What Are MDX Queries?

Multidimensional Expressions (MDX) help connect large data repositories to valuable business insights. As users learn how MDX works, they see it’s more than just a query language. It unlocks a world of data-driven possibilities.

How Does MDX Work and What Is It Used For?

MDX is a query language used to analyze and retrieve data from multidimensional databases. It is commonly used in business intelligence and data analytics. MDX helps define, manipulate, and query datasets across multiple axes.

SQL works with relational databases using rows and columns. MDX, on the other hand, works with multidimensional OLAP data models. It offers more flexibility for analyzing data across multiple dimensions. Also, it provides high efficiency and flexibility for complex analysis.

Let’s understand it with a real-world scenario. The maps app on a smartphone initially displays a zoomed-out picture of the Earth. Once the user enters a destination, the system shows the best routes. Voila! The person can now reach their destination even if they don’t know the locality. It acts like a navigation system for business data. Its syntax-rich queries guide users through complex, multidimensional data.

What Is an MDX Query and How Is It Structured?

It is a set of directions given by the user. It specifies the dimensions, measures and filters needed to retrieve data. Dimensions are the categories of data. Measures are the numerical values. Filters are used to narrow down the results. Time, Geography or Product Types can be seen as dimensions in enterprise data models. Measures like Sales Revenue or Units Sold are recorded as numeric values. Filters, such as sales in Q1 or in a specific region, help narrow down the data.

Here’s a sample query for better understanding:

SELECT
{ [Measures].[Total Sales], [Measures].[Average Profit] } ON COLUMNS,
{ [Time].[Time Hierarchy].[Year].&[2023] } ON ROWS
FROM
[SalesCube]
WHERE
([Geography].[Hierarchy].[Country].&[USA])

The above query retrieves the average profits on the total sales of a product for the year 2023. The year is the dimension of the data model and the USA geography is the filter. While these elements are specific to the dataset, the universal aspects of the syntax are as follows:

• Tuples: These are the parentheses separated by commas. They represent a single data point in the multidimensional space like this: (Dimension1.Member, Dimension2.Member).
• Sets: Sets are collections of tuples defined by curly brackets. They are used to define a group of data points. For example: {(Member1, Member2), (Member3, Member4)}
• SELECT Statement: SELECT specifies the dimensions and measures from where the data is retrieved. The basic structure includes establishing elements to be represented as COLUMNS and ROWS.
• FROM Clause: this element names the cube from where the data will be retrieved.
• WHERE Clause: This clause applies to a multidimensional context and defines the specific section of the cube to be accessed.

Why Should You Use MDX for Multidimensional Analysis?

MDX simplifies complex data analysis by enhancing OLAP operations. Here are the primary capabilities:

• Multi-Analysis Scenarios: OLAP data models are designed to handle large volumes of data across multiple dimensions. Users can run MDX queries to slice and dice data across dimensions like time, geography and product categories.
• Semi-Cumulative Calculations: MDX transcends simple sums or averages and supports semi-cumulative calculations. A business can design calculations to calculate specific figures for a particular category.
• Many-To-Many Relationships: A single fact can be associated with multiple dimensions to form a many-to-many relationship. MDX allows users to perform complex analytical tasks across various dimensions.
• Time-Window Analysis: MDX facilitates temporal analysis for users to effectively analyze data over different time periods. Users can quickly analyze sales data by month, quarter or year.
• Custom Measures and KPIs: MDX allows users to define and calculate specific metrics tailored to their business needs. The expressions and formulas can incorporate business logic to derive new measurements from existing data.
• Integration with Data Visualization Tools: Integrating MDX with data visualization tools can provide deeper insights into complex data. It enables the creation of charts and graphs based on the underlying data for pattern recognition and anomaly detection.

How Does Kyvos Help Run MDX Queries Without Writing Code?

Let’s continue with the example of a maps app. Imagine if users had to write code every time they wanted to go from A to B. Only software experts would be able to use it. That would make the app hard for most people. Instead, the app accepts commands in everyday language. It then converts them into coordinates automatically. Kyvos does something similar to MDX querying.

Generally, non-technical users have to rely on IT professionals to write a query. To tackle this, Kyvos supports natural language interactions for data exploration using MDX. Users can now access OLAP data models by asking questions in the natural language. and Kyvos will generate an MDX query for them.

The KPI designer assistant simplifies complex calculations. It uses semi-cumulative calculations to define essential business KPIs. Automated modeling helps businesses identify key metrics to track. These metrics are known as KPIs. It uses computer programs to analyze past data and trends. Based on this analysis, it suggests the most relevant KPIs.

The platform also offers integrity and confidentiality with a multi-tiered security architecture. Such intense focus on data governance allows the benefits of AI to be leveraged without compromising data security.