TL;DR
Automotive data analytics pays back fastest when an OEM or supplier starts with warranty and defect analytics, a dealer group with service retention, and a fleet with vehicle health. It works by joining plant, supplier, vehicle telemetry, warranty and dealer data, so teams can explain problems and act before costs grow. The join key is the VIN plus the part lot, which lets engineers trace a field failure back to one supplier batch or one station. Microsoft Fabric, Databricks or Snowflake can all hold this data, so data readiness matters more than the platform. Connected-car data also carries legal weight, since the FTC’s January 2026 order against GM requires express consent before collecting or sharing it. Score each use case on value and readiness, prove one against a measured baseline, then scale.
Key Takeaways
Automotive data analytics joins plant, supplier, telemetry, warranty and dealer data, so teams can act on problems that cross departments.
The VIN and the part lot number are the join keys, so engineers can trace a field failure to a plant, station and supplier batch.
Warranty and defect detection is usually the best first use case for OEMs and suppliers, because the cost is large and already measured.
A lakehouse with a VIN-keyed silver layer and a shared semantic model scales well, because every use case reuses the same keys and metrics.
Connected-vehicle data needs consent and purpose controls, since the FTC’s January 2026 final order against GM and OnStar requires express consent.
Measure ROI against an operational baseline, such as warranty cost per unit, recorded before the pilot starts.
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How Business Intelligence is Transforming Modern Manufacturing Plants?
A Kanerika video on how business intelligence is changing modern manufacturing plants, the plant-floor starting point for the automotive data analytics this guide covers.
A Warranty Spike the Plant Data Already Explained
When a field quality engineer opens the weekly warranty report, she sees a cluster of claims for the same steering component. The vehicles were all built at one plant, during a narrow production window. Dealers, meanwhile, replace the part one claim at a time.
The answer is already sitting in data the company owns. A single supplier lot went to one assembly line, while a torque station on that line drifted out of range for a few shifts. The plant’s execution system logged the drift and the supplier’s certificate logged the lot, but nobody joined the two.
Weeks pass before someone connects the claims to that lot and that station. By then, more vehicles from the same window are on the road. Every week of delay widens the set of cars that may need inspection, and the bill grows with it.
That gap, between data that exists and data that is connected, is what automotive data analytics closes. This guide explains where the data comes from and which use cases pay back. It then covers how the architecture fits together and how to decide where to start.
What Is Automotive Data Analytics?
Automotive data analytics is the practice of collecting data from across the vehicle lifecycle and turning it into decisions about design, production, service and sales. The lifecycle is the part that matters most, because a car produces data long before a customer sees it. That data sits in supplier quality records, plant machines and end-of-line tests.
After the sale, the car keeps producing data through telematics, service visits and warranty claims. Analytics then links those records, so a team can follow one vehicle or one part from the supplier’s line to the repair bay.
Three pressures make this urgent. Vehicles carry more software and connectivity, so they now report their own condition. Warranty and recall costs also come straight out of the manufacturer’s margin. Finally, regulators treat connected-vehicle data as consumer data with consent rules, as the FTC’s January 2026 GM order shows.
Teams usually group analytics work into four types. Each one answers a harder question than the one before it, so each also needs more joined data.
Type Question it answers Automotive example
Descriptive What happened? Warranty claims per thousand vehicles by model and month Diagnostic Why did it happen? Tracing a claim spike back to one supplier lot and one torque station Predictive What is likely to happen? Flagging vehicles when battery or brake signals point to a coming failure Prescriptive What should we do? Recommending which VIN range to inspect first, or which part to stock at which dealer
Many programs stall at the descriptive level because the data needed for diagnosis lives in a different system. A different team usually owns that system, and nobody has joined it yet. Crossing that line is what the rest of this guide is about.
Automotive Analytics vs BI vs AI
Business intelligence is the reporting layer. It gives plant managers and dealer principals dashboards on yield, sales and service volume, though usually from one system at a time. Our guide to business intelligence for manufacturing covers that plant reporting layer in more depth.
Automotive analytics goes further by joining data across systems to explain causes, for example by linking a warranty claim to its build record. So it can answer the diagnostic and predictive questions that a single-source dashboard cannot reach.
AI and machine learning are tools that sit inside that work, as our post on machine learning in predictive analytics explains. A vision model that spots weld defects depends on the same joined and cleaned data, and so does a model that predicts battery faults. Without that data, the model simply learns from noise.
