AI-Ready Data
The export you pulled for training came back pre-aggregated, and the next source you tried came back as one nested object with everything you needed buried inside it.
AI-ready data is model input that keeps both its detail and its shape. DataFuseAI's Explode transform flattens nested JSON into named columns with declared data types, holds those names and types steady from run to run, keeps records row-level through the pipeline, and reports every column's statistics before a model reads them.
properties| order_id | segment | opened_at | amount |
|---|---|---|---|
| 1001 | north | 2026-01-14 | 230 |
| 1002 | south | 2026-01-16 | 490 |
| 1003 | north | 2026-02-02 | 118 |
Challenges We Solve
Four failures land on your training data before a model ever reads it: the input arrives already summarised, the input arrives as nested JSON, column names and types change between runs, and bad values reach a run unnoticed. DataFuseAI handles all four inside the pipeline that produces the data.
The export arrives pre-aggregated
The table you were handed was built for a dashboard. An upstream job already grouped and summed it, so the row-level detail your model needed is gone by the time you open the file. That shape is analytics-ready data, not model input.
The source arrives as one nested object
Whether it came from an API response, a document store, or a JSON export, the fields you want sit inside a properties object, several levels down. Your tabular pipeline reads that as a single opaque column, so somebody writes flattening code by hand and maintains it again every time the shape changes.
Column names and types drift between runs
A source adds a field, renames another, or starts returning a number as a string. The next run either fails on load or reads a different shape than the last one, and the difference is not written down anywhere.
Bad values reach the model before anyone checks the columns
A column that arrived typed wrong, mostly null, or skewed by an outlier is easy to miss until a model is already training on it. That check normally lives outside the pipeline, in a notebook or a spreadsheet somebody has to remember to open.
Related reading: wrong numbers reaching a report.
Key Facts
Attributed figures behind this page: outside research first, then what the pipeline produces.
- 1
Nearly 50% of AI-driven digital use cases will miss their ROI targets in 2026, with weak data foundations among the cited causes — IDC, 2026
- 2
Through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data — Gartner, 2025
- 3
The driver catalog's
Alltab lists 36 connector tiles covering relational databases, NoSQL document and wide-column stores, cloud warehouses, object storage and file transports, and a REST API connector — DataFuseAI, 2026 - 4
The Explode transform unpacks a nested
propertiesobject from a source record, mapping each typed key in its schema tree to a named output column — DataFuseAI, 2026 - 5
A profiling run reports, per column, its
Data Type, theDistinct/Non Distinct/Nullsplit, value range, length range,Cardinality,Sum,High Frequency Value, andStandard Deviation— DataFuseAI, 2026 - 6
More than 80% of AI projects fail, twice the rate of non-AI IT projects, and data-quality issues were the second most common root cause named across 65 practitioner interviews — RAND Corporation, 2024
How DataFuseAI Helps
DataFuseAI prepares model input inside the same pipeline that moves it: flatten the nested payload, connect the source it came from, keep the records row-level, and read the column statistics before it leaves. Each of those is a node in the pipeline rather than a script your team writes and maintains beside it.
Flatten nested objects into named, typed columns
The Explode transform takes the nested object in a source record, opens its schema tree, and maps each path you select to an output column alias. Your model input arrives as flat, named columns instead of one JSON string you parse downstream.
Connect the sources the data actually starts in
Point a connection profile at a REST API, a document store, a cloud warehouse, or an S3, FTP, or SFTP path, and it becomes a pipeline source. Making a non-tabular source usable stops being a project of its own.
Keep records row-level, and aggregate only where you asked for it
Aggregation is a node you place on one branch, not something the pipeline does to everything that passes through it. The branches feeding your model keep one record per row; the branch feeding a report gets its own Aggregate node.
Read the column statistics before the data leaves the pipeline
Attach a Profiling node and get a report with one row per column: its data type, the distinct, non-distinct and null split, value and length ranges, cardinality, and the highest-frequency value, on the data you are about to hand over.
Nested JSON, flattened into typed columns
Any source that returns a nested object — an API response, a document store, a JSON export — opens the same way. Run the Explode transform and the object expands into a schema tree you can read. Select a path, give it an output column name, and that key becomes its own column in the pipeline's output. The raw object stays queryable in the source table, so nothing is discarded to get the flat shape.
One modal maps each nested path to an output column
The left pane lists the fields arriving from the source: deal_id, properties, dealname, amount, dealstage, closedate, owner_id, priority. The centre pane headed Schema expands properties and shows each key beneath it with the type it arrived as. The right pane holds the mapping rows: a Depth path such as properties.dealname beside the Alias that names the output column it lands in, with properties.amount, properties.dealstage, and properties.closedate mapped the same way. Nothing here is hand-written parsing code.
