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Construction firms are expected to steer the ship into profitable waters while avoiding risky squalls. To get there, CEOs have a wealth of knowledge about the business at their disposal, including financial statements, WIP reports, KPIs and performance reviews.

These documents all serve as ammunition for the executive officer to wage a war against inefficiency, waste and margin fade. At the project level, foremen are expected to wage similar wars. They are responsible for the effective execution of a defined scope and ultimately meeting or beating the budgeted labor hours. Yet, time and again, the field leadership is rarely afforded access to accurate data that would allow them to make prudent, objective decisions on the jobsite.

Similarly, the quality of data points originating from the field is often less than ideal. When dealt inaccurate information, project managers are either blissfully unaware or are frustrated about making gut decisions with imperfect knowledge. Weak data from the field ultimately causes systemic rot in a company’s information channels that creeps up the hierarchical ladder — right into the executive suite.

Getting to the Head of the Stream

Data integrity management starts at the point of origin: the field. The breakdown in quality data reporting in the field typically results from one or more process-related issues that exist well before the first shovel breaks soil onsite. These include:

  • unrealistic budgets due to scope gaps in the estimates;
  • budgets solely based on estimates, with no reallocation of dollars or hours to accommodate the realities of the project or work sequence logic;
  • falsely understated budgets generated by project management, rooted in an argument that “the field will always use 100 percent of the budget, so project managers might as well shortchange the field’s hours/dollars;”
  • a reluctance to share any budgetary information with the field, typically resulting from paranoia that proprietary cost information might fall into the wrong hands;
  • non-standardized data collection processes or reporting mechanisms; and
  • abusive management styles that incentivize field leadership to hide the realities of project progress in order to avoid retribution until the last possible moment.
The aforementioned challenges are real and pervasive in today’s construction industry. But in the absence of accurate data, how do managers make the right decisions for their projects? Traditionally, contracting firms have relied on the invaluable experience of their seasoned field leadership to make those gut decisions, and about 90 percent of the time, those decisions are solid enough to produce favorable outcomes. However, that 10 percent of less-than-ideal decision-making can result in disproportionate project losses. In turn, these losses could wreck an otherwise profitable annual income statement. Further, the absence of accurate data can cause serious under-recognition of budgeted costs on future work or gross overstatement of budgeted costs on future bids, leading to loss of competitive pricing.

Furthermore, veteran field leadership is leaving the industry at a record clip. As a result, more projects are being staffed with young and relatively inexperienced field leadership. Poor data, met with inexperience in the field, is a recipe for disaster.

So what is the solution?

Strong data management starts with an organizational culture that prioritizes quality information. Everyone must be on board and buy into the premise that an increase in data quality will result in easier decision-making, time savings and other benefits for the company.

In addition, the foundation of a truly data-driven culture is the idea that budgetary information should be shared with the field. This concept runs counterculture to the management philosophy of many construction business owners. There is an old-school mentality that budgets should not be of concern to the field, and that if the field is working as hard as it can every day, the results will be the same (regardless of whether the foreman is privy to the budget). That would be true if construction occurred on an assembly line, but projects are extremely dynamic. Optimizing productivity is more about working smarter than it is about working harder. If business leaders want their foremen to care about meeting or beating budgets, it is only logical that true budget information be shared with them.

The Value of Data Integrity

Embracing the notion that budget transparency increases the effectiveness of field leadership is just the first step on an extensive journey to becoming a data-driven operation. Even if the decision to share more information with the field is handed down from the executive level, it will still take a long time for behavioral changes to catch up with the stated desires of leadership.

The purchase of an IT platform is not a panacea for a firm’s data woes. If the proper resources are not allocated to support the orientation, rollout and training for the new system, it will quickly become just another administrative burden, rather than a tool for efficiently collecting and processing data. The same argument can be made for process development and implementation.

Given the significant costs associated with the purchase and implementation of systems and processes, the decision to invest in data integrity and reporting efficiencies must be made within the context of a firm’s long-range strategic trajectory. Said differently, a company won’t earn its money back right away. Quality data processes and systems are as much about controlling risk as they are about saving time and money. In the long run, the increased capability to control information will pay for itself many times over, but firms investing in this arena must exercise patience.

The 'What' of Data Management

With culture, systems and processes in place, it is time to look at the “what” of data management: What to collect? What to process? What to report? As mentioned earlier, quality information starts in the field. The project team needs to know: What was installed? How much material was used? How much labor was associated with that task? Or for general contractors, what is the progress on a given subcontract?

Everything starts with an accurate budget. Best-of-class contractors invite field leaders into the office to assist in recasting the estimated budget into a project budget that represents the optimal allocation of dollars/hours based on a logical sequence of workflow. Inclusion of foremen and superintendents in this process allows them to gain buy-in to the budget. If field leadership has ownership in the accuracy and logic of the budget, both the field and the office can be confident that cost coding onsite is aligned with the mutually agreed-upon expectations for the project. With quality data collection in place, the challenges of processing and reporting can be entertained.

There are two extremes of data processing: manual entry and full automation. Manual entry creates a significant administrative burden on an organization and invites the risk of human error. However, manual entry is the simplest way to generate reports without investing in fully automated systems.

Full automation has its drawbacks as well. For example, a glitch in the system can be difficult to detect until the error in reporting becomes drastic and consequential. Also, fully automated systems are cumbersome. Changing what data is reported and how it is reported is not an easy task and typically calls for an additional hard-cost investment.

The perfect balance of manual entry and full automation is unique to each organization. For firms just starting out on the path to becoming a data-driven operation, it would be wise to err on the side of simplicity, as the process of trial and error can result in several iterations of report formats and data representations. Once confident that the reporting is accurate and formatted appropriately, automation becomes the next logical discussion.

Proper Report Analysis Is Vital

Physical data reporting aside, proper analysis of report documents is an essential piece of the data puzzle. Companies have to make the appropriate investment in training their employees to interpret reports and dashboards. Without formal indoctrination to the analysis of the data, information can be misconstrued, leading to poor or adverse decision-making.

Additionally, company leaders must build the expectation that reports on labor productivity, material management and measures are not to be used by project managers as tools for abusing the field. These reports are simply communication tools intended to initiate and facilitate conversations between the office and the field. If reports are used as a weapon, field management will quickly begin to misrepresent data in its favor to avoid conflict. Project-specific progress reports should provide indications as to where certain challenges exist on a given job. Armed with that information, a foreman and a project manager should be able to work collaboratively to find solutions and mitigate exposures.

Once a firm is confident in the integrity of its project-specific data, most other data processing and reporting challenges will be largely administrative in nature. However, the challenges of interpreting information appropriately and making sound decisions remain. At all levels of an organizational chart, managers must understand where the data is coming from, how it is being represented and, ultimately, what to do with that information. The primary accountability focus should be on the quality and integrity of the data, not the actual data points themselves.

For example, it does not serve managers well to anguish over the fact that actual costs are greater than budgeted costs on a given project. The real value lies in using that information to open discussions with project team members to determine why the data is showing cost overruns, and to collaboratively find cost-mitigating solutions. That particular discussion is the true hallmark of a data-driven culture and an indication that the company has arrived at a milestone in the data management journey.

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