ESG & NetZero:Net Zero
Urban Data Science
Climate Change Threatens Today’s
Cities
Advance the energy transition for a lower carbon future with lower risk and greater impact.
Cities are among humanity’s most magnificent creations, but the world’s cities are currently under severe threat by climate change. Major floods, storms, droughts, and fires are becoming increasingly common and deadly. To respond effectively to climate change, cities must mitigate and adapt in the short term and become Net Zero GHG producers in the medium term. Both objectives will require new policies, behaviours, and regulations across a wide range of urban management issues.
But what are the right policies?
To determine what’s best, we need to collect reliable data from a broad spectrum of agencies and firms and across the urban landscape. Then, we must perform rigorous analytics to understand what the data means. At present, however, the necessary data are incompatible, fragmented, and mostly inaccessible. Net Zero Data Science has been created to help solve exactly this problem.
Urban Twin
Combining the construction industry with technology has yielded the “Digital Twin,” a build represented as interacting systems in a computer simulation.
The same approach can now be applied at a much larger scale to the city and its region in order to facilitate the accelerated transition to Net Zero. This is the Urban Twin™, our solution to urban-scale data and analytics.
The Urban Twin is a platform to collect, organize, and analyze massive amounts of hard data that come from hundreds of different sources. In the past, they were stored in different and divergent systems that were proprietary, fragmented, siloed, and incompatible. Bringing them all together was simply impossible…until now.
This is precisely why The Modern Data Company has launched Net Zero Urban Data Science. Our data operating system, DataOS, is fully capable of supporting this massive task. In fact, it’s the type of challenge that DataOS was designed for.
Users
Often people tell us, “We have the data. We just don’t know what to do with it.” With the Urban Twin platform, you can easily collect and organize the data, and begin mining it for valuable insights.
Developers
- Build a portfolio-level view of the performance of all your building assets.
- Aggregate data across all properties.
- Gain insight into energy performance to facilitate effective investments.
Developers
- Build a portfolio-level view of the performance of all your building assets.
- Aggregate data across all properties.
- Gain insight into energy performance to facilitate effective investments.
Developers
- Build a portfolio-level view of the performance of all your building assets.
- Aggregate data across all properties.
- Gain insight into energy performance to facilitate effective investments.
Net Zero Data Value Stack
Value increases as you move from left to right. You can only succeed with reliable data on the left.
- Government
- Private sector
- Consumer
- Scientific
- Community
- Trend
- Government
- Private sector
- Consumer
- Scientific
- Community
- Trend
- Government
- Private sector
- Consumer
- Scientific
- Community
- Trend
- Government
- Private sector
- Consumer
- Scientific
- Community
- Trend
- Government
- Private sector
- Consumer
- Scientific
- Community
- Trend
- Government
- Private sector
- Consumer
- Scientific
- Community
- Trend
The DataOS Platform
Often people tell us, “We have the data. We just don’t know what to do with it.” With the Urban Twin platform, you can easily collect and organize the data, and begin mining it for valuable insights.
Original Thinking About Data Management
DataOS is an integrated data management platform built with a best-of-class, cloud-agnostic data fabric architecture. It delivers real-time, high-quality, trustworthy, and actionable data, which enables the creation of a robust data supply chain capable of creating significant value from data assets. It frees data for use by, and to create value for, anyone and everyone across the city and its region.
DataOS was designed from the outset to handle datasets of massive scale and to free analysts from the tedious work of wrangling the data, enabling them instead to focus on delving into it for insights.
City Leaders
- A cloud-based platform for the collection and integration of data on the massive scale of an entire city or region.
- Seamless data sharing.
- The capacity to automatically ingest, assess, and cleanse huge data sets from diverse sources.
- Ready-made tools for comparison, visualization, analysis, modeling, and presentation to support policy makers and leaders in finding solutions to intractable problems.
- Advanced analysis capabilities are built in, including AI and ML.
- Real-time functionality eliminates lags.
- Full integration with analytics tools such as Tableau, R, and Python.
Frequently Asked
Questions
This term describes a situation where data is trapped in silos or systems and cannot be easily shared, analyzed, or integrated with other data sources. Data Prisons can occur because of a lack of data interoperability, data governance, or a failure to implement data management best practices–many times a combination of all three. As a result of these limitations, organizations cannot extract meaningful insights from their data, leading to inefficiencies such as duplicated efforts, lost data, and decreased trust in data quality. A data prison can also result in missed business oportunities and a failure to predict and react to disruption.
This term describes a situation where data is trapped in silos or systems and cannot be easily shared, analyzed, or integrated with other data sources. Data Prisons can occur because of a lack of data interoperability, data governance, or a failure to implement data management best practices–many times a combination of all three. As a result of these limitations, organizations cannot extract meaningful insights from their data, leading to inefficiencies such as duplicated efforts, lost data, and decreased trust in data quality. A data prison can also result in missed business oportunities and a failure to predict and react to disruption.
This term describes a situation where data is trapped in silos or systems and cannot be easily shared, analyzed, or integrated with other data sources. Data Prisons can occur because of a lack of data interoperability, data governance, or a failure to implement data management best practices–many times a combination of all three. As a result of these limitations, organizations cannot extract meaningful insights from their data, leading to inefficiencies such as duplicated efforts, lost data, and decreased trust in data quality. A data prison can also result in missed business oportunities and a failure to predict and react to disruption.
This term describes a situation where data is trapped in silos or systems and cannot be easily shared, analyzed, or integrated with other data sources. Data Prisons can occur because of a lack of data interoperability, data governance, or a failure to implement data management best practices–many times a combination of all three. As a result of these limitations, organizations cannot extract meaningful insights from their data, leading to inefficiencies such as duplicated efforts, lost data, and decreased trust in data quality. A data prison can also result in missed business oportunities and a failure to predict and react to disruption.
This term describes a situation where data is trapped in silos or systems and cannot be easily shared, analyzed, or integrated with other data sources. Data Prisons can occur because of a lack of data interoperability, data governance, or a failure to implement data management best practices–many times a combination of all three. As a result of these limitations, organizations cannot extract meaningful insights from their data, leading to inefficiencies such as duplicated efforts, lost data, and decreased trust in data quality. A data prison can also result in missed business oportunities and a failure to predict and react to disruption.
This term describes a situation where data is trapped in silos or systems and cannot be easily shared, analyzed, or integrated with other data sources. Data Prisons can occur because of a lack of data interoperability, data governance, or a failure to implement data management best practices–many times a combination of all three. As a result of these limitations, organizations cannot extract meaningful insights from their data, leading to inefficiencies such as duplicated efforts, lost data, and decreased trust in data quality. A data prison can also result in missed business oportunities and a failure to predict and react to disruption.