Data Infrastructure for IoT Company Launches, Powering the API Economy

Data Infrastructure for IoT Company Launches, Powering the API Economy

AI-driven data infrastructure platform unlocks innovation at scale for commercial and industrial IoT environments.

Mapped, an AI-powered data infrastructure platform for commercial and industrial IoT, has launched and promises to change how data discovery, extraction, mapping, and enrichment happens in commercial and industrial IoT environments.

Mapped automatically discovers all disparate devices in commercial and industrial environments and map them into a simple ontology to help digitisation teams, app developers, systems engineers, and subject matter experts instantly access normalised live data. Mapped further simplifies the experience by providing one simple, secure, and reliable API.

Founded by Shaun Cooley, formerly vice president and chief technology officer of IoT at Cisco and a former Symantec distinguished engineer is joined by key executives from Cisco, Twilio, and Qualcomm. The company has raised USD 3 million in seed funding from Greycroft, ANIMO Ventures and is backed by several industry leaders. Mapped has several early partners, including Fortune 100 technology giants, one of the world’s largest retailers, leading commercial real estate owners/operators, and a commercial manufacturer.

“We invested in Mapped because we believe in the team’s approach to solving the data infrastructure problem across all commercial and industrial environments. The company’s product leverages machine learning to automate data discovery, extraction, and mapping to deliver a true data infrastructure for every building, globally.

The team’s deep expertise in IoT, security, and cloud technologies make them well equipped to bring this disruptive technology to market and their focus on open source and developer-focused APIs will enable the next wave of software development in this space,” said Brentt Baltimore, principal, Greycroft, a Los Angeles-based venture capital firm.

“I have seen even the largest of Fortune 100 companies struggle to digitise their existing automation environments or obtain data in a repeatable and scalable form. Most commercial and industrial building systems and components use legacy protocols that are complicated, unstructured, and often vendor and install-specific,” said Cooley, founder and CEO, Mapped.

“Energy efficiency, space optimisation, maintaining a healthy environment, predictive maintenance, workforce planning, and even insurance in commercial real estate all depend on data, but enterprises struggle to obtain and use data at scale. Our technology simplifies data sharing across various systems and locations in a secure and reliable manner. Additionally, Mapped helps alleviate anxiety around COVID-19 safety, as data can be used in real-time to inform around personal distance adherence, filtration needs, and building capacity.”

Unlocking Innovation at Scale

By automating the tedious task of data discovery, normalization, and enrichment, Mapped empowers IT teams, OT teams, operations and supervisory teams to share data across systems or locations to derive valuable insights and bring about new innovations. The Mapped platform and its single API allows for innovation at scale.

“We help our customers by abstracting the discovery and integration details so they can spend time developing their ideas and initiatives to drive innovation, ” Cooley continued. “There is an enormous amount of pressure on IT and IoT teams to digitize their environments, but historical complexities often result in delayed projects and cost overruns for customers. We believe Mapped eliminates that friction, allowing our customers to focus on innovation rather than integration.”

AI-Powered Data Infrastructure

Leap-frogging the many nuances of existing data ecosystems, Mapped’s technology allows for ease in discovering all automation, sensor, control, or IoT devices on a network by monitoring data traffic and any related activities. Data is then extracted from the devices and moved to the cloud where machine-learning takes over to normalize, structure, and enrich the data.

From experiencing an ease of wrangling disparate data from multiple on-premises and cloud sources, to knowing that all data sources are available in a secure and consistent manner, regardless of source, protocol, or location, Mapped gives customers the data infrastructure needed to organize, analyze, and react to incoming and outgoing information across multiple systems in any one environment.

“Internet of Things is first and foremost a data integration problem. Many IoT initiatives face implementation and operational challenges, caused by lack of understanding of the various data sources and bespoke approaches to data integration,” said Matt Eastwood, senior vice president, Enterprise Infrastructure, Cloud, Developers and Alliances, IDC. “With an AI-powered commercial data platform like Mapped, developers can shift away from performing discovery and integration, and instead focus on application development and outcomes.”

API-Based Digital Transformation

With a single API and a robust developer-friendly data ontology, software developers are free to focus on building applications rather than the tedium of integration. All without ever having to set foot in the physical buildings or read manufacturer documentation for legacy systems, irrespective of make, model, or protocol.

“With the radical disruptive changes occurring in both commercial and industrial business operations — many of which have been accelerated by the pandemic — the marketplace is seeking API’s that offer digital transformation solutions. There is a strong need for API’s that both enable the discovery of a multitude of known and unknown installed assets from various generations of technology, and optimise the data extracted from those assets to the fullest extent possible, ” said Craig Resnick, vice president, ARC Advisory Group. “Mapped provides its users with an AI-driven data infrastructure platform that can save the user months of asset discovery time, bringing together critical data across an organisation, easing the burden that historical system complexities can bring, and maximising operational profitability.”