Case Study
52% Fewer Configuration Errors in Connected Vehicle Data
A mobility services provider serving OEMs, EV makers, insurers and fleets needed vehicle telemetry delivered ready for analysis across many clouds. Kanerika automated the data destinations and storage setup, cutting manual configuration errors by 52%.
Read the Case Study → Where Automotive Data Comes From and How to Connect It
Automotive data lives in five domains, and a different team usually owns each one with its own systems. For example, a plant runs on manufacturing execution systems (MES) and machine sensors, while purchasing and supplier quality manage part and lot records.
Connected-vehicle teams own telemetry, while the service organization owns warranty claims and repair orders. Sales and marketing also own dealer and customer data. Each domain answers its own questions well, but the expensive problems almost always cross from one domain into another.
Domain Typical systems Example signals Usual owner
Plant and manufacturing MES, PLC and sensor historians, end-of-line test Torque values, cycle times, test results, scrap Plant operations Supplier and quality ERP, supplier portals, quality management system Part lot numbers, certificates, incoming inspection Purchasing and supplier quality Vehicle telemetry Telematics units, connected-vehicle cloud Fault codes, battery state, location, usage Connected-vehicle team Warranty and service Warranty system, dealer management system Claims, repair orders, labor codes, parts replaced Aftersales and field quality Dealer and customer Dealer management system, CRM, sales systems Orders, service visits, survey results Sales and marketing
The plant domain on its own is a deep subject, so we cover it in our guide to manufacturing data analytics . This post looks instead at what happens when all five domains are connected to each other.
Why VIN and Part-Lot Traceability Is the Join Key
Two identifiers can link every domain in the table. The vehicle identification number (VIN) follows a car from build to scrap, while the part lot number follows a component from the supplier’s line into the vehicle.
Here is how a trace works in practice. A warranty claim names a VIN and a failed part. The build record for that VIN then shows the plant, line, shift and station readings.
The parts record also shows which supplier lot went into the car. With those three joins, an engineer can ask how many other VINs received the same lot through the same station. That question sets the size of a field action.
Without the joins, teams estimate scope from build dates and risk containing too much or too little. Once the joins exist, scope follows the evidence instead.
The hard part is rarely the database. Instead, it is getting every system to record the VIN and lot the same way, in the same format, at the moment each event happens.
The VIN at least has a fixed format. The federal VIN rule, 49 CFR 565.13 , sets it at 17 characters, and the letters I, O and Q are never used. A silver-layer check can therefore reject malformed VINs before they break a join.
Batch, Change Capture and Streaming: Match Latency to the Decision
The right ingestion pattern depends on how quickly someone has to act on the data, so most automotive sources can start as batch loads.
Latency Example decision Ingestion pattern
Daily or weekly Warranty trend review, supplier scorecards Scheduled batch loads from ERP and warranty systems Minutes to hours Quality hold on a suspect lot, parts reorder Change data capture from operational databases Seconds Station alarm, vehicle fault alert to a fleet manager Event streaming from sensors and telematics
Streaming everything raises cost without improving decisions that only happen once a week. So start with batch, then add change capture where a delay costs money. After that, stream only the signals that someone acts on within minutes. The difference between moving data and joining it is covered in our comparison of data ingestion vs data integration .
Automotive Data Analytics Use Cases That Pay Back
The use cases that pay back all change a specific decision that already costs money when it is made late or wrong. That is a better filter than technical novelty, because a clever model attached to no decision saves nothing.
Each use case below therefore follows the same pattern. It names the problem, the data that has to be joined, the decision that changes and the metric that proves it worked.
Manufacturing Quality and Defect Detection
Quality analytics links station readings, test results and supplier lots to defects found at end of line or in the field. The decision it changes is when to stop, inspect or adjust a process. First-pass yield is the metric to track, along with scrap rate.
Automotive manufacturers rely on data analytics to improve quality control and reduce production errors. AI systems now scan components in real-time, flagging defects before they reach the assembly stage. This reduces waste, speeds up production, and improves consistency across plants. In parallel, some teams also conduct automotive pentesting to ensure that connected systems remain secure throughout the production process.
Vision systems for defect detection get most of the attention, but the larger gain usually comes from diagnosis. When a defect appears, engineers see which station, shift and lot it shares with earlier defects. So they start from evidence instead of a blank page.
