In the ever-evolving landscape of the construction industry, where decisions can make or break a project or business, analytics maturity has become a beacon guiding companies toward informed choices.
At the heart of this evolution is decision intelligence, a process that not only analyzes historical and current data but also explores the potential outcomes of different choices. In this blog post, we delve into the concept of analytics maturity and decision intelligence, specifically through the lens of the dynamic world of construction.
Understanding analytics maturity
Analytics maturity in construction is a journey that progresses through different stages, each offering a deeper level of understanding and foresight. Let's embark on this journey:
Stage 0 - No Analytics
Decisions made without data; A stage that highlights the importance of introducing analytics into the decision-making process. What we see here are companies pursuing projects relying on gut feelings, personal experiences, and word of mouth. There is no systematic approach to decision-making - they make decisions about pursuing opportunities based on personal relationships rather than data-driven insights.
Stage 1 - Descriptive Analytics
This initial stage involves understanding what has happened in the past. It answers the question, "What happened?" by considering current and historical data.
Companies at this stage have started to analyze historical data of past projects to understand which types of projects have been most profitable. This retrospective analysis guides their decisions on which sectors to focus on for future opportunities.
Stage 2 - Diagnostic Analytics:
Moving beyond descriptive analytics, diagnostic analytics identifies not only what happened but also why it happened. This stage aims to uncover casualties behind the data, providing deeper insights.
Building on descriptive analytics, teams at this stage investigate why certain projects were more successful than others. They use these learnings to qualify/unqualify new opportunities they’ve identified, allowing them to spend time more efficiently on the projects most likely to be profitable.
Stage 3 - Predictive Analytics:
Predictive analytics attempts to uncover "what will happen, why, and when?" by analyzing current and historical data to identify trends and patterns. Machine learning algorithms are crucial in predicting future trends, and empowering companies to make informed decisions.
At this stage, companies use ML tools to predict trends or potential outcomes. For instance, they can anticipate an upcoming surge in residential development by analyzing the volumes of permits by sector and year-over-year growth to adjust their focus accordingly.
Stage 4 - Prescriptive Analytics:
Prescriptive analytics takes it a step further by offering insights on "what should happen?" and "what should the company do?" It leverages machine learning and deep learning capabilities to prescribe actions for decision-makers based on big data analysis.
At this stage, the business development team not only predicts trends but also receives recommendations. These systems would need to predict a potential slowdown in a specific sector and suggest diversifying into other sectors to maintain a steady project pipeline.
Stage 5 - Cognitive Analytics:
Finally, cognitive analytics involves the collaboration of AI with humans to support the decision-making process. This fully automated process uses machine learning models with self-learning features to deliver actionable intelligence, eliminating the need for extensive analytics expertise.
Here we see teams fully integrate cognitive analytics into their workflow. The system collaborates seamlessly with team members, automatically identifying potential clients based on historical preferences. It provides tailored strategies for engaging with clients, reducing manual effort, and increasing efficiency.
Decoding decision intelligence in construction
Decision intelligence combines various disciplines, including artificial intelligence (AI), data science, and human behavior, to improve decision-making processes. The goal of decision intelligence is to enhance the quality of decisions by leveraging data, algorithms, and human expertise.
The key components of decision intelligence typically include:
Data Analysis
Analyzing large datasets to extract valuable insights serves as the foundational step in achieving intelligence. This initial process typically involves examining historical data, real-time information, or a combination of both.
Decision Models
Building models that represent decision scenarios helps in understanding the potential outcomes and their probabilities. Decision models can be quantitative, using mathematical approaches, or qualitative, incorporating expert judgment.
Artificial Intelligence and Machine Learning
AI and machine learning techniques play a crucial role. These technologies can help in pattern recognition, predictive modeling, and optimization, providing valuable input for decision-making.
Human Expertise
It’s essential to recognize the importance of human judgment and expertise in decision-making. Here the aim is to integrate human insights and decision-maker intuition with the analytical power of AI and data-driven models.
Visualization and Communication
Effective communication of complex information is essential for decision-making. Visualization tools and clear communication strategies are often employed to convey insights and recommendations to decision-makers.
Continuous Learning and Adaptation
These systems are designed to evolve and adapt over time. Continuous learning from new data and feedback from decisions helps improve the accuracy and relevance of models and recommendations.
