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.
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