In the last installment of the From Data to Decisions blog series, we introduced Spotfire® Data Science as the new gold standard for industry-specific analytics. It combines powerful data science tools, intuitive and interactive visualizations, and modular extensibility in one seamlessly integrated platform.
This time, we’re focusing on the built-in data functions in Spotfire Data Science, which make the platform immediately impactful. These no-code, out-of-the-box analytics functions remove friction from the analysis process—no coding required, no configuration guesswork—and put sophisticated geospatial, time series, and data integrity tools into analysts’ and domain experts’ hands.
Let’s explore how these built-in capabilities are helping organizations accelerate insight and drive smarter decisions, right from day one.
Nine new data functions built directly into Spotfire Data Science.
Fast, flexible geoanalytics—without external tools
Spatial awareness is critical across industries, especially in energy, logistics, agriculture, and environmental sciences. With Spotfire Data Science built-in geospatial data functions, users can natively prepare, transform, and analyze geographic data without needing third-party GIS tools or custom Python scripts.
Need to convert geological survey data into the right coordinate reference system (CRS)? Spotfire Data Science has it covered. Want to measure geodesic distances to plan new access roads or pipelines? You can do that too, with control over accuracy, performance, and level of detail.
Area and distance calculations, even for irregular or overlapping polygons, can now be performed in a few clicks. For industries like oil and gas, this means accurate well placement, better infrastructure planning, and more confident land-use assessments—all visualized instantly, directly in Spotfire.
Pattern recognition and well similarity analysis simplified
Understanding subsurface complexity is a cornerstone of successful exploration and development for energy companies. Spotfire Data Science includes out-of-the-box pattern recognition functions explicitly built for time-dependent or depth-dependent data, such as well logs.
With just a few configurations, you can use dynamic time warping to compare the sequence patterns of different wells to pinpoint the most geologically similar formations. Standard and scaled distances, variability metrics, and outlier detection all help drive more profound insights from otherwise hard-to-interpret curves.
These tools are especially valuable in exploration workflows, where identifying analog wells early can dramatically reduce risk and cost. Optional data normalization and smoothing means you’re always comparing clean, meaningful data, even in missing or irregular values. Add in visual outputs of path comparisons and similarity rankings, and you have a complete analytic workflow that’s powerful, fast, and easy to act on.
Time and depth series preparation
Whether you’re working with OSIsoft PI systems, sensor feeds, or geological depth logs, analyzing time or depth data often means wrestling with irregular sampling, noisy data, or inconsistent intervals. Spotfire Data Science handles all of this for you.
Built-in data functions for smoothing make it easy to see real trends and eliminate misleading spikes or gaps. Choose from options like LOESS, exponential, or Friedman’s supersmoother based on the nature of your data, and adjust smoothing levels to find the balance between granularity and clarity.
Upsampling or downsampling? Select your method, and Spotfire will handle the rest—there is no need to write custom interpolation or aggregation logic. Whether you’re aligning data across sensors, preparing it for machine learning, or cleaning up a messy dataset, this is prep work at its smartest.
Insightful summarization of missing data
Missing data isn’t just a nuisance; it’s often the difference between trustworthy analysis and flawed conclusions. That’s why Spotfire Data Science includes built-in diagnostics to help you evaluate, summarize, and plan for incomplete datasets.
Simply add the missing data summary data function to provide a summary of all missing / invalid values from your data.
You get high-level visibility into which columns and rows have missing values, the percentage of data affected, and correlations across multiple fields. This enables informed decisions: Should you impute missing values? Drop records? Adjust your model?
These insights support “what-if” scenarios that help you weigh the trade-offs and understand the potential impact of different handling strategies—all before you begin modeling or visualizing.
Built-in to help you discover insights faster than ever
What makes these data functions powerful isn’t just their technical depth; it’s how accessible they are. They’re built into Spotfire Data Science and ready to go out of the box, but fully configurable to meet the needs of your data, industry, and business challenges.
Whether you’re an energy analyst planning the next well, a manufacturing engineer optimizing production timelines, or a data scientist ensuring data readiness for modeling, these built-in capabilities fast-track the insight cycle and reduce dependency on custom scripting or external preprocessing.
With every user able to execute them (based on permissions), these data functions extend the power of data science beyond the core analytics team, democratizing insight and accelerating impact across the enterprise.
Stay tuned
Stay tuned for our next From Data to Decisions blog, where we’ll explore Spotfire Data Science Add-ons, which allow users to access plugin visualizations and actions implementing solutions to industry-specific use cases.
Want to see these data functions in action? View our latest webinars that dive into the power of Spotfire Data in energy and manufacturing—or reach out to learn how these built-in tools can help you simplify analysis, enrich decision-making, and turn complexity into clarity.