{"id":25799,"date":"2024-05-13T14:28:46","date_gmt":"2024-05-13T21:28:46","guid":{"rendered":"https:\/\/essential.construction\/news\/keeping-it-clean-analytics-can-benefit-contractors-so-long-as-they-maintain-good-data-hygiene\/"},"modified":"2024-05-13T14:28:49","modified_gmt":"2024-05-13T21:28:49","slug":"keeping-it-clean-analytics-can-benefit-contractors-so-long-as-they-maintain-good-data-hygiene","status":"publish","type":"post","link":"https:\/\/essential.construction\/news\/keeping-it-clean-analytics-can-benefit-contractors-so-long-as-they-maintain-good-data-hygiene\/","title":{"rendered":"Keeping it Clean: Analytics Can Benefit Contractors\u2014So Long as They Maintain Good \u2018Data Hygiene\u2019"},"content":{"rendered":"

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Construction firms receive a flood of
\ninformation these days\u2014everything from sales pitches in their email inboxes, to
\ncover stories in industry magazines\u2014about the potential for data analytics to
\nrevolutionize what they do.<\/p>\n

And it\u2019s true that under the right
\ncircumstances shifting from siloed spreadsheets to advanced data warehouses and
\nanalytics engines can yield transformative insights.<\/p>\n

However, the discussion of these benefits
\noften leaves out a critical fact: Beautiful charts, graphs and animations are
\nmeaningless if the data used to create them is full of holes.<\/p>\n

For many contractors, what might be thought
\nof as poor \u201cdata hygiene\u201d is a pressing concern. A flawed approach to data
\nentry\u2014especially the need for different stakeholders to manually enter data
\ninto different systems multiple times\u2014tends to be the root of the problem.<\/p>\n

As the volume of inaccurate records grows with
\ntime, seemingly small mistakes morph into major anomalies that warp the story
\ntold by the data. For example, the project manager may enter \u201cSmithCo Steel\u201d
\ninto the spreadsheet even as the controller refers to \u201cSmith Co. Steel\u201d in the
\ndocument. Without clarity into this inconsistency, a later analysis will skew
\nthe results.<\/p>\n

In a worst-case scenario, faulty or
\nincomplete data in categories such as vendors, subcontractors, employees, equipment,
\nmaterials or project costs undermines a contractor\u2019s good-faith effort to base its
\nstrategy on the facts.<\/p>\n

And yet despite the high importance of data
\nintegrity, some contractors are reluctant to tackle this issue.<\/p>\n

This may be because they see it as a
\ntime-consuming, backward-looking exercise that involves laboriously poring over
\nexisting files to ferret out incompleteness, inconsistency, duplication or lack
\nof timeliness.<\/p>\n

However, ramping up data accuracy\u2014especially
\nwhen it includes shoring up data-collection processes<\/em>\u2014sharpens your
\nunderstanding of present-day trends. It also positions you to take advantage of
\nfuture-oriented data analytics, a predictive approach that stands to get even better
\nwith the continued evolution of machine-learning and AI.<\/p>\n

Bolstering data integrity isn\u2019t as difficult
\nas it may seem. A few simple steps can put your organization on the right path.<\/p>\n

Step 1: Put a Premium on Pulldowns<\/h3>\n

Whether the system is Sage, Viewpoint,
\nFoundation or Microsoft Excel, contractors often make the mistake up setting up
\ndata-collection processes in ways that require employees to repeatedly enter
\ncompany names, project numbers and other critical markers by hand. This
\nincreases the risk of generating duplicate or divergent records, as in the
\nSmithCo Steel example above (or should that be Smith Co. Steel?).<\/p>\n

A better approach is to leverage the
\nability of the software to generate a pulldown menu. All users should be
\ntrained to make use of this feature and, whenever possible, avoid manually
\nentering data.<\/p>\n

It should be noted, though, that Microsoft
\nExcel users will need to build an app for pulldowns. Ask your IT department or
\nan external consultant to build the app for key documents. (Project managers
\nand accountants rarely have the time or expertise to do this themselves; left
\nto their own devices, they will probably stick to manual entries.)\u00a0<\/p>\n

Step 2: Get a Data Hygiene Test<\/h3>\n

Figuring out whether your company has a
\ndata-cleanliness problem does not require your teams to work nights and
\nweekends hunting down errors in old spreadsheets.<\/p>\n

It can be accomplished with software.<\/p>\n

Look for a tool that can give you a score
\non factors such as data completeness, accuracy, consistency and timeliness.
\nGranularity is important. If a contractor is running an analysis that involves
\njob descriptions as a key component, it helps to know if 30 percent of your
\nrecords actually fail to include any job descriptions at all.<\/p>\n

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.<\/p>\n

Step 3: Leverage Existing Best Practices<\/strong><\/h3>\n

<\/strong>The need for data cleanliness is hardly unique to construction contractors. As a result, there\u2019s no need to reinvent the wheel: Existing practices in master data management (MDM) <\/strong>provide relatively painless pathways to resolving what might seem like intractable conundrums.<\/p>\n

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Take the example of one regional
\nconstruction contractor in the United States. The company, which was onboarding
\na new enterprise data warehouse, had long tracked its change orders using
\nProlog project management software. Management wanted to merge this data stream
\nwith flows from the contractor\u2019s Sage accounting system. However, there was a
\nproblem: The job-numbering systems were different. \u201cJob 1-2-3\u201d in Prolog was,
\nin Sage, \u201cJob A-B-C.\u201d<\/p>\n

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<\/a> to sync the job data, allowing us to merge the data flows and ready them for analysis.<\/p>\n

Contractors typically use one system for
\ntheir bids and another for accounting. Let\u2019s say the contractor aims to win a
\nbid with Skanska AB. It would be helpful, as part of that process, to merge
\nboth the accounting and bid-system data streams for Skanska AB. Why? Because it
\nwould yield easy analysis of prior bids as well as past project costs,
\ntimelines and results. Mapping makes this kind of thing easy to accomplish, and
\nthere are many other high-utility methodologies that are part and parcel of
\nMDM.<\/p>\n

A Solid Base for Construction Data Analytics<\/h2>\n

Data cleanliness is a prerequisite for
\neffective use of construction data analytics.<\/p>\n

In addition to improving analysis,
\nachieving progress in this area expedites major data transitions as well, such
\nas moving from one accounting system to another or acquiring another company
\nand merging its data streams with your own.<\/p>\n

Construction data analytics platforms and
\ndata warehouses can be an indispensable part of the process, which explains why
\nthis is such a fast-growing field. All told, there is growing awareness
\nin the industry in the potential for these tools to empower contractors to
\ntrack and manage bids, crews, equipment, punch lists, blueprints, requests for
\ninformation and more in easy-to-use interfaces.
\nMoving forward, AI also stands to improve risk
\nforecasting, jobsite quality-control and route-planning\/transportation. Good
\ndata hygiene allows contractors to hit the ground running as this quantum leap
\nfurther transforms the industry.<\/p>\n

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