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MDX Queries

Multidimensional Expressions (MDX) bridge the gap between vast data repositories and the meaningful insights businesses seek to extract from them. As one understands its mechanics and applications, it becomes evident that MDX is more than a query language. It is about unlocking a world of data-driven possibilities.

What is MDX?

MDX is a query language used to analyze and retrieve data from multidimensional databases. Commonly used in business intelligence and data analytics, MDX defines, manipulates and queries datasets spanning multiple axes. While SQL handles relational (two-dimensional) databases with rows and columns, MDX queries multidimensional OLAP data models with excellent efficiency and flexibility.

Let’s understand MDX with a real-world scenario. The maps app on a smartphone initially displays a zoomed-out picture of the Earth. Once the user enters their destination in the search bar, the satellite-based navigation system works its magic to show the most suitable routes. Voila! The person can now reach their destination even if they don’t know the locality. MDX is the navigation system for business data and the syntax-rich queries are the clues the user can provide for insightful pathways through multidimensional data landscapes.

What is MDX Query?

An MDX query is a set of directions given by the user to specify the dimensions, measures and filters needed for data retrieval. Dimensions are the categories of data, measures are the quantitative data points and filters are the criteria used to refine the results. Time, Geography or Product Types can be seen as dimensions in enterprise data models. Measures such as Sales Revenue or Units Sold are recorded in numeric values and filters like sales in Q1 or for a specific region can be applied to 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 MDX 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 MDX will retrieve the data. 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 Use MDX?

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 using MDX 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 using MDX.

Custom Measures and KPIs: MDX allows users to define and calculate specific metrics tailored to their business needs. MDX 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.

With Kyvos, Run MDX Queries Without Having to Write Them

Carrying on with the example of the maps app, imagine if users had to write a piece of code every time, they had to navigate from Point A to Point B. Wouldn’t it restrict the usage of the app to software experts? The maps app solves this by taking user commands in the common language and translating them to coordinates on the maps on its own. Kyvos does something similar to MDX querying.

Generally, non-technical users have to rely on IT professionals to write an MDX query so that they can generate insights. To tackle this, Kyvos supports GenAI-driven natural language interactions for data exploration using MDX. Business users can now access OLAP data models by writing their requirements in the natural language and Kyvos GenAI 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 figure out which important metrics (KPIs) they should track to measure success. It uses computer programs to analyze data and suggest the most relevant KPIs based on past information and trends.

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.

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