Real-world applications for data analytics in construction
Intelligence can be a valuable asset in the construction industry and should be the end goal of your data analytics maturity journey. We’ve seen several ways decision intelligence is already applied in the construction industry to enhance decision-making processes and contribute to business success:
Market Analysis and Opportunity Identification
Analyzing market trends, identifying potential opportunities, and assessing the demand for specific construction projects can aid business development teams. Data-driven insights can help prioritize target markets and guide decision-makers in allocating resources effectively.
Bid Optimization
Applying analytical techniques to historical bid data, market conditions, and project requirements can optimize the bidding process. Predictive modeling can estimate the likelihood of winning a bid and guide the team in setting competitive yet profitable pricing strategies.
Risk Assessment
Evaluating and quantifying risks associated with new construction projects is essential. Factors such as delivery type, sector, location, cost, and timeline need to be considered. Understanding potential risks enables informed decisions about pursuing specific projects and developing risk mitigation strategies.
Resource Allocation and Project Prioritization
Optimizing resource allocation involves analyzing past project outcomes, win vs. loss rates, and project characteristics. These insights help prioritize projects aligning with the company's strengths and revenue growth potential, avoiding wasted time on unfit projects.
Client Relationship Management
Analyzing client data and historical interactions aids in identifying patterns and preferences. Understanding client needs enables tailoring approaches, improving client satisfaction, and fostering long-term relationships.
Strategic Planning and Portfolio Management
Developing long-term business strategies by analyzing market dynamics, competitor activities, and industry trends is something we’re seeing more and more companies tackle. Portfolio management can be optimized by evaluating the profitability and strategic fit of different types of projects and guiding decisions on diversification or specialization.
Market Forecasting
Forecasting future market conditions allows proactive adaptation to changes in demand, supply chain dynamics, and regulatory environments.
How to get started
Incorporating decision intelligence in business development within the construction industry can lead to more informed and strategic decision-making. By leveraging data-driven insights, construction companies can enhance their competitive edge, increase operational efficiency, and drive sustainable business growth.
For growth and development teams, navigating through the vast seas of information out there is overwhelming enough. Partnering with startups building tools like Mercator AI has proven to be invaluable by aligning these technologies with various stages of analytics maturity.
So, where should you start?
Invest in Analysis
Start small – take the time to understand where you are in the analytics maturity journey. To get to stage one, start with retrospective analysis, what happened before? Leveling up to stage two should focus on diagnostic analysis, not only what happened but also why it happened. Getting to Stage three starts with trying to uncover what will happen, why, and when? And to reach those later stages, start to explore "what if" scenarios with advanced analytics models to uncover potential project profitability and ecosystem expansion opportunities.
Leverage Market Intelligence Tools
Utilize market intelligence tools to make sense of vast market data and democratize insights. These tools expand the surface area for project discovery, ensuring more eyes and ears are on the lookout for new opportunities.
Collaborate with Startups
Partner with startups specializing in construction analytics, such as Mercator AI, to develop tailored solutions for the industry. Collaborative efforts can enhance the effectiveness of decision intelligence tools and address industry-specific challenges.
The end goal of analytics maturity
As the construction industry embraces the ever-evolving landscape of analytics maturity and decision intelligence, the path to informed choices becomes clearer. The significance of decision intelligence in leveraging data, algorithms, and human expertise is a clear priority for the industry.
We’ve shown you the real-world applications highlighted in market analysis, bid optimization, risk assessment, resource allocation, client relationship management, and strategic planning.
For companies that recognize the value of integrating decision intelligence into their operations, the next steps are clear: to thrive in the dynamic construction landscape, embracing analytics maturity and decision intelligence is not just a choice but a necessity.
Begin by investing in analytics. Start with a thorough understanding of your current analytics maturity stage, progress through retrospective and diagnostic analysis, and subsequently explore advanced models for uncovering potential project profitability and ecosystem expansion opportunities.
Simultaneously, leverage market intelligence tools to democratize insights from vast datasets, expanding the surface area for project discovery and ensuring a broader scope for new opportunities.
Lastly, foster collaboration with startups specializing in construction analytics. Partnering with these innovative companies can leverage their expertise to develop tailored solutions for the industry. Collaborative efforts have the potential to enhance the effectiveness of decision intelligence tools and address industry-specific challenges, positioning your business for sustainable growth in the dynamic construction landscape.
