Cleaning Up Asset Intel for Smart City Appeal

For all cities and businesses, data is at the heart of strategic decision-making. It jump-starts success, providing footing for the insights that drive informed, tactical planning.

But these days, data — in large volume — comes at such a fast and furious pace, it can be hard to keep up. How can organizations get data to a place where it can be accessed, understood and used to redefine functionality?

“Data has infinite potential,” says Scott Parker, a project manager at Burns & McDonnell. “It can lead to smarter cities and more efficient facilities. But for many organizations — especially those with a massive number of assets to track — the sheer amount of information that comes in the door can make it difficult to get their data house in order.”

The right structure, systems and technology can provide the knowledge base that makes all the difference, opening the door for innovation and strategy to effect change — and then be captured in the bottom line.

“Everyone is looking for a better way to do business, whether you’re manufacturing a product or maintaining a city street,” says Brian Hiller, a project manager at Burns & McDonnell. Asset data can be the game-changer — once it’s gathered, organized and structured in a system that’s easy to access and use.

Why Big Data Makes a Big Difference

According to the IBM Systems Journal, digital data storage became more cost-effective than paper in 1996. Big data has become a driving force in business — and it shows no signs of slowing. To gain a competitive edge, organizations are investing more than ever in business intelligence. Global spending on big data hardware could grow at a compound annual rate of 30 percent through 2018, according to research from A.T. Kearney.

Many companies are relying on big data to provide a competitive edge, using it to drive almost every aspect of business strategy, from marketing to operations. Others, especially in the civic and manufacturing spaces, also are relying on the consistency smart data provides to fill the knowledge void left by retiring senior staff. Across the board, organizations are using smart data to inform decision-making, justify capital expenditures and, in general, do more with less.

“Using staff to manually collect information about transformers is no longer feasible for many utilities because of reduction in field staff,” says Chrissy Carr, a project director at Burns & McDonnell. “Yet the responsibility to maintain reliability hasn’t diminished. By relying on data and predictive analytics to anticipate and prevent failures, utilities can meet their customers’ expectations at reduced operational costs.”

Quality In, Quality Out

Organizations have access to more information and more ways of collecting it than ever. While some cities and companies have an excellent grasp of assets and processes, others are just beginning.

Hiller encourages clients to consider what information they want to get out of a system before they structure how data goes into it. A firm plan for how data will be used — for maintenance analysis or budget planning, for instance — will drive strategy for the collection of quality information.

A baseline assessment can help organizations define what assets are owned, what condition they are in and what it costs to maintain them. Data gathering — typically into an asset management software system — can happen through historical inventories, manual collection with mobile tools or even sensors that continually send information through a secure, wireless connection to the cloud. Once data is in place, it can begin delivering on its promise.

“The end goal is to help clients get the most value out of the assets they’ve put into service,” Hiller says. That could mean analyzing a wastewater pump’s performance to anticipate and prevent failure. Or it could be finding a more efficient way to air-condition a building, water a city park or optimize driving routes to save fuel.

Data Collection with a Double-Duty Tool

The ability to make data-driven strategic decisions relies on the availability of clean data, which is often challenging to collect and organize. But for new building or renovation projects, that data can be hidden in plain sight — in a building information model (BIM) that architects develop during the design process.

“The tools we use to design buildings are heavily data-oriented,” says Vicky Borchers, a senior architect at Burns & McDonnell. “BIM drawings are visual tools, but behind the scenes is a rich store of data that drives the graphics.”

Borchers and her colleagues are using BIM models in an innovative way by pivoting the data within the models to be useful to building operators long after the project is completed. They structure that data to migrate to a computerized maintenance management system, resulting in an incredibly detailed picture of a building’s systems.

For building owners and operators, that level of information means more informed and strategic decisions on how to run and maintain a facility. For instance, the performance of a crucial asset could be closely monitored to understand the optimal time for replacement instead of waiting for it to fail and experiencing a long lead time for a part to arrive.

“These BIM drawings are simply data visualized — and they can do double duty,” Borchers says. “It’s a better way of helping operators take ownership of data once the building is complete.”

Making Sense of 1.5 Million Square Feet Worth of Data

Architects and engineers have long been required to submit documentation, but the format can vary. It can be frustratingly old school — scanned PDFs in dozens of physical binders. Or it can be structured in a way that makes sense to the builder but not the building’s eventual operator. 

Many companies across the country face these types of data collection problems. For instance, when 1.5 million square feet of manufacturing capacity was completed for a multinational company, building operators received 3 terabytes of documentation data. The next step was to understand what the data was to be used for and train the operator on implementation for smooth facility operation.

“Owner participation is key to making a BIM model work for asset management data collection,” Borchers says. “We needed to know how they planned to use the data so we could understand how to structure it.” 

The team met with dozens of stakeholder groups, then organized the data by building, system and discipline to create a building management guide.

The BIM model also was aggregated into the facility’s computerized maintenance management system (CMMS), an action that saved hundreds of thousands of dollars over typical collection methods.
It costs $150-$200 per asset to input data into a CMMS — and this facility has nearly 10,000 assets. Using the restructured BIM model, the team delivered a database, saving time and money.

Smart Cities Through Data Analytics

A smart city isn’t just one with fast internet, it’s one that can rely on data analytics to run efficiently and optimally. An increased knowledge base means smarter decisions on how to prioritize public funding, an especially useful tool for managing aging infrastructure. Parker, who spent 15 years in city government before joining Burns & McDonnell, saw firsthand the positive impact asset management has on communities. 

“The real art is how to take hundreds of thousands of data points and make them actionable and scalable,” he says. “Technology is glamorous, but it doesn’t work without a strategic framework for the data. What’s the philosophy, the methodology? What makes it applicable for the context you are working in?”

In Sioux Falls, South Dakota, that meant uniting city departments — previously using several separate software systems — into a single coordinated enterprise asset management (EAM) platform. It also meant capitalizing on a knowledge of asset needs and life cycles to help build EAM into a business strategy, helping the city increase operational efficiency.

In Kansas City, Missouri, data collection and analysis helped maximize public investment in a massive overflow control program. After a report showed half the rainwater entering the sewer system came from improperly connected private plumbing connections, geographic information system (GIS) data was used to pinpoint 50,000 properties where cost-effective plumbing corrections could make a substantial difference. (Read about the benefits and efficiency of mobile technology.)

Property evaluation teams used digital forms directly integrated into the asset management system, enabling effective data sharing. Organizers even used data analytics to track the right method of public outreach. Based on the data provided by sensors installed in the sewer system, the program is expected to save millions of dollars in future capital improvements.

Predictive Problem-Solving

Reliability is paramount in the electric utility world. But, sometimes a transformer is run to failure — and once one fails, it can take up to a year to get a new one of the right class and size. There’s a better path, and it’s paved with data analytics. 

“Most utilities only have the capacity to do a dissolved gas analysis (DGA) once a year, and the results might go into a basic spreadsheet,” Carr says. But if utilities can collect DGA information remotely and review performance over time, they can make proactive decisions about replacement.

Predictive analysis is especially valuable for fiber optics because cable age isn’t always a determinant for failure. Duke Energy, Southern Company and Burns & McDonnell are researching the health of fiber-optic cable, including the review of cable performance over time for indicators of deterioration, with support from an asset health center that’s collecting and analyzing the data. Additional analysis will come from an algorithm that Carr and teammates are currently in the process of building to help determine the health of the fiber-optic cable.

Preparing for the Ongoing Data Revolution

Companies are facing profitability expectations. Cities have smaller budgets to meet ever-changing needs of the citizens. Data can be the difference maker.

“Every decision can be backed up with numbers, not anecdotal information,” Hiller says. “Managers can apply their instincts to data analysis. A strong asset data structure makes organizations stronger, more adaptable and more confident in their strategic directions.”

Big data offers enormous potential for every city and business — to make smarter decisions, increase efficiency, reduce costs, drive proactive innovations, improve sustainability and continually enhance the customer experience.

How a BIM Model Drives Smarter Operational Decisions

During design, architects and engineers detail measurements, location, specifications and connected systems in the BIM model.

The data is pivoted to meet the specific information requirements of building operators and synced with the maintenance management system.

When service is required, the maintenance department can click into the BIM model to see a graphic interface of the part, when it was last serviced, view work orders and even download the unit’s manual.

5 Steps for Getting Your Data to an Actionable Place

1: After assessing your assets, select or develop asset management and maintenance software to house your data.

2: Develop a plan to catalog your assets, including an asset hierarchy or registry.

3: Compile a data structure by defining what you want to get out of the system.

4: Collect, analyze and prioritize information using mobile tools, historical records and BIM models.

5: Incorporate data-driven lessons into decision-making.

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Chrissy Carr Client Strategy Director 816-822-3417
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