explain trends and relationships between different data sources.

While these non-technical alternatives are uncomplicated for professionals to operate, they lack the integration and structure of data warehousing. Many companies have broken down the digitization process into several projects regarding their budgets, dependencies, and timeframes. The tasks are operationally run in a project management solution, while discussions are driven using various applications and software. Much data is scattered in emails, chats, and different on-premise and cloud applications. These challenges make reporting the real story a real challenge.

The AirQuery approach to dealing with business metadata 

AirQuery delivers interactive dashboards, data pipelines, and more BI tools and features and is created to model, capture, and demonstrate your business metadata and structured data. It can combine numerous and diverse management frameworks, like administration, risk, compliance, performance management, reporting, operations, tasks, and more. With AirQuery, you will have a complete account of a trend's rationale, not just an annotation. This helps your teams to come up with appropriate action plans. And by delivering everything within a holistic platform, employees can automate the flow of business metadata, handle it with business rules, and utilize it to make more effective decisions that produce more promising business developments.

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