By Bruce Orr with ProNovos, Chief Data Scientist
Construction firms receive a flood of information these days—everything from sales pitches in their email inboxes, to cover stories in industry magazines—about the potential for data analytics to revolutionize what they do.
And it’s true that under the right circumstances shifting from siloed spreadsheets to advanced data warehouses and analytics engines can yield transformative insights.
However, the discussion of these benefits often leaves out a critical fact: Beautiful charts, graphs and animations are meaningless if the data used to create them is full of holes.
For many contractors, what might be thought of as poor “data hygiene” is a pressing concern. A flawed approach to data entry—especially the need for different stakeholders to manually enter data into different systems multiple times—tends to be the root of the problem.
As the volume of inaccurate records grows with time, seemingly small mistakes morph into major anomalies that warp the story told by the data. For example, the project manager may enter “SmithCo Steel” into the spreadsheet even as the controller refers to “Smith Co. Steel” in the document. Without clarity into this inconsistency, a later analysis will skew the results.
In a worst-case scenario, faulty or incomplete data in categories such as vendors, subcontractors, employees, equipment, materials or project costs undermines a contractor’s good-faith effort to base its strategy on the facts.
And yet despite the high importance of data integrity, some contractors are reluctant to tackle this issue.
This may be because they see it as a time-consuming, backward-looking exercise that involves laboriously poring over existing files to ferret out incompleteness, inconsistency, duplication or lack of timeliness.
However, ramping up data accuracy—especially when it includes shoring up data-collection processes—sharpens your understanding of present-day trends. It also positions you to take advantage of future-oriented data analytics, a predictive approach that stands to get even better with the continued evolution of machine-learning and AI.
Bolstering data integrity isn’t as difficult as it may seem. A few simple steps can put your organization on the right path.
Step 1: Put a Premium on Pulldowns
Whether the system is Sage, Viewpoint, Foundation or Microsoft Excel, contractors often make the mistake up setting up data-collection processes in ways that require employees to repeatedly enter company names, project numbers and other critical markers by hand. This increases the risk of generating duplicate or divergent records, as in the SmithCo Steel example above (or should that be Smith Co. Steel?).
A better approach is to leverage the ability of the software to generate a pulldown menu. All users should be trained to make use of this feature and, whenever possible, avoid manually entering data.
It should be noted, though, that Microsoft Excel users will need to build an app for pulldowns. Ask your IT department or an external consultant to build the app for key documents. (Project managers and accountants rarely have the time or expertise to do this themselves; left to their own devices, they will probably stick to manual entries.)
Step 2: Get a Data Hygiene Test
Figuring out whether your company has a data-cleanliness problem does not require your teams to work nights and weekends hunting down errors in old spreadsheets.
It can be accomplished with software.
Look for a tool that can give you a score on factors such as data completeness, accuracy, consistency and timeliness. Granularity is important. If a contractor is running an analysis that involves job descriptions as a key component, it helps to know if 30 percent of your records actually fail to include any job descriptions at all.
When it comes to the likes of duplicate entries, a data hygiene test can uncover whether you have a major or minor issue. This, in turn, enables you to understand any spillover effects on compliance, revenues or expenditures. Consultants can also tell contractors whether process flaws contribute to or create data-quality issues.
Step 3: Leverage Existing Best Practices
The need for data cleanliness is hardly unique to construction contractors. As a result, there’s no need to reinvent the wheel: Existing practices in master data management (MDM) provide relatively painless pathways to resolving what might seem like intractable conundrums.
Take the example of one regional construction contractor in the United States. The company, which was onboarding a new enterprise data warehouse, had long tracked its change orders using Prolog project management software. Management wanted to merge this data stream with flows from the contractor’s Sage accounting system. However, there was a problem: The job-numbering systems were different. “Job 1-2-3” in Prolog was, in Sage, “Job A-B-C.”
For anyone with expertise in master data management, this was a familiar situation with a readymade solution. In this case, our team used a mapping tool in the data warehouse to sync the job data, allowing us to merge the data flows and ready them for analysis.
Contractors typically use one system for their bids and another for accounting. Let’s say the contractor aims to win a bid with Skanska AB. It would be helpful, as part of that process, to merge both the accounting and bid-system data streams for Skanska AB. Why? Because it would yield easy analysis of prior bids as well as past project costs, timelines and results. Mapping makes this kind of thing easy to accomplish, and there are many other high-utility methodologies that are part and parcel of MDM.
A Solid Base for Construction Data Analytics
Data cleanliness is a prerequisite for effective use of construction data analytics.
In addition to improving analysis, achieving progress in this area expedites major data transitions as well, such as moving from one accounting system to another or acquiring another company and merging its data streams with your own.
Construction data analytics platforms and data warehouses can be an indispensable part of the process, which explains why this is such a fast-growing field. All told, there is growing awareness in the industry in the potential for these tools to empower contractors to track and manage bids, crews, equipment, punch lists, blueprints, requests for information and more in easy-to-use interfaces. Moving forward, AI also stands to improve risk forecasting, jobsite quality-control and route-planning/transportation. Good data hygiene allows contractors to hit the ground running as this quantum leap further transforms the industry.
- Posted by ahqiadmin
- On December 16, 2019
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