Plants that already collect station data can often begin here before buying new sensors. In practice, the work is mostly about connecting what the line records with what the quality team finds later.
Warranty, Field Quality and Recall Analytics
Warranty is where data problems turn into direct cost, because every claim is paid from the manufacturer’s margin. It is also the place where all five domains meet.
A field quality team starts with claims and repair orders. Text analytics then groups technicians’ notes into failure modes, since the same fault gets described in many different ways. After that, the team joins the claims to build records and supplier lots.
That join moves the question from “how many claims did we get” to “which vehicles are at risk.”
The answer matters most when a defect may affect safety, since the first decision is which vehicles are affected. Precise traceability lets that scope follow VIN range, plant and lot instead of a wide date window. A tighter scope therefore means fewer cars called back without cause and fewer affected cars missed.
US rules make that scope a documented decision. Under 49 CFR 573.6 , a manufacturer reports to NHTSA within 5 working days of deciding a defect is safety related. The report must explain the basis for the recall population and how recalled vehicles differ from similar ones left out. VIN and lot joins give that explanation its evidence.
Telemetry also adds an early warning layer. Fault codes from connected vehicles can show a pattern before the first claims arrive, which gives engineers time to act. Track warranty cost per unit, claims per thousand vehicles and days from first signal to containment.
Predictive Maintenance and Vehicle Health
Predictive maintenance uses sensor and fault-code history to estimate when a component will fail, whether on plant equipment or on vehicles in service. The value is fewer unplanned stops, as well as repairs booked before a breakdown. We cover model choice and deployment in depth in our guide to AI-driven predictive maintenance .
Picture a delivery fleet where battery voltage and brake-wear fault codes start trending on a group of vans. The model flags those vans for service at their next planned depot stop. The decision shifts from towing a broken van to booking the repair before the breakdown. Roadside events or unplanned downtime per 1,000 vehicles then show whether it worked. The same signals also feed route and dispatch planning, covered in our look at AI in transportation .
Supply Chain, Parts and Inventory Analytics
Parts analytics joins supplier deliveries, production schedules and dealer parts demand. It then helps decide what to reorder, where to hold stock and which supplier risks need a backup plan. Our roundup of supply chain analytics tools compares the software teams use for that work. Freight and warehouse questions overlap heavily with logistics analytics , so we treat them there.
A typical case starts when a late supplier lot shows up against next week’s production schedule. The team can then move dealer stock or expedite the shipment before back-orders build, measured by dealer parts fill rate and back-order days.
EV Battery and Charging Analytics
Batteries make electric vehicles a data problem by design. The battery management system records voltage, temperature, current and charge cycles, and together those signals show how each pack is aging.
Analytics then turns that history into a state-of-health estimate for every pack. It can flag cells that degrade faster than their peers. It can also test whether frequent fast charging relates to faster degradation.
Those estimates also feed the reserve set aside for battery warranty claims. On the manufacturing side, the same method links cell test data to field performance, so a weak cell batch shows up early. Track the gap between predicted and actual state of health, as well as battery warranty cost per vehicle.
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AI Solutions for the Automotive Industry
Kanerika builds AI models for automotive teams, from vision inspection on the line to predictive models on vehicle and battery data, on top of governed, joined data.
Explore AI in Automotive Dealer, Sales and Aftersales Analytics
Dealer analytics links sales, service and customer records. It shows which customers come back for service and which ones move to independent shops instead.
Analytics helps automakers personalize services based on user behavior. By combining CRM data with connected car insights, companies can offer tailored maintenance and driving tips. In service environments, data-driven tools such as Tekmetric’s reliable digital vehicle inspection solution help translate inspection data into clear, visual reports that improve transparency, customer trust, and service approval rates.
For an OEM, the useful view sits at the network level. There, it shows which dealers turn inspection findings into approved work and where parts shortages delay repairs. It also shows which models drive repeat visits, so service retention and repair-order approval rate are the metrics to watch.
Connected Vehicle, Fleet and Usage-Based Insurance Data
Connected vehicles send location, speed, braking and fault data back to the manufacturer’s cloud. Fleet operators then use it to plan maintenance and routes, a pattern we cover in IoT in logistics and supply chain . Insurers use driving behavior to price usage-based policies, as explained in our guide to telematics in insurance .
