Thinking about implementing artificial intelligence (AI) and machine learning (ML) in finance? You’re not alone. Many other organizations are either already using AI or learning about it, so we’ve got you covered with six advanced Power BI Embedded features.
In this blog, we’ll explore six AI-powered tools—guided by Rishi Grover, Vena’s Co-Founder and Chief Solutions Architect—that you can leverage to generate faster insights for your team.
1. Key Influencers
“How do I see which factors affect my selected dimension?”
When you’re analyzing one dimension, Key Influencers lets you:
- see which factors affect your selected dimension
- contrast the relative importance of those factors
Key Factors shows you:
- top contributors to your selected dimension that influence an increase from your base scenario to your comparison scenario
- top contributors to your selected dimension that influence a decrease from your base scenario to your comparison scenario
You can analyze any of your dimensions and drill down into your accounts with the greatest variability.
2. Top Segments
“Can I analyze multiple dimensions together?”
Top Segments shows you the influencers made up of a combination of values.
The difference between Top Segments and Key Influencers is that while Key Influencers analyzes a single dimension, Top Segments analyzes multiple dimensions together.
Now, with multiple dimensions selected, you can analyze:
- top contributors that influence your comparison scenario to be higher than your base scenario
- top contributors that influence your comparison scenario to be lower than your base scenario
3. Decomposition Tree
“How do I analyze dimensions and then slice and dice to visualize data across multiple dimensions?”
With your dimensions selected, Decomposition Tree shows you aggregated data and helps you determine where next to drill down into. You can analyze your selected dimensions and then drill down into your data in the order of your selected dimensions.
You can conduct root cause analysis by using AI Splits—which automatically helps you find the highest and lowest values with all available fields considered—within the Decomposition Tree.
4. Predictive Forecast
“How is AI added into my forecasts?”
This is best used for time series data or whole numbers that increase uniformly. With your actuals brought in, Predictive Forecast shows you forecast results that are adjusted by:
- desired confidence interval (you can calculate a range based on this adjustment)
You can also:
- see how these adjustments affect your results
- forecast with greater granularity to weeks or days
- explore what-if scenarios
5. Anomaly Detection
“How else does AI support root cause analysis?”
Without even needing to slice and dice your data, for any of your data entities or segments in your time series, Anomaly Detection shows you for each anomaly detected:
- detection confidence interval
- variance from the expected value
- possible explanation(s) in natural language
You can identify mistakes or outliers, along with their possible causes, with seasonality—and not only trends—considered.
6. Natural Language
“How can my users get answers quickly?
Natural Language lets users submit simple queries—by inputting a few keywords—to get an answer in the form of a Q&A visual that you can drill down into.
The Q&A visual has four core elements:
- Question box
- Auto-suggested questions
- Ability to convert the Q&A visual into a standard visual
- Ability to configure the natural language engine
Now, you can empower your users to get their answers when they need them.
Power BI Embedded
With Power BI Embedded—the market’s only end-to-end reporting and analytics solution—now directly embedded into Vena’s Complete Planning platform, you can integrate AI-informed data strategies into company-wide business intelligence, enabling instant insights at scale for you to work smarter and faster—without requiring additional Power BI licenses.
Using out-of-the-box interactive dashboards, you can turn data into real-time, actionable insights for every one of your users in their data-driven decision making. As you drill into your data, you should be able to visualize and review your unbiased data against baselines, generate insights and make proactive decisions.