To produce a truly original and actionable idea, a problem-solver needs to not only understand what has happened, but why it happened in the first place. Accordingly, I’m delighted to introduce the all new SparkBeyond Discovery: a no-code AI Analytics platform that empowers data professionals everywhere with the most powerful data analysis tools. Tools that were once only used by leading enterprises. 

It’s now easier, faster and more precise than ever for data professionals, regardless of their experience with analytics, to access, explore, and learn so much more from their data. By listening to our clients and learning from their behavior: how different roles use analytics in their day-to-day work, how they explore data to answer ad-hoc questions, and how they make decisions, we’ve rebuilt SparkBeyond Discovery from the ground up. 

We soon understood that if actionable insights are directly accessible to the people who need them, they will be encouraged to keep asking questions and get a deeper understanding of their organization’s business landscape. In this light, we’ve rearchitected SparkBeyond Discovery to further drive the cycle of learning, iteration, and improvement, which is critical to keep pace in a world where speed is often the defining characteristic of market leaders.

We know how important it is for a company to be united around their one core metric. That’s why we’ve tried to make it incredibly easy for every data professional — not just data scientists  — to query data and discover insights, without having to use python, R or advanced SQL. 

Let’s explore how SparkBeyond Discovery — previously only available to enterprise elites — is ideal in equipping cross-functional teams with data science capabilities

SparkBeyond Discovery is renowned for testing millions of hypotheses in minutes, as well as its groundbreaking methodology that provides the unique foundation for accurate, actionable insights and robust predictive models. Now, with a simplified user interface and embedded methodology across all steps of the analyst workflow, the re-architectured platform equips all data professionals with powerful data science expertise within an intuitive no-code environment.

For example, if analysts don’t know which targets to focus on, or want to look across multiple permutations across multiple time periods, the platform’s drag-and-drop UI allows data professionals to build their own search space across text, geo-spatial and time-series data. By exhaustively searching the data for patterns, leaving no stone unturned, a data professional can be confident in the resulting features generated by the platform. 

Yet there’s no tradeoff between such user-friendly simplicity and the powerful engines of the original SparkBeyond Discovery. The platform is built atop the world’s largest collection of algorithms, integrating millions of code and functions to generate superior, enriched composite features, while allowing the user to examine a range of metrics on each feature. From today, a data professional can explore all possible ‘a-ha’ moments across all types of complex data sources, and zero in on the best candidates at the click of a button. 

What used to take a data science team weeks, can now take an analyst hours, allowing them to focus on more advanced data analysis and creativity beyond the ability of machines.

As traditional analytics dashboards are static aggregations of the underlying data, they offer a very narrow analysis along a specific dimension. Many ‘insights’ reveal what’s happening, yet this approach loses so much context as each point is a one-dimensional representation of everything that underlies it. 

We like to say here at SparkBeyond that your data only tells part of the story. Most analyst reports don’t take into account the influence of external factors, that can range from weather and local events, to macro-economic factors and market conditions. Instead, the new SparkBeyond Discovery has built-in external data sets like Points-of-Interest maps or demographic data, that provide context, help improve feature interpretability and reinforce model performance with a comprehensive viewpoint.

The magic of AI analytics happens when an analyst dives further into the data. The new SparkBeyond Discovery allows the user to click on any data point and dive into those underlying instances and behaviors, surfacing granular insights in accessible, natural language. 

This kind of open data access also helps the data professionals operate on a higher level. Instead of an overloaded Analytics Center of Excellence (CoE) team running code in a siloed environment, data teams can be the organization’s ‘data educators’, helping others get up to speed with analytics tools so that they can answer their day-to-day questions — as the proverb goes, teaching them to fish, rather than supplying a fish every time they’re hungry. As other teams become self-sufficient, data teams can spend their time tackling exploratory, complex problems, instead of fulfilling an endless stream of requests.

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