This is the most sensitive data in the value chain, because it describes where people go and how they drive. The privacy section later in this guide covers what that means in the US. The on-vehicle side, where data is processed before it leaves the car, is the subject of our post on edge computing in autonomous vehicles . For an OEM, the first decision is usually which signals to collect at all. Track the share of active VINs with recorded consent, along with the telemetry cost per vehicle.
How Automotive Analytics Works: A Reference Architecture
A reference architecture is the path data takes from the plant floor and the vehicle to a decision. In automotive it usually has six layers, and each one has a clear job.
Sources. MES, sensors, ERP, supplier portals and telematics, as well as warranty and dealer systems.Ingestion. Batch loads, change data capture and event streams, chosen by how fast each decision must be made.Lakehouse storage. The bronze layer keeps raw data as received. Next, the silver layer cleans it and keys every record to a VIN or part lot. Finally, the gold layer holds business-ready tables, such as warranty claims by build cohort.Semantic model. Shared definitions for metrics such as first-pass yield, so every dashboard uses the same math.BI and machine learning. Dashboards for daily decisions, while models handle prediction and anomaly detection .Action and feedback. Alerts, quality holds and work orders, with outcomes written back so rules and models improve.
The platform vendors document this layered pattern. Microsoft calls the medallion design the recommended approach for Fabric, with a silver layer that fixes errors, standardizes formats and removes duplicates. Databricks describes the same three layers as raw, validated and enriched.
Following One Warranty Claim Through the Layers
Follow one warranty claim through those layers to see how they connect. The claim lands raw in bronze, exactly as the warranty system sent it. In silver, its VIN is normalized and joined to the build record and the supplier lot.
The claim then appears in a gold table of claims by build cohort. The semantic model defines claims per thousand vehicles, so a dashboard flags the cohort that stands out. Once an engineer places the quality hold, that outcome is written back for the next review.
In code, the silver and gold steps are two short queries. The example below is Spark SQL, as it would run in a Databricks or Fabric lakehouse notebook, with illustrative table names.
-- Silver: clean the VIN, then join each claim to its build record and part lot
CREATE OR REPLACE TABLE silver.warranty_claims AS
SELECT
c.claim_id,
UPPER(TRIM(c.vin)) AS vin,
c.claim_date,
c.failed_part_number,
b.plant_id,
b.line_id,
b.build_date,
p.supplier_lot
FROM bronze.warranty_claims AS c
JOIN silver.build_records AS b
ON b.vin = UPPER(TRIM(c.vin))
JOIN silver.part_installs AS p
ON p.vin = b.vin
AND p.part_number = c.failed_part_number
WHERE UPPER(TRIM(c.vin)) RLIKE '^[A-HJ-NPR-Z0-9]{17}$';
-- Gold: claims per thousand vehicles by plant, line and supplier lot
CREATE OR REPLACE TABLE gold.claims_by_cohort AS
SELECT
s.plant_id,
s.line_id,
s.supplier_lot,
v.vehicles_built,
COUNT(DISTINCT s.claim_id) AS claims,
COUNT(DISTINCT s.claim_id) * 1000.0 / v.vehicles_built AS claims_per_thousand
FROM silver.warranty_claims AS s
JOIN gold.vehicles_built_by_lot AS v
ON v.plant_id = s.plant_id
AND v.line_id = s.line_id
AND v.supplier_lot = s.supplier_lot
GROUP BY s.plant_id, s.line_id, s.supplier_lot, v.vehicles_built;The silver layer is where most of the value gets created. Once every record carries a clean VIN or lot key, a question that used to need three teams and a spreadsheet becomes a single query.
Teams new to this layered pattern can start small, and our comparison of a data lake vs lakehouse explains why the layers matter. For instance, one pair of domains, such as warranty claims and build records, is enough to prove the design before telemetry is added.
Tools and Platforms: Microsoft Fabric, Databricks, Snowflake and Industry Tools
Three cloud data platforms are common choices for enterprise automotive analytics builds, and each can handle the batch and streaming mix described above. The real differences are in streaming and in what your stack already runs on.
Microsoft documents Fabric Real-Time Intelligence as a set of eventstreams, eventhouses for time-based events, whose data can be made available in OneLake, and Activator alerts. Our Fabric real-time intelligence guide covers each piece. Databricks, by comparison, builds streaming pipelines on Apache Spark Structured Streaming , which uses the same APIs as batch processing.
