Information Visibility: The 3 Stages of Data Analytics (Part 1 of 3)
Capital projects create enormous amounts of information every day, but how that data is gathered, organized, accessed, and used separates the top players from the average ones in the industry landscape. Managing large-scale construction projects requires many moving pieces to keep the operational wheels turning.
Unfortunately, issues like disorganized, inaccurate data, siloed data systems, paper-based reporting, and a lack of system integration can slow down projects and impede efficiency. In the competitive construction landscape, investment in the right tools and data analysis methods is critical for your business to thrive.
By transforming your data into actionable intelligence, you can reduce risk, streamline project timelines, model future outcomes, and provide greater information transparency across the organization.
When establishing an analytics methodology within your company, it’s helpful to think of the process in three parts: information visibility, data analytics, and business intelligence. In leveraging your data through this progression, you will benefit from immediate project insights, and effectively guarantee greater success in future projects.
Information Visibility: What Is Your Business Doing Now?
Information visibility is an essential component of your overall data strategy. If your teams are working with inaccurate data, you will be setting yourself up for serious errors down the line: going over budget, finishing the project late, or running into supply chain issues.
To ensure that you’re creating a sound strategy for information visibility, you’ll need to consider what systems are used to view, share, and collect information. Part of this process requires determining where the organization is creating data and how to gather that data efficiently.
Data Standards
It’s crucial to ensure that the data is clean and standardized before being called the sole source of truth. Ideally, this happens on the system-side through process improvement, better data entry methods, and limiting opportunities for error at data entry points. If this is not possible, your data team must work to develop strategies to clean and standardize the data upon gathering. This helps bolster company-wide accuracy and deliver reliable business and project metrics.
Most project and portfolio data is diverse, unstructured, and disorganized. In some cases, essential data is underutilized or ignored entirely. But data alone doesn’t provide intrinsic value; instead, the value materializes from the context of other workflows, relationships, and patterns.
Data Visibility
Whether employees are in-office or in the field, data visibility can lead to better, more informed decisions. For example, you can create integrated reports for company-wide cohesion if you’re collecting data through separate systems and technologies.
Disparate departments and teams can rely on this single source of truth rather than working within siloed systems that don’t present the complete picture or context. In terms of data transfer between teams, you can leverage cloud technology to produce a centralized system for real-time data storage and collection and avoid the likelihood of error or duplication.
Managers with incomplete, unreliable, or imprecise data tend to make worse decisions, and clean-up of data and data processes can act as the first line of defense.
Bringing visibility to your stored data will allow you to:
Clean up and unify data
Establish a single source of truth
Foster greater data system integration
Develop better team cohesion and understanding
Join us for Part 2: Data Analytics - How is Your Business Doing Now?
