The construction industry has been slow to adopt a culture of data-driven intelligence, but as the case for it grows — and as it becomes more accessible to firms of all sizes — the laggards may get left behind.
Key insights
- In recent years, large firms have developed and implemented data analytics constructs to help them operate more efficiently.
- Smaller companies often do not embrace data analytics, because their back offices are lean or non-existent.
- In many cases, deep analysis of data leads to a lot of smaller changes that ultimately add up.
- Data analysis doesn’t have to be a massive, enterprise-level project — the concepts can be applied to singular issues.
The construction industry has been slow to embrace data analytics. However, as data intelligence proves itself in other industries — as well as in use cases within construction itself — any apprehension now seems shortsighted.
Ready to embrace big data?
In recent years, large firms have developed and implemented data analytics constructs to help them operate more efficiently. Barriers to entry have decreased and organizations of all sizes can now tap the power of data analytics to achieve similar results.
The slow adoption of data in construction
There’s a hidden contradiction in the construction industry. If a new building technique shows possibility, widespread adoption can be swift. But that swift adoption hasn’t carried over to data. This is indicative of a cultural mindset that needs — and slowly is — changing.
Traditional business models have played a role in slow data adoption for construction companies. In the past, financial data was housed in different systems, typically segregated from production data. Additionally, lag time between actual production and aggregation of production-related data meant many firms looked at financial data in arrears, where the information offers little insight to help you make astute predictions and estimations.
This problem can be exacerbated in smaller companies where back offices are lean or even non-existent. Those responsible for back office functions in small firms may also spend a lot of time on job sites. This not only creates a scenario where they think they know what is going on in the big picture, but it also masks how much they don’t see (that is hidden in the data).
Smaller firms are frequently quick to move on to the next project, and the time it takes to look back on projects can feel counterproductive. But as larger firms have experimented in data analytics, they’ve found that construction organizations have been guilty of not knowing what they don’t know. This recognition of the impact of data analytics illustrates its potential for construction firms of all sizes.
Motivations for data analytics in construction
Increased profitability is the most obvious and useful result of data analytics implementation. But it’s not as simple as saying, “Let’s use data analytics and make more money.” Understand all the ways data analytics can lead to improved profits. For example, data analytics can result in:
- More efficient management
- More accurate budgets/bid estimates
- Decreased project risk
Data doesn’t instantly make things better. In many cases, deep data analysis leads to multiple smaller changes that ultimately add up to positive results. For example, a change order process may lead to a bottleneck and slow transfer of field data to the back office. You may identify a more efficient route for materials deliveries and share real-time data between the field and office to reduce the risk of something falling through the cracks. Here you can see how data analytics can make many aspects of an organization more efficient.
Facts in production and financial data can also help you gain insight into which jobs you should and shouldn’t pursue. For example, some organizations have learned that larger jobs actually eroded profit margins. An analysis of the right data can help you understand which bids could be more profitable.
Recognize the utility of business intelligence from data analytics as an initial step. Next, understand what data you have and compile it.
The data is there, you just have to use it
All the data in the world won’t make a positive impact if it isn’t understood and implemented effectively.
The ultimate goal of data analytics is to use it as a predictive model so you can potentially be more successful in bidding and operating (and ultimately optimize profits). In order to achieve this, leverage your data to tell stories. The greatest information dashboards in the industry are virtually useless if the information doesn’t lead to concrete action.
Data analysis doesn’t have to be a massive, enterprise-level project. In fact, concepts can be applied to singular issues. While this is true, keep in mind that the more data analytics is applied to a business, the greater the insights.
Realize that it’s easy to be misled by all the options out there. You may find that software providers, consultants, and technology companies offer business intelligence solutions. To capitalize on data analytics, understand which solutions fit your particular organization. The wrong solutions can prevent you from seeing the full potential of data analytics — or can even be detrimental.
Every organization should have different data solutions since they all have different data and processes. Understand whom to trust to get the most from data analytics.
How we can help
Business intelligence has arrived for small- and medium-sized businesses (SMBs). In fact, given that smaller firms don’t benefit as easily from economies of scale like larger counterparts, data might be even more valuable for SMBs. CLA is here to help you identify the right approach for your organization.