Snowflake documents Snowpipe Streaming for row-level ingest from apps and devices. It describes the service as designed for up to 20 GB per second per table. Data can be queryable in as little as 5 seconds, though Snowflake notes that results depend on workload.
Telematics platforms, quality management systems and dealer management systems stay as sources, and the platform joins what each of them holds.
Platform Documented streaming feature Good fit Watch out for
Microsoft Fabric Real-Time Intelligence: eventstreams, eventhouses, Activator alerts When the business already runs on Power BI, Microsoft 365 and Azure Sizing shared capacity across many workloads Databricks Spark Structured Streaming with the same APIs as batch When teams do heavy data engineering and machine learning on telemetry Needs strong engineering skills to run well Snowflake Snowpipe Streaming for row-level ingest When SQL-first teams share data with partners Throughput depends on workload, so test with real volumes Industry tools Telematics, quality management and dealer platforms When a task stays inside a single domain They rarely join data across domains
Platform choice matters less than many buyers expect. A team that has already modeled VIN-keyed data will usually move faster than one that picked the “best” platform but skipped the modeling.
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Data Analytics Services
Kanerika designs and builds analytics platforms on Microsoft Fabric, Databricks and Snowflake, from ingestion and data modeling to dashboards, and measures each pilot against a recorded baseline.
Explore Data Analytics Services Which Use Case First? A Value-vs-Readiness Framework
The first use case should be the one with the highest value that your data can support today, so each candidate gets scored on both. Value is what the decision is worth if it improves, while readiness is whether the data exists, is joined and is trusted.
A high-value idea with no usable data is a data project first. Scoring makes that visible early, before a team commits budget to a model it cannot feed. The five criteria below also keep the scoring consistent across candidates.
Criterion What to check Scores high when
Business value Yearly cost of the problem Warranty or scrap cost is large and already reported Data availability Do the sources exist and record VIN or lot? Keys are captured at the source, so joins work Data quality Completeness and consistency Few missing or mismatched keys Decision owner Who acts on the output? One named owner who can change the process Time to evidence How soon can results be measured? Effects show within one or two quarters
Readiness is easiest to score with a join rate. Count the share of warranty claims that match both a build record and a supplier lot. Set the bar before scoring, for example 95%, and send any use case below it to the data roadmap instead of a pilot.
Plot the candidates on two axes. High value with high readiness is where to start, while high value with low readiness goes on the data roadmap. Low-value ideas still wait, whatever their readiness.
Worked Example: Choosing Analytics for Warranty Defect Detection
Take a tier-1 supplier weighing three candidates. The steps below show how the framework then turns that list into one pilot.
List the candidates. Warranty defect detection, battery health analytics and dealer parts forecasting.Score value. Warranty spend is the largest of the three costs and is reported monthly, so it scores high.Score readiness. Claims carry the VIN and build records carry station data, but supplier lots sit in a separate quality system.Close the gap. The team adds the lot number to the build-record join, which is a scoped data task instead of a new platform.Run a pilot. One model line for one quarter, with warranty cost per unit and days to containment as the measures.Decide. If days to containment fall against the baseline, then extend the approach to other lines.
Battery health may score higher on value later, once enough telemetry history builds up. That is why the scoring is worth repeating every quarter instead of filling it in once.
Cost, Timeline and How to Measure ROI
The cost of automotive analytics is driven mostly by data work, so the number of sources and their condition matter more than any license. Price ranges quoted without that context tend to mislead.
A better approach is to understand what drives cost, then prove the return against a baseline. For timing, plan the first pilot around one quarter of measurable results, since that matches the time-to-evidence criterion above.
The Main Cost Drivers
Number of sources. Each MES, ERP, telematics or dealer feed needs a connector, mapping and monitoring. Moving off a legacy system is its own project, covered in our guide to data migration in automotive .Data quality work. Fixing missing VINs, mismatched part numbers and duplicate records is often the largest line item. Data observability tools catch those breaks before a dashboard shows the wrong number.Streaming volume. High-frequency telemetry raises ingestion and storage cost, so sample or aggregate where the decision allows.Platform capacity. Compute for transformations and queries, as well as model training.People. Data engineers and analysts, together with a product owner from the business side.Change management. Training plant, field quality and dealer teams so they act on the output.
Calculate ROI From an Operational Baseline
ROI is easiest to defend when it comes from a number the business already tracks. The method below keeps the comparison honest.
