Most enterprise dashboards and reports contain descriptive analytics outlining what happened, like “sales decreased by 10% last quarter,” or “product X and Y are selling well in NY/NJ.”
That’s useful information, but in today’s data-driven environment, simply knowing what happened is table stakes. Getting to the “why” behind what’s happening—diagnostic analytics—is where analytics shines.
But key driver, root cause, and multivariate analysis are all time-consuming and manual. And demand for this form of analysis far outstrips supply. The result? Gnarly analysis bottlenecks, delayed business decisions, suboptimal execution, or some combination of all of these.
Here we’ll outline the key forms of diagnostic analytics, the limitations of traditional manual approaches, and the benefits of AI-powered diagnostic analytics.
In This Post
Limitations of today’s diagnostic analytics approaches
Let’s talk about how diagnostic analytics works. Chances are, if you ask a business analyst to dig into what’s behind a drop in sales last quarter, they’ll combine the following analysis approaches with data and process limitations.
Approach:
Root cause analysis
Description:
Identifies the underlying reasons for an observed problem.
Limitations:
- Time-consuming and resource-intensive
- Impossible to uncover all root causes due to number of variables
- Highly dependent on the quality of data and expertise of the analysts
Approach:
Key driver analysis
Description:
Analysis to identify the most significant factors in a dataset.
Limitations:
- Identifies significant factors but doesn’t explain why they’re significant
- Lacks the ability to drill into multiple levels of drivers
Approach:
Multivariate analysis
Description:
Analyzes multiple variables simultaneously to understand their relationships and effects.
Limitations:
- Requires high skill and complex models
- Interpretation can be difficult due to the interaction of multiple variables
- Sensitive to multicollinearity and requires large dataset
Approach:
Drill-down analysis
Description:
Examines data at detailed levels to understand patterns and causes.
Limitations:
- Data is “rolled up,” and granular drill-down isn’t possible
- May miss the forest for the trees
- Requires robust data infrastructure for effective analysis
Approach:
Variance and cohort analysis
Description:
Compares actual performance with standards or benchmarks or with similar cohorts to identify deviations and their causes.
Limitations:
- Limited to identifying variances without explaining the root causes (e.g., X is Y% more than cohort Z, but why?)
- Relies on the accuracy and relevance of benchmarks, standards, or comparison cohorts
- Can be overly simplistic for complex problems
Approach:
Cluster analysis
Description:
Groups similar data points into clusters to identify patterns and relationships.
Limitations:
- Doesn’t work well with high-dimensional data
- Interpretation can be challenging and subjective
Approach:
Regression
Description:
Identifies relationships between variables and determines the strength and nature of these relationships.
Limitations:
- Assumes linear relationships between variables (i.e., multivariate analysis is challenging)
- Sensitive to outliers and collinearity
- Requires large datasets to be effective
Approach:
Time-series analysis
Description:
Analyzes data points collected or recorded at specific time intervals to identify trends, cycles, and seasonal variations.
Limitations:
- Complex to implement and interpret
- Requires consistent and high-quality time-series data
- Can be influenced by external factors not accounted for in the model
Net-net, today’s manual approaches to diagnostic analytics are:
✘ Time-consuming. It can take days, weeks, or months to gather, clean, prep, integrate, explore, and present diagnostic insights. Opportunities are long gone. Or you’ve bled for weeks!
✘ Unscalable. It’s impossible to manually explore thousands of variables or millions of combinations to isolate true root causes. No pivoting your way through big data.
✘ Unstandardized. No two pieces of analysis are typically executed the same way—let alone by different analysts—and tools abound for each form of analysis, exacerbating the problem.
Thankfully, AI-based analytics can help.
Benefits of diagnostic analytics, powered by AI
AI-based diagnostic analytics—in which AI is used to automate the data-heavy and manual pieces of analysis—overcomes many of these limitations.