Explode transform opened on an API source. Incoming fields on the left, the Schema tree in the centre with the properties object expanded and each key shown with its type, and the Depth to Alias rows on the right naming the output column each path lands in. Demo pipeline built on public and synthetic data, not a customer deployment.The raw object stays queryable after it lands
The same connection profile is queryable directly, whatever the source. Each ingested object appears as a table with typed columns: id string, properties object, createdAt string, updatedAt string, archived boolean, url string. Select from it and the properties cell renders the JSON as the source returned it. Flattening is a transform you place where you want it, not something ingest does to the payload.
properties cell as the JSON object that arrived. Demo data from a public sandbox, not a customer deployment.Connect the sources your model data actually lives in
A source becomes usable here by filling in a connection profile and saving it. That holds for a REST API behind cursor pagination, a document store, an object bucket, and a cloud warehouse: the catalog carries the driver, the profile carries the connection parameters, and the saved profile is a pipeline source like any other.
The catalog covers the source classes model data starts in
The All tab lists 36 connector tiles. Relational engines, document and wide-column stores, cloud warehouses, the AWS RDS and Azure managed families, and the transport tiles S3, FTP, SFTP, Upload, and API all sit in the same driver catalog.
All tab, 36 connector tiles. The tab strip filters the same catalog by family: RDBMS, NoSQL, AWS, Azure, GCP, FILE, FTP, SFTP, S3, and API.A REST API is a profile with an endpoint table
The Endpoints table on this REST API profile holds five rows, each with its own Name, Path, Method, and Pagination mode: GET on every row, cursor on four of them and none on one. Base URL and an Authentication Type of Bearer Token sit above it, the token value masked. More on working with API data.
Base URL and Authentication Type at the top with the token value masked, and an Endpoints table beneath it carrying a Name, Path, Method, and Pagination mode per endpoint. Demo profile, not a customer deployment.A document store fills in the same form
A document store is the same form with different parameters. The Driver field holds the source's Spark connector and driver JARs, Driver Class names the class to load, and the connection parameters below it include Host, Port, User, Password, Database, and Collection. Non-relational sources arrive through the same form as everything else.
Driver field holds the Spark connector and driver JARs, Driver Class names the class to load, and the Connection Parameters tab carries Host, Port, User, Password, Database, and Collection. Demo profile, not a customer deployment.Related: the platform-level view of connecting the sources you already run.
One canvas, from raw nested source to typed sink
This demo pipeline happens to run on five API sources, but the same canvas takes any nested source. Each branch carries its own transforms: Explode on the four payloads that arrive nested, Join, Derived, Route, and Aggregate where a branch asked for it, into five database sinks with Profiling attached. Two of those sinks are fed straight from Route, with no Aggregate node anywhere in their path.
Five API sources, one canvas, five typed sinks
The left column is five Source nodes, each labelled API. Four of them pass through Explode first. Join brings two branches together, Derived adds computed columns, Union merges two flattened branches, and Route splits one branch into two. Route's two outbound edges reach the hs_won_deals and hs_open_deals sinks with nothing in between, so nothing on that path collapses the rows. Three Profiling nodes hang off the sinks.
Three of the branches do end in an Aggregate node, because a reporting consumer asked for that shape.
Source nodes labelled API on the left, through Explode, Join, Derived, Union, Route, and Aggregate transforms, into five sinks labelled postgresql, with Profiling nodes attached. Built on public and synthetic data, not a customer deployment.A computed column, with its type declared up front
The Derived modal names the new column stage_probability and sets its DataType to double before the expression is written. The expression itself is a CASE over an existing column, built from the fields listed beside it. The output column's name and type are set in the pipeline definition, not by whatever the payload returned that day.
Derived transform building a computed column. Column Name is stage_probability and DataType is double, both declared before the CASE expression that fills it. Demo pipeline built on public and synthetic data, not a customer deployment.See the column statistics before the model trains on them
A Profiling node writes a report with one row per column: its Data Type, the Count( Distinct → Non Distinct → Null ) split, Value and Length ranges, Cardinality, Sum, High Frequency Value, and Standard Deviation. You read it on the data the pipeline is about to hand over, not on a sample exported afterwards.
One row per column, on the data about to leave
The report's Column Name and Data Type columns say what the shape is; Cardinality and High Frequency Value say what is actually in it. A column typed string that should be numeric shows up here, and so does a Null count you did not expect. Profiling and the transform library that fixes what it finds sit in the same pipeline.
Column Name, Data Type, Chart, Count( Distinct → Non Distinct → Null ), Value( Min → Mean → Max ), Length( Min → Avg → Max ), Cardinality, Sum, High Frequency Value, and Standard Deviation. Demo pipeline on public and synthetic data, not a customer deployment.Open one column and see its distribution
Any column opens as a histogram. The Chart pane draws the bars, the Distribution Table lists each Value with its Count, and the Column Result pane repeats the column's totals beside them. A skew you would otherwise infer from a summary statistic is on screen instead. Related: data quality validation checks and handling duplicate records.