Record the baseline. Measure the current KPI over a recent period, for example warranty cost per unit on the pilot model line. The KPI menu below lists the usual choices.Run the pilot. Keep the scope fixed to one line, plant or region so that the comparison is fair.Verify the saving. Compare the KPI after the pilot with the baseline, then have finance confirm it.Subtract the run cost. Deduct platform, people and support cost to get the net return.Decide on scale. Extend only the use cases whose net return still holds.
A simple example shows the arithmetic, using assumed figures. Say a pilot line builds 50,000 vehicles a year, and warranty cost per unit falls by $4 after the pilot. The gross saving is then $200,000 a year.
If running the solution costs $120,000 a year, the net return is $80,000. These numbers are illustrations, not benchmarks.
A KPI Menu for the Baseline
The KPI for step 1 should be a number the business already trusts. The menu below pairs each common benefit with the KPI that proves it, along with the person who usually owns that number.
Benefit KPI to track Usual owner
Fewer defects leaving the plant First-pass yield, scrap rate Plant quality manager Lower warranty spend Warranty cost per unit, claims per thousand vehicles Field quality and aftersales Faster containment Days from first signal to containment Quality engineering Better parts availability Dealer parts fill rate, back-order days Parts and logistics Stronger aftersales revenue Service retention, repair-order approval rate Aftersales and dealer network
Pick two or three of these per use case, then record their current values before any build starts. After that, report against the baseline every month, so the benefit shows up in the same numbers finance already uses.
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Set the Baseline for Your First Automotive Use Case
Kanerika helps you pick the KPI, record its current value and scope a pilot that can prove a result. A short working session turns a list of ideas into one measurable plan.
Schedule a Demo → Challenges: Data Quality, Silos, Privacy and Cybersecurity
The hardest problems in automotive analytics are data quality, ownership and the legal rules around vehicle data. Technology alone rarely fixes them, because each one starts with how people record and share information.
Many of them are familiar data governance challenges , only with a VIN attached. Each challenge still has a recognizable symptom and a known fix, as the table below shows.
Challenge Symptom Fix
Inconsistent keys The same VIN or part number appears in different formats Standardize keys at the source and in the silver layer Data silos Each team has its own version of warranty or quality numbers One semantic model with shared metric definitions Legacy plant systems Older MES and PLC data is hard to extract Historian connectors or edge gateways, loaded in batch first Privacy and consent Nobody is sure which telemetry may be used or shared Consent status stored with the VIN, purpose limits per use case Cybersecurity Connected systems open new attack paths Security engineering across the vehicle lifecycle and the data pipeline
Data silos deserve a closer look, since they cause the most friction. When quality and aftersales each calculate warranty cost differently, meetings turn into arguments about whose number is right. A shared semantic model ends that by giving every team the same definition.
Connected-Car Privacy in the US
US regulators have made it clear that connected-vehicle data is also consumer data. In January 2026, the Federal Trade Commission finalized its order against General Motors and OnStar over how they collected and sold driver data.
The order bans GM for five years from disclosing geolocation and driver behavior data to consumer reporting agencies. It also requires affirmative express consent before connected-vehicle data is collected, used or shared, with limited exceptions such as emergency responders. The order lasts 20 years.
For analytics teams, the design lesson is practical. Store consent status with the VIN, then check it before data enters any use case. Also keep a record of who received which data, and treat warranty analysis and insurance pricing as separate purposes.
A consent-gated view is one way to enforce that rule in the data layer. In the sketch below, telemetry reaches the warranty use case only for VINs with consent for that purpose.
-- Telemetry for warranty analysis, limited to VINs that consented to this purpose
CREATE OR REPLACE VIEW gold.telemetry_for_warranty AS
SELECT
t.vin,
t.event_time,
t.fault_code,
t.battery_soc
FROM silver.vehicle_telemetry AS t
JOIN silver.vin_consent AS c
ON c.vin = t.vin
AND c.purpose = 'warranty_analysis'
AND t.event_time >= c.granted_at
AND t.event_time < COALESCE(c.revoked_at, CURRENT_TIMESTAMP());Insurance pricing would then get its own view and its own purpose code, so one consent never covers both.
