Nearly all contractors are seeking cultural and operational changes to increase data-driven decision-making. Investing in business intelligence (BI) processes and programs that produce dashboards and report on KPIs create access to company insights, which can transform vital metrics into action, ultimately empowering leaders to perform at a higher level.

BI encompasses a variety of tools, practices, and technologies that can transform raw data into meaningful insights for business analyses. This article aims to help professionals articulate the key components of BI, their importance and functionality, and how they play into modern business strategy and operations.

Why Now?

The deployment of BI is not merely an enhancement but rather a modern-day imperative for construction companies experiencing industry pressures. For instance, the labor shortage and skills gap present significant hurdles, requiring companies to optimize workforce management and enhance productivity through data-driven strategies. Alongside these workforce challenges, schedule compression demands more efficient project planning and execution, and BI can provide real-time insights for better decision-making.

Increased competition and margin volatility further underscores the need for BI, as companies seek to differentiate themselves and create certainty in their financial performance through superior operational efficiency and strategic foresight. BI tools offer the capability to navigate these pressures by leveraging data for project outcomes, cost management, and competitive positioning.

These challenges can help facilitate meaningful conversations in your organization about its BI aspirations. As you read through the components of how to generate your own BI program, consider who in your organization will be the right people to engage each at step.

Before you begin, start by considering the why: Why is it important for your company to get serious about BI? Why do it now? Also, answer the more individual question: What’s in it for you?

By considering these questions, you’ll achieve greater alignment and buy-in on this journey, whether you are just starting out or are well on your way.

How to Build It: The Components of BI

Data Collection

Purpose-built business computer applications are crucial because they streamline data entry and collection processes, leading to higher quality data.

These applications are designed by a vendor and selected by a company with specific needs in mind; when implemented properly, they often include features that ensure data is entered correctly the first time from wherever they originate (e.g., in the field).

Additionally, they might have validation checks that prevent incorrect data types from being entered or drop-down menus that limit choices to ensure consistency in the data

 collected, as high-quality data is essential for trusted BI ecosystems.

When data entering the system is accurate, complete, and consistent, then the analyses and reports generated from it are more reliable. And, purpose-built applications can automate much of the data collection process, reducing the need for time-consuming manual data entry that is often prone to errors. This automation not only improves data quality, but also keeps employees focused on more value-added activities.

Data Architecture

The data architecture must support the weight of current data demands and also possess the flexibility to accommodate future expansions and technologies. A well-conceived data architecture ultimately acts as the backbone of BI.

The narrative of crafting such an architecture involves multiple considerations, each critical to ensuring that the final system delivers insights efficiently and effectively.

Data storage is the technical manifestation of your data architecture. As your team designs your data architecture, it is important to ensure the structure is scalable and flexible, capable of growing with the organization, and accommodating increasing volumes of data without degradation in performance. All the while, it should be flexible and adaptable to new data types, sources, and BI technologies.

An often-dismissed factor is data governance and quality, including policies, standards, and procedures that manage data access, usage, and security within an organization. A solid data architecture embeds these governance measures to ensure data integrity, quality, and compliance with regulations.

Similarly, security and compliance must be given great care. In this era when data breaches can have significant legal and reputational consequences, securing sensitive data is paramount.

A comprehensive data architecture incorporates robust security measures including access controls, encryption, and audit trails to protect data integrity and confidentiality that may also need to comply with evolving regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).¹

Data architecture also includes aspects to support performance and access to data integrations across applications. Users expect quick responses to queries and analyses, requiring the architecture to optimize processes for data storage, processing, and retrieval as well as accessibility for a wide range of data to support diverse user needs.

When thinking about data integration across systems, the goal is to create seamless integrations for appropriate data flows among systems and to the data repository.