Here’s the purpose of diagnostic analytics powered by AI—it helps business and analytics teams:
- Lower the barrier to performing diagnostic analytics, making these valuable forms of analysis accessible to more people in the business and reducing bottlenecks
- Accelerate time to insights by reducing analysis hours
- Enable users to leverage vast amounts of data quickly and efficiently to spot trends and correlations that might take a human analyst days or weeks to identify. Users can gain insights to make more informed decisions faster, giving them a competitive edge.
Leading AI-native analytics platforms include augmented diagnostic analytics approaches that:
- Are intuitive, accessible, natural language-based (vs. code-based), and fully transparent
- Ingest and analyze large-scale, high-dimensional data easily
- Perform necessary pre-processing, insights modeling, ranking, and presentation
AI-powered diagnostic analytics in real life
Imagine you’re a retailer and you notice a sudden drop in online sales. Descriptive analytics will tell you the numbers—sales are down 15%. Diagnostic analytics goes further, identifying that the decline is due to a glitch in your mobile checkout process that frustrates users into abandoning their carts. You’ve spotted the problem and can take specific steps to fix it.
Here’s another example: A customer service team notices an uptick in complaints. Descriptive analytics shows a 25% increase in complaints over the past month. Diagnostic analytics reveals that most complaints are related to a recent software update that introduced a few bugs. Armed with this information, you can address the specific issues causing the complaints and improve customer satisfaction.
Tellius' AI-powered diagnostic analytics unlocks new opportunities
Traditional business intelligence (BI) and manual approaches to diagnostic analytics are laborious, error-prone, and time-consuming, especially when you’re dealing with large datasets, complex analyses, and repetitive tasks. They lack the ability to handle data at scale, quickly iterate through multiple models and feature combinations, and provide real-time updates or automated insights.
Tellius is a next-generation AI-native analytics platform that puts an equal emphasis on diagnostic analytics as traditional descriptive and predictive analytics. Our platform is unmatched in the market thanks to the fact that our diagnostic analytics are:
✔ Rapid and proactive
Eliminates time-consuming manual exploration by applying intelligent automation at each step of the diagnostic analytics process to drastically expedite analysis (more on this in the next section). Furthermore, Tellius‘ automated insights can be proactively pushed to users (vs. traditional, reactive approaches to exploring metrics/KPIs).
✔ Capable of exploring billions of data points
Automatically tests the impact of thousands of dimensions on metrics to get to the true root causes. Tellius is able to operate at this speed and scale on massive unaggregated data because our system is built on a modern, cloud-scalable microservices architecture and distributed compute engine that allows our automated insights ML algorithms and statistical analytics techniques to parse billions of data points. In comparison, traditional analytics and BI tools struggle when it comes to large-scale, highly dimensional data because they were architected for smaller-scale data.
✔ Interpretable and repeatable
Whereas traditional manual approaches to diagnostic analytics can vary depending on the analyst and their preferred analysis approaches, Tellius’ automated insights follow a series of repeatable, uniform steps, making them standardized and interpretable. For example, automated insights undergo pre-processing, enrichment, featurization, automated model and feature selection, ranking and relevance, and presentation steps each time an insight is generated.
✔ Able to iterate quickly
Tellius offers the ability to iterate and re-run jobs as part of an overall analysis process. Users and analysts may not get a “magic bullet” answer the first time, but it gets them on the board faster to home in on variables of interest while providing control to exclude/include data columns and re-run analysis.
✔ Transparent
Users can access and view underlying models and performance while exploring the records that make up segments. Rather than a black box where you get an answer, analysts/users alike can understand (with confidence) why an answer is right, thanks to feature importance ranking, details about the underlying insight model’s performance, and other key factors.
Be proactive, not reactive
In conclusion, diagnostic analytics can transform your data from a rearview mirror into a roadmap by providing the context and causation behind events. This deeper understanding allows you to be proactive rather than reactive, solving problems before they escalate and identifying opportunities for improvement. It’s time to move beyond the basics and leverage the full potential of your data.
Want to check out Tellius’ AI-native analytics platform? Get a demo for your organization here.