Cybersecurity Rules That Shape Vehicle Data
Vehicle data pipelines also sit inside a cybersecurity regime. According to TÜV Rheinland , UN Regulation No. 155 has applied to new vehicle types in the EU since July 2022. Since July 2024, all new M and N category vehicles need R155 compliance before they can be admitted to EU roads.
The engineering reference is ISO/SAE 21434 , the standard for cybersecurity engineering in road vehicles, published in August 2021.
For analytics, the practical effect is that telemetry and over-the-air data flows become part of the vehicle’s security case. Pipelines therefore need access control, encryption and monitoring that a security team can document and defend.
Case Study: 52% Fewer Configuration Errors in Connected Vehicle Data
A smart connectivity and mobility services provider runs a telemetry analytics platform that serves OEMs, EV makers, insurers and fleets. Its clients wanted vehicle data delivered in formats ready for analysis, on whichever system each one used.
The challenge was variety, because each client needed telemetry sent to a different destination. Those destinations spanned AWS, Google Cloud, Azure, Oracle Cloud, on-premises systems and streaming targets, while storage for each one was configured by hand.
Kanerika built data destination integration that delivered telemetry in analytics-ready formats across all of those targets. It also automated the storage configuration, and the published case study reports these results.
52% decrease in manual configuration errors
25% faster decision making
18% increase in operational efficiency
28% improvement in automation
The lesson carries over to any automotive data program. Clean, consistently delivered data comes before the analytics that depends on it.
How Kanerika Delivers Automotive Data Analytics
Kanerika works with automotive manufacturers, suppliers and mobility providers through five stages, and each stage ends with a clear deliverable.
Assess. A review of sources, keys and data quality, together with a scored list of candidate use cases.Integrate and model. Connectors for the chosen sources, as well as a lakehouse model keyed to VIN and part lot.Pilot. One use case on one line or region, measured against a recorded baseline.Production and governance. Monitoring, access control, consent handling and shared metric definitions.Scale. The next use cases from the scored list, which then reuse the same model and pipelines.
The platform and engineering side of this work is described on our data analytics services page . Industry detail is on our automotive solutions page , while AI-specific work, such as vision inspection and predictive models, is on the AI in automotive page .
Kanerika Service
Automotive Digital Transformation Consulting and Solutions
Kanerika works with automakers, suppliers and mobility providers to connect plant, supplier, telemetry, warranty and dealer data, then pilot and scale the use cases that pay back.
Explore Automotive Solutions What Comes Next: Software-Defined Vehicles and AI Agents on Vehicle Data
Software-defined vehicles move more of a car’s features into software that can be updated over the air. That turns each vehicle into a continuous data source, so analytics becomes part of product development, not a report at the end.
AI agents are the next layer on top of joined data. An agent could watch warranty and telemetry signals, open a quality investigation and then draft a containment plan for an engineer to approve. Plant-side examples of that pattern appear in our guide to agentic AI in manufacturing . The same pattern is already being tried with AI agents in the supply chain , and it fits the wider manufacturing industry trends toward connected plants.
Both depend on the groundwork in this guide, since an agent acts only as well as the data and permissions it is given. Those permissions come from an enterprise data governance model, so build it before the agents.
Where to Start With Automotive Data Analytics: The Bottom Line
The right starting point for automotive data analytics depends on who owns the problem, but the groundwork is the same for everyone. Join your data on VIN and part lot first, then score each use case on value and readiness.
An OEM or supplier should start with warranty and defect detection, measured by warranty cost per unit. A dealer group should start with service retention, tracked through repair-order approval rate. A fleet operator should start with vehicle health, measured by unplanned downtime.
Whichever buyer you are, prove that first use case against a baseline recorded before the pilot. Then scale the next one on the same model, because that is where automotive data analytics starts paying back.
Frequently Asked Questions
What is automotive data analytics?
Automotive data analytics is the practice of collecting data from across the vehicle lifecycle and turning it into decisions. It joins plant, supplier, vehicle telemetry, warranty and dealer data, usually on the VIN and the part lot number. Teams use the joined data to improve quality, lower warranty cost, plan parts and understand how customers use their vehicles.
Why is data analytics important in the automotive industry?
Vehicles now carry more software and connectivity, so they produce data from the plant all the way to the repair bay. Warranty and recall costs come straight out of the manufacturer’s margin. Analytics lets teams trace a field failure back to its plant, station and supplier lot, set recall scope by evidence and act before costs grow.
What are the biggest challenges in automotive data analytics?
