Construction: The Data Operator
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The Data Operator
Many different stakeholders can benefit if information about constructed buildings is easily available. Today that information is mostly held on paper or by each player in their own ERP systems. The biggest benefits are gained when information is not kept in separate silos but is available to empower different uses broadly.
Many users have need for same or partially overlapping data such as:
Owner of the apartment or building
Persons living in the apartment
Caretaker company
Field force of different repair companies (lift, HVAC, utilities like power company, …)
Manufacturers of different equipment like HVAC, heat exchangers, elevators, central TV-tuners, water and electric meters etc. (to be frank, these companies often have quite limited needs compared to other stakeholders)
Municipality
State (office of statistics)
Through the lifecycle of the building all types of potentially useful data items are generated by various parties such as:
Zoning rules and changes to them
All companies participating to the construction of the building and their roles
Building Information Model with original drawings of the building and planned building materials and structural data (building, ventilation, electrical and communications wiring, …)
Used materials and parts and their lot numbers. Manufacturer and place of purchase.
Exact colour codes for paints used (helps residents later to order right stuff)
Major components like lifts, heat exchangers installed with details
All construction documentation with images, drone videos etc.
Environmental data during construction (temperature, humidity, rain etc.)
Inspection reports from municipality in different phases
Connection to municipal and public services like water works, electricity, fiber etc.
Final acceptance report
Contracts with maintenance companies (housing manager, care taker, waste disposal, insurances, water, …)
Bank data (collaterals etc.)
Resident reclamations during lifetime
Warranty repairs
Future repair plans
Maintenance repairs and major changes during lifetime from building and from apartments (roof, repaint exterior, balcony repairs, new windows, lift repairs, changed equipment like heat exchanger).
Environmental data from sensors inside the building (like humidity in structures) and from the outside (temp, humidity, air quality, lightning, noise,..)
Initial software in all components and updates
Metering data (water, electricity)
Data from electric car charging docks
Data from solar cells, solar heat collectors or wind turbines if such installed
Changes of ownership, prices
Tenant data (rented out spaces at ground floor for shops, entrepreneurs)
Deconstruction documentation
Recycling documentation
Energy classification
Housing association bylaws
Other information such as parking space occupancy, smart lock records, sauna and other public area reservations etc.
Public information such as weather predictions and actual weather data, electricity prices, general apartment price level in area
City plans for the area (major constructions raising or lowering price)
Map data like services in the area (schools, supermarkets, transport like metro or train station nearby, bus travel times to city center, green areas, other points of interest).
This big data associated with the building can be used to create a wide variety of services of which the fleet learning services are some of the most important.
The dynamics of construction data operator are different compared to for example social media companies. In social media the data buyers are advertisers and the data come from users – it’s a linear model. In this scenario the data providers and data consumers are the same bunch for most. Only additions that one can think of are universities and some startups that might buy into the data. But in general, this is a community that wins big time by sharing data with each other. This is true in general of all industrial data operators.
Building Information Model
BIM contains the drawings of the building in 3D with additional information about the structure and parts. With BIM data it is possible to do cost accounting (calculate total cost of production with variable costs at each step as well fixed costs), calculate time tables for construction, energy needs parts and the whole building and simulations. BIM helps automating these calculations.
When changes are done to the drawings of the building (and hence to BIM), these changes affect automatically all derived simulations and cost calculations.
Collecting data from sensors over the entire lifetime of the fleet of buildings helps to improve these calculations. This means that the planning process also gets better with time and following generations of the modular building are better than previous one.
Site Solutions
Site solutions are used during the physical construction that serve temporary need but also contribute a lot to the building data.
They include functionalities like
Scanning goods as they come in and updating used materials to databases
Drones that can document the building process with arial images by regularly flying automatically over the site
RFID based safety solutions that ensure people wear hard-hats in dangerous areas, track automatically working hours, prevent overcharging, ensure that every employee hour is correctly recorded and prevent accidents by requiring that people enter in pairs especially hazardous areas.
Sensors and drones to monitor unwanted people on site during off-hours. In typical scenario an alarm is raised, drone flies automatically to the spot to detect that it was an animal or other false alarm (or rarely a real incident).
Asset tracking is another generic use case. Vendors renting equipment to sites have their own systems. Expensive gear like cranes, welding machines etc. can be equipped with location tracking devices. Tracking normally is used to set geo-fences. When the device is rented out, the expected location is programmed in.
When the device arrives to the expected location, the tracking starts. If the device leaves the geo-fence the device starts sending its location information to the location server. This allows to find the asset and get it back even when it would have been stolen or borrowed without permission.
API
A standardized interface is needed to make the data available for users. At simplest there can be a REST interface to ask for various information regarding the apartment, house, or city part. Access is granted on requestor identity and what roles are associated with it.
