DATA & DIGITAL INNOVATION
Contech from First Principles: A 'Site Up' approach to meaningful data
As the industry has evolved and software is more broadly adopted, expectations are beginning to shift left - the spotlight is now firmly on the importance of collecting clean, reliable data
Kurt Robinson | Published on 30 November 23
Across the construction industry there is a common need to do more with less - be it with resources, materials, plant, or technology.
Whether a developer, contractor or subcontractor, successful project teams understand that data collection begins early and is only made possible with technology that subcontractors willingly use.
As useful as AI will be, it will remain reliant on good quality, raw data. And while real people still build apartments, schools and hospitals - your data collection will still begin at the site level - with your people, on your worksites and in your offices - everyday.
Everyone's forgotten the subbie
Enlisting site teams to construct your data sets
Your business insights are only as reliable as the raw data they are compiled from, so ensuring your team is producing and analysing clean, reliable data is absolutely paramount.
Before introducing high-spec tech and AI to your project teams, take a step back and review your data collection from first principles - beginning with the subcontractor from a site level up.
What it means to obtain a clean and consistent construction data-set
There has been limited focus on how construction data is collected at the site-level and how this impacts ongoing access to the data during the project, as well as the reliability of the data that is later used for generating business insights. Incomplete, inaccurate or inconsistent data will deliver distorted perceptions of your projects and organisation.
While a range of variables impact the quality and reliability of data that is collected, a few key principles should be considered when implementing software systems:
Standardise Data Collection: Using consistent and replicable methods to collect raw data which enables the data to be reliably compared across people, organisations, time and projects. E.g. All subcontractors using the same platform and the same forms to track Incidents, sign in to the worksite, or complete Inspections allows for side-by-side comparisons.
Ensure Data Validity: this refers to the accuracy and quality of the data, and how it is received, stored and extracted in preparation for analysis. This is a particular concern for data that is compiled from different sources and then compared, such as multiple apps or point solutions which may have different rules for inputting data e.g. free-form text inputs vs. drop-down fields, use different date or data field variables, or contain duplications.
Maintain Data Continuity: This is the continued acquisition and availability of unenhanced data for the duration of the time period being studied, i.e. in construction, this refers to a User's access to data across the construction project lifecycle, from ECI through to handover and DLP. When data is stored in different apps or point solutions, it becomes fragmented and siloed, reducing access and increasing project inefficiencies, delays and the likelihood of errors.
It's easier to achieve than it sounds
While the average construction company produces gigabytes of data every week, most can't afford to employ data scientists or systems analysts, so here's how construction companies are preparing their organisations for the plethora of emerging software technologies just around the corner...
Upending the Decision Matrix and beginning with the Subcontractor
Opting for platforms with breadth and depth, designed for team collaboration, and containing 95% of the core operational functionality required for the duration of a project
We are beginning to see a shift in how development and construction management teams select technology for their organisations. Traditionally, decisions were made by management teams with limited consultation of end-users, however, this method resulted in under-utilisation of software, increasing use of shadow technologies and disparate systems implemented across teams and projects.
In the interest of high utilisation and connected data sets (together with all the principles previous discussed), decision makers are taking a different approach. Priorities are realigning and they are beginning with frontline user requirements.
1. Subcontractor/User
- Providing software that is easy to onboard and use, thus increasing broader site- wide adoption
- Ensuring Users have full control over their own personal data, including who their data is shared with, what projects they are added to, and how it is stored and deleted
- Tailoring the appearance of software for each user group to ensure users only see what they need in order to perform their role and are not overwhelmed with irrelevant project information
2. Company level
- Implementing software that is designed for collaboration and delivers 95% of the operational functionality that is required by the whole team - from subcontractors through to management
- Ensuring this same software can be effectively utilised for the duration of the project, from ECI's through to DLP and Handover
3. Project level
- Installing hardware and IoT that captures real-time project data such as Site Access systems
- Fostering accountability across docs and comms to capture a project-wide audit trail
- Digitising worksite processes to ensure everyone has to follow the same procedure to complete a task
- Ensuring completeness data by making data fields and photo attachments mandatory
- Creating parallel or sequential workflows with holdpoints that require approvals/sign-offs and increase accuracy and accountability
The shift to single project delivery platforms
The key to driving operational insights derives from a platform approach to ensure the user can, and does engage through all stages of the project delivery lifecycle
Looking Ahead: We're preparing our clients for emerging tech
We know that data will revolutionalise construction, and industry heavyweights are already understanding the impact of carefully collected data and applied data intelligence in their organisations.
But AI can't solve bad data problems, it will just compound them. We need to trust the data first.
At Simpel, our focus is on empowering our clients to best-leverage the clean and connected data sets that our single project delivery platform already provides. It doesn't take a data scientist or systems analyst to understand and use them, and the data can be used daily to make faster operational decisions and deliver business intelligence that will help deliver projects sooner.
To learn more, contact us for a demo.
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