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Trusted