Astrato’s New Analytics Engine Enables a Self-Service, User-Friendly Experience

Have you ever been on the phone with an automated customer service line and found yourself shouting commands over and over with no success? You say “my recent order wasn’t delivered,” and the system replies “you can’t log in to your account, is this correct?” You reply “No,” and the system says “Let’s try again.” This cycle continues until you’re able to speak with a human being who can help you find your missing order. The automated system is meant to be more efficient than a person (it’s a computer, after all!) but at the end of the day, when you’re able to plead your case to a representative yourself, the process works much, much better.

A similar(ish) problem happens when a BI tool attempts to programmatically answer a unique or complex business question. Historically, the solution to this problem is to employ a highly skilled developer to work their magic and bridge the gap between business question and answer. Until now, it wasn’t possible for business users to even attempt this process on their own, and so critical insights remained siloed and difficult to access or analyze.

But now, with the launch of Astrato’s major update to its Self-Service Analytics Engine, anyone – from data experts to business users – can deftly navigate to the answers they need without sacrificing their time, resources, or sanity. It’s the equivalent of a more organic interaction with a system designed to help you find answers, like in the example described above. By enabling users to engage with efficient, self-service analytics, this engine drives a more powerful, more engaging experience that delivers results faster, and more reliably.

The Self-Service Analytics Engine is based on the Unified Star Schema (USS), and helps teams get the most out of their data, with user-friendly live query and self-service capabilities. This ground-breaking solution helps businesses to use data more confidently, pulling trustworthy, accurate conclusions natively, with a simple drag and drop.

Astrato’s Self-Service Engine Delivers a Seamless User Experience 

Astrato’s Self-Service Analytics Engine is underpinned by a new methodology that introduces a different way of organizing and blending your data. With this new approach as a foundation, Astrato delivers a self-service experience that is much easier to use than other BI tools, such as Tableau, Looker, and ThoughtSpot.

Typically, organizations store their data across multiple platforms, often in different formats, and for various uses across different departments. This can easily lead to data silos or inconsistencies. For this reason, many teams are now choosing to centralize their data in a Data Cloud, like Snowflake, where data is more efficiently stored, and more easily accessible by everyone.

When consolidating data into one place, it typically needs refining or transforming for analytics use. This process is performed in-house using a range of data products, or with support from consultancies. The output is schemas – a cleaner presentation layer that roughly aligns to business domains and can be easily combined. For example, inventory data can be combined with sales data, with conformed dimensions – like products – common between them. This ensures consistent reporting across the enterprise and results in much more efficient data exploration and analysis.

An example of a business-wide schema is shown below.

The result of this process is analytics-ready data in the form of tables. For larger businesses, there may be dozens or even hundreds of tables at the end result. The challenge for many BI tools within this structure becomes 1) how to effectively answer business questions easily and 2) how to avoid mistakes in analysis.

In-memory BI tools tackle this question by extracting and modeling data, which takes governance out of the data warehouse and increases complexity. When using live query, tools like Power BI can require the DAX language in some scenarios, but this is complex for a typical business user. And in some cases, tools like Tableau don’t allow joins to enable this kind of structure to be queried. Moreover, creating a join between two tables can often only be done by a skilled developer, because the specific methodology depends on the business question at hand, and different questions require different joins – something a business user may not understand.

Thus, the challenge is to empower business users to analyze the data, produce reports and dashboards, tell data stories, and find insights efficiently without needing deep technical skills. 

Query Natively and with Ease with Astrato’s Self-Service Engine

Astrato’s powerful new Self-Service Analytics Engine allows any user to query this structure with a simple drag and drop, providing them with a highly improved self-service experience. 

How has Astrato achieved this remarkable innovation? 

To begin: Astrato’s new Self-Service Analytics Engine harnesses the power of UNION.

The words “join” and “union” in English are practically synonyms. But in SQL, JOIN and UNION are two separate commands, performing two radically different operations. To illustrate the difference, imagine you have two cans of beer, with each can representing one table. The JOIN command would put them next to each other, while the UNION command would put them one above the other. This idea is revolutionary, and a key part of this incredible, innovative Self-Service Engine. 

In further blogs to this series, we’ll learn more about UNION, and take a deeper dive into how and why this engine was built, introducing the challenges posed by  fan traps, chasm traps, and loops on conformed dimensions, and how they’ve been overcome by the team at Astrato.

We can’t wait to share more – stay tuned!