Chart, the Distribution Table pairing each Value with its Count, and Column Result with the column's totals. Demo pipeline on public and synthetic data, not a customer deployment.Key Benefits
The pipeline that moves the data is also the pipeline that shapes it. That removes the flattening script, the re-export request, and the after-the-fact spreadsheet check from the path between a source and a training run. Nothing in that path depends on a script your team wrote and now maintains.
Your training set has the same columns next month
Output column names and types are set in the pipeline definition, so a run produces the shape you configured, not the shape today's payload happened to have. A source that adds or renames a field does not quietly change what your model reads.
The awkward sources stop being a separate project
A REST API with cursor pagination and a document store are connection profiles, filled in and saved like any relational source. Getting model data out of them is pipeline work, not a bespoke extraction script somebody owns.
The report branch and the model branch can disagree
One canvas can feed a summarised reporting table and a row-level model input at the same time, because aggregation belongs to a branch rather than to the pipeline. You stop choosing between the two consumers or maintaining two pipelines.
You see the columns before the training run does
A column that arrived typed wrong, or mostly null, is something you find in the pipeline's own report rather than downstream of it. The check happens before the data is handed over, not after somebody asks where the numbers came from.
Go deeper: what AI models need from data pipelines and AI-ready data infrastructure.
Frequently Asked Questions About AI-Ready Data
Analytics-ready data is shaped for a human reading a dashboard, so it is aggregated: one row per segment per period. AI-ready data is shaped for a model, so it keeps one row per record, with column names and types that hold from run to run. The same pipeline can produce both, on different branches. The analytics-ready side of that split is a separate job with its own trade-offs.
AI-ready data is data a model can consume directly: one row per record rather than a pre-summarised total, columns that carry declared data types, names and types that stay the same between runs, and no nested blob that has to be parsed on the way in. It is a property of how the data was prepared, not of where it is stored. What AI models need from data pipelines goes further.
It needs to arrive structured at the point the model reads it. The source does not: a REST API returning nested JSON or a document store is a normal input, and the flattening happens in the pipeline. In DataFuseAI that step is the Explode transform, which maps each nested path to a named output column, so the payload can stay semi-structured right up until the moment it needs not to be.
Schema drift is a source changing the shape it returns: a new field, a renamed field, or a value that used to be a number arriving as a string. A pipeline that reads whatever it is given either fails on load or loads a different shape than last time without saying so. Declaring the output column names and types in the pipeline definition is what keeps the run's shape a decision you made rather than one the payload made.
You map each path you want to its own output column. In DataFuseAI the Explode transform shows the incoming fields, a schema tree of the nested object with each key and its type, and a list of mapping rows: a source path like properties.dealname on one side, the output column name it lands in on the other. The raw object stays queryable afterwards, so flattening removes nothing.
An aggregate is a summary: the rows that produced it are gone by the time you read it. A model that needs one record per event cannot recover those rows from a total, so an export built for a dashboard arrives already missing what the training run wanted. Aggregation is not the problem; aggregating on the branch that feeds the model is. Put the Aggregate node on the branch that asked for a summary.
Clean is part of it, not all of it. Data can have no nulls, no duplicates and no bad values and still be the wrong shape for a model: pre-aggregated, or nested, or arriving with different column names each run. AI-ready means clean and structured and stable. A profiling report tells you about the first; the pipeline definition is what gives you the other two.
Check three things on the data you are about to hand over. Is it still one row per record, or has something summarised it? Do the column names and types match what the last run produced? And does a per-column report show the distinct, non-distinct and null counts, the value and length ranges, and the highest-frequency value you expected? A profiling node inside the pipeline answers the third without exporting anything.
The failures cluster into three kinds. Availability: the data a model needs is in a source nobody has connected yet. Quality: it is there but wrong, with nulls, duplicates, or values typed as something they are not. Structure: it is there and correct but in the wrong shape, already summarised or still nested, so it cannot be read one record at a time. Structure is the one a pipeline is in a position to fix.
Yes, if you can connect to where it lives. The driver catalog covers relational engines, document and wide-column stores, cloud warehouses, and file transports including S3, FTP, and SFTP, so an older database or a flat-file drop is a connection profile like anything else. Once it is a pipeline source, the same transforms and the same profiling apply to it.
In practice, whoever owns the pipeline between the source and the model: usually a data engineer, sometimes the ML engineer who got tired of waiting. The work is preparation work, not modelling work, and it sits with your team rather than with whoever built the source system. Putting it in the pipeline rather than in a notebook is what makes it somebody's job instead of everybody's.
Sources for the figures on this page
The outside figures cited on this page are listed below with their named source, year, and link.
| Statistic | Source | Year |
|---|---|---|
| Nearly 50% of AI-driven digital use cases will miss their ROI targets in 2026, with weak data foundations among the cited causes | IDC | 2026 |
| Through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data | Gartner | 2025 |
| More than 80% of AI projects fail, twice the rate of non-AI IT projects, and data-quality issues were the second most common root cause named across 65 practitioner interviews | RAND Corporation | 2024 |