A data operator is likely to offer multitude of interfaces ranging from running analytics in a sandbox environment to real time data feeds based on what the market needs are and how the data owners see the benefits vs. risks.
As example the REST interface could look like this:
Services
What additional services can be built on top of such a data operator? Let’s list a few examples.
Real estate agencies are interested in predictive models for apartment prices and what features affect it the most. Typical features for such a model are type of building (block of flats, detached), size, floor (first, top, ...), bathrooms, year built, past and planned repairs, own lot or rented, rent of land, condition, facilities around like schools, materialized sale prices, etc.
Predictive models for house owners on current price and return of investment for different renovation alternatives.
Virtual reality tools can help to visualise the unbuilt houses to potential buyers or zoning administrators in city etc. VR tools can also show to customers in real time how the personalisation they are thinking of will affect the final interior. Tools will allow customers to place their existing furniture into the VR environment.
As mentioned earlier, for new residents the data can automate generating a small orientation booklet or an app telling exactly where is the nearby drug store, school, public transport, name of the caretaking company with phone number, what is responsibility of tenant and what the house association will cover, what upgrades and changes the previous owners have made, where are the electric meters, where to cut water when there are repairs etc. Very convenient also for people who rent out their apartment for short periods.
For renovation the data base contains exactly the right color codes for paints and sizing for various home appliances. Often hardware store businesses are hindered by the fact that customers do not know exactly what they need to order. This prevents or delays ordering and sometimes wrong stuff gets ordered leading to disappointments and hassles. As an improvement hardware store chains can implement apps for visualizing what the change or upgrade means with augmented reality apps. Soon machine learning model based tools can generate on infinite variety of styles and choices for potential buyers using whatever stuff the seller have in their inventory.
For the house owners and care taker company, the data can be used to build predictive models for estimating when big ticket items like heat exchanger needs replacement. If there are sensors on infrastructure, predictions can be very accurate. Detecting faulty sensors is another use case where data from multiple buildings is essential.
The care taker company can immediately see all relevant house data. Information is never lost and there is no need to keep searching for it in manual records. If building has new technology like smart locks in public areas the care taker personnel can gain access whenever there is need on a scheduled visit. Keys are never lost. If an employee leaves the company or is on leave, they cannot enter.
Data also allows following the performance of various repair companies and this can be shared across. Who really has the skills and who not, how accurate are quoted prices and schedules etc.?
Performance becomes also visible on individual component level. What is the average mean time between repairs of elements like lifts, heat exchangers, tuners?
If bigger structural changes are planned like an extra floor or changing from a flat roof to a gabled roof or add parking spaces - how long do the city zoning decisions take, have these types of proposals in general been accepted, what type of modifications tend to be asked for, how long do these types of projects last, what is the price level? How do these types of changes affect the apartment prices? If there are negative decisions, an automated model can read through it and pick up parts of the decision logic that can be contested and make an automated or semi-automated appeal to the zoning authority.
On the municipality side the data can be used to understand environmental factors. Are house prices on noisy neighbourhoods cheaper and do inhabitants have more health problems? (would require access to health data though so often a no-go). Effect of green park nearby to prices. If a noise wall is built along a big road, what does it cost, what is the return on investment in terms of higher apartment prices and lower health costs. How much do various zoning regulations like complex facades, minimum number of parking spaces increase apartment prices and rents? For the first time it becomes possible to quantify these issues and calculate exactly what is the real cost/benefit for bad/good planning. This allows city planning to make better plans and to have fact-based arguments for their proposals.
Similarly, for the municipality the building data tells how many people live in certain area and how they move about in the city. This they can use to estimate need for services like schools, public transport, health centers etc. Not estimates, but aggregate information on real need that may vary between time of the year. This is important in unexpected cases. It becomes possible to predict traffic and impact of additional bus routes if metro station goes under repair.
The data helps also maintenance teams when augmented reality data have access to it. For example:
Maintenance crew with modest skill levels can make fairly complex operations requiring specialist knowledge when aided by cloud driven augmented reality (AR) applications. There is less need to train and renew trainings for field engineers as the instructions are streamed in real time when the need arises.
Some maintenance tasks could be done by people on the premises themselves with the help of AR applications. This is beneficial for properties that are remote or otherwise hard to reach. Self-maintenance also allows to act immediately when something critical like large water leaks are detected.
Startups can focus on building focused model for specific problem areas and customer segments which however may have large global markets allowing to scale.
On risk side, the data operator data also has potential privacy issues. If for example indoor air quality is measured, the carbon dioxide levels tell if the house is empty during holiday season and CO2 from master bedroom tells what types of activities are performed there, how often and how many people are engaged. Loose-lipped walls may not be to everyone’s liking. Some data needs to be deleted or highly aggregated when ownership changes.
Next week we’ll end the mini-series on construction by looking at alternatives to modular designs.