Advancing Construction Analytics 2022: Takeaways from the data conference
At the fourth annual Advancing Construction Analytics Conference last month, Director— Continuous Improvement and Benchmark Services Amy Jones, Vice President—Preconstruction Will Senner and Director—Data Management Analytics Joe Dib represented Skanska and brought their expertise in data analytics to attendees. Here, we share several key takeaways from their sessions.
Driving User Engagement for Sophisticated Analytics Applications
Digital construction tools are incredibly powerful from a technical standpoint, but often come with a steep learning curve. Understanding how to effectively utilize these platforms naturally broadens user engagement with them and creates space for more productivity and innovative problem solving.
In this session, Amy and Will shared several lessons learned from developing Skanska Metriks® and increasing its adoption and use within the company.
- To successfully drive user engagement with analytics applications, you must approach the application’s design by focusing on visualizations that support a user’s ability to gain insights.
- When training users on new tools, develop materials that outline how the tool helps with specific processes. Customized learning tools increase adoption more effectively than generic ones.
- The importance of data literacy can’t be overstated. Training should reinforce general data literacy skills necessary for working effectively with and communicating through data.
- The lifecycle and long-term sustainability of an analytics tool hinges on the degree to which the application’s owner is committed to a mindset of continuous improvement. Another key to success? Providing user support beyond the rollout phase.
“A big challenge that we run into with analytics tools is that when you roll something out, people won’t necessarily just start using it right away. It’s not a case of, ‘if you build it, they will come,’” explains Will. “It takes very thoughtful planning in how the analytics tool is developed along with strategic, ongoing training.”
Will emphasizes that training should focus on how users are expected to apply the analytics to a real business process and extract data-informed insights which they can blend with their own professional expertise to reach better decisions or more efficient solutions.
“It’s essential to always be improving the tools we build. I have said many times that it is not a once and done,” says Amy. “There needs to be a framework and team put in place to continue to support the development and improvement of the product to take it to the next level – incrementally and in a transformative way.”
Overcoming Technical Roadblocks to Maintain a Data Lake and Data Warehouse Simultaneously
Sometimes the only thing keeping someone from making the most of a data solution is a technical roadblock.
In this session, Joe explained the technical infrastructure needed to build a data analytics program, covering architecture diagrams and patterns, as well as challenges and advantages of setting up a data lake and data warehouse correctly from the outset.
- Technical architecture matters but business value is more important. Start building dashboards manually while you build system infrastructures. Automate processes wherever possible to save time and bring added efficiency.
- Data management is a messy business. There are a lot of basic technical principles when it comes to data management, but it also takes creativity to solve data problems and drive long term solutions.
- Outsourcing is not always the best idea unless you’re looking for specific technical expertise or temporary extra “boots on the ground.” Think strategically about where you might need a third party to support your system building efforts.
According to Joe, “The construction industry is one of the last giant industries ‘waking up’ to the data and analytics transformations.”
“It’s easy to get lost in technology,” he continues. “Balancing a sound investment in technical infrastructure with return on investment as well as data standards and governance is key for the success of any data analytics program.”