Trust the Data Your Products Generate
Your connected products already generate data. The challenge is turning that data into reliable intelligence your teams, customers, analytics tools, and AI applications can use.
Twisthink designs the device and data systems that turn raw device signals into reliable, contextual information so your customers, teams, and AI systems can use it.
The Challenge
Your connected products generate data, but you cannot trust or use it.
Many companies have years of IoT, telematics, or field data, but the information is spread across disconnected systems, is incomplete, inconsistent, or difficult to access. That creates problems when teams try to build analytics or AI applications on top of it.
Common challenges include:
Data gaps, duplication, and inconsistent formats
Separate platforms or silos limit access to necessary data
Ensuring the right level of security and data access across a wide user base
Better decisions begin with better data.
Turn product and field data into trusted intelligence.
Twisthink helps companies create a reliable data foundation for analytics, AI, and better decision-making.
We begin by understanding the business objective, the decisions the data needs to support, and the workflows it needs to improve. From there, we identify the right data, address quality and access issues, and build the devices or data platforms needed to make it useful.
The goal is not simply to collect or centralize more data.
It is to deliver the right information, with the right context, to the right people and their AI tools.
The Path to
Trusted Product Data
A trusted product data solution connects information across products, platforms, and business systems and prepares it for real-world use.
- Understand the objective
- Assess the data
- Connect the sources
- Improve quality and context
- Build a secure data platform
- Deliver actionable insights
Define the business questions, user needs, and workflows the data must support.
Identify what data exists, where it lives, what is missing, and what needs to improve.
Bring together device data, third-party telematics platforms, enterprise systems, and other relevant sources.
Clean, organize, validate, and enrich the data so it can be interpreted consistently.
Create cloud-based infrastructure that manages data access, storage, processing, and governance as the solution scales.
Build analytics, dashboards, alerts, or AI-powered applications that turn the data into useful action.
Our Expertise:
- Data Strategy and Consulting:
Define the data, workflows, and technical foundation needed to support business objectives. - Product and Field Data Assessment:
Evaluate data quality, availability, consistency, and context across connected products and systems. - Cloud Data Platforms:
Build secure, scalable platforms for collecting, processing, storing, and managing IoT data. - Telematics Integrations:
Connect and normalize data from third-party telematics platforms and other external sources. - Enterprise Data Integrations:
Integrate product data with ERP, CRM, service, maintenance, and other business systems. - Data Engineering:
Clean, transform, organize, and enrich raw data so it can support analytics and AI. - AI-Powered Applications:
Build applications that use product and field data to automate tasks, identify patterns, and improve decisions. - User Experience Design:
Turn complex information into clear tools, dashboards, and workflows people can use.
Vermeer telematics platform drives greater efficiency and productivity
Vermeer partnered with Twisthink to transform equipment data into a user-centered telematics platform that helps dealers and equipment owners make better decisions. By researching how people actually use machine data, then designing the cloud architecture, user experience, and web application together, Twisthink turned complex telematics data into clear, actionable insights.
Build AI and analytics on data you can trust.
Frequently Asked Questions
FAQs
Q: What does it mean for IoT data to be trusted?
A: Trusted IoT data is accurate enough for the intended use, available when it is needed, and traceable back to its source. Users need confidence that the data represents actual conditions in the field and that they understand where it came from.
The required level of trust depends on the application. In one recent project, users only needed a coarse indication of equipment temperature, so accuracy within roughly five degrees was acceptable and allowed for a lower-cost sensor. That same level of accuracy in a smart thermostat would quickly cause users to question the data and the product.
Q: Why do I need to spend time cleaning my data? Can’t AI just handle it?
A: AI is only as useful as the data it receives. If the underlying data is inaccurate, incomplete, inconsistent, or missing important context, AI cannot reliably determine what happened in the real world. That can lead to misleading analytics, inaccurate chatbot responses, or incorrect actions from an AI agent.
AI can still be a powerful tool in the data-cleaning process. It can help normalize data from different sources, evaluate large datasets, identify anomalies, and flag records that need attention. The key is to use AI as part of a deliberate data-quality process, not as a substitute for one.
Q: How can I tell whether poor data quality is causing problems with analytics or AI?
A: A common sign is that users begin questioning the results. They may see inconsistent insights, obvious errors, missing information, or answers that arrive too late to be useful.
In one project, telematics data showed fuel levels increasing even though the equipment had not been refueled. The issue originated in the device and firmware but appeared to users as an unreliable dashboard. In another project, building an equipment-support chatbot exposed fragmented datasets, missing information, and inconsistent parts data. In both cases, the problems became clear when the available data could not support the experience users expected.
Q: How do you prevent AI systems from exposing device data to unauthorized users?
A: Access control should be enforced by the data platform, not left to the AI model. The system needs to establish who the user is, determine what data that person is authorized to access, and apply those permissions before any information is provided to the AI.
Relying on the model itself to enforce data boundaries creates unnecessary risk, including the possibility of information being exposed through prompt injection. In a recent project, we integrated the platform with the customer’s single sign-on system and filtered every data request based on the user’s identity and permissions. This allowed internal employees, dealers, and end customers to use the same AI application while seeing only the data available to them.
Q: How do you prepare connected product data for analytics and AI?
A: Preparing connected-product data starts with knowing which asset generated each record and preserving the context needed to interpret it. That may include the asset type, device configuration, firmware version, units of measurement, location, and operating conditions.
We also evaluate whether the data is complete and reliable. Are there gaps, and do those gaps reflect normal product behavior or a problem with the device or connection? Timestamps must remain accurate even when data arrives late or out of order. From there, data-processing pipelines can validate and standardize the information into common formats, which is especially important when working across a mixed fleet of assets.