The biggest challenges are inconsistent keys, data silos, legacy plant systems, privacy rules and cybersecurity. The same VIN or part number often appears in different formats across systems, and teams calculate the same metric differently. Older MES and PLC data is hard to extract, and connected-vehicle data needs consent controls and secure pipelines before analysts can use it.
How much does automotive data analytics cost, and how fast does it pay back?
Cost depends mostly on the number of data sources, the data quality work they need, streaming volume, platform capacity and the people involved. Payback depends on the use case chosen. A focused pilot on one line or region should be designed to show a measurable result within a quarter or two, against a baseline such as warranty cost per unit.
Which tools and platforms are used for automotive data analytics?
Many enterprise programs build on a cloud data platform such as Microsoft Fabric, Databricks or Snowflake. Fabric has Real-Time Intelligence with eventstreams and eventhouses, Databricks uses Spark Structured Streaming and Snowflake has Snowpipe Streaming. Industry tools for telematics, quality management and dealer operations feed these platforms, and BI tools such as Power BI handle the reporting.
How do connected-car privacy rules affect automotive analytics?
In January 2026 the FTC finalized an order requiring GM to get affirmative express consent before collecting, using or sharing connected-vehicle data. It also bans GM from sharing geolocation and driver behavior data with consumer reporting agencies for five years. Analytics teams should store consent status with each VIN, limit data to stated purposes and log every data share.
Can automotive analytics work with existing ERP, MES and dealer systems?
Yes. Most programs keep existing ERP, MES, warranty and dealer management systems in place and connect them through batch loads, change data capture or event streams. The data lands in a lakehouse where every record is keyed to the VIN and part lot. The source systems keep running while analytics reads from the joined copy of the data.
What are the four types of data analytics in automotive?
The four types are descriptive, diagnostic, predictive and prescriptive analytics. Descriptive shows what happened, such as warranty claims by model. Diagnostic explains why, for example by tracing a claim spike to one supplier lot. Predictive flags vehicles likely to fail from battery or brake signals. Prescriptive recommends an action, such as which VIN range to inspect first.
What types of data do automotive companies collect?
Automotive companies collect data in five domains. Plants record torque values, cycle times and test results. Supplier systems hold part lot numbers and inspection records. Connected vehicles send fault codes, battery state, location and usage. Warranty and service systems hold claims and repair orders, and dealers hold sales, service visit and customer survey data.
How is data analytics used in vehicle manufacturing?
In manufacturing, analytics links station readings, end-of-line test results and supplier lots to defects found in the plant or in the field. Engineers can see which station, shift and lot a defect shares with earlier ones, so they fix the process faster. The main metrics are first-pass yield, scrap rate and days to contain a defect.
What role does data analytics play in autonomous vehicles?
Autonomous and driver-assistance systems depend on large volumes of sensor data to detect objects and make driving decisions. Much of that processing happens on the vehicle itself, at the edge. Analytics in the cloud then studies fleet data to find unusual cases, track system performance across conditions and feed improvements into the next software update.
How do automotive companies use analytics for customer experience?
Automakers and dealers combine sales, service and connected-car records to understand how customers use their vehicles and when they need service. That supports timely maintenance reminders, clearer inspection reports and offers that match real usage. At the network level, analytics also shows which dealers keep their service customers and where parts shortages delay repairs for owners.
Can data analytics reduce warranty and vehicle costs?
Yes, mainly by catching defects earlier and narrowing their scope. Joining claims to build records and supplier lots shows which vehicles are actually at risk, so fewer cars are inspected or recalled without cause. Quality analytics also cuts scrap and rework inside the plant. Track warranty cost per unit against a recorded baseline to prove the saving.
What are the 5 V's of big data in connected vehicles?
The 5 V’s are volume, velocity, variety, veracity and value. In connected vehicles, volume comes from continuous sensor readings, velocity from streaming telemetry and variety from fault codes, location and battery data. Veracity means checking that keys like the VIN are accurate. Value is the decision the data improves, such as faster defect containment in the field.
How is data analytics used in EV battery management?
Battery analytics uses voltage, temperature, current and charge-cycle data from the battery management system to estimate each pack’s state of health. It flags cells that degrade faster than their peers, tests whether charging patterns relate to faster aging and informs battery warranty reserves. Manufacturers also link cell test data to field performance to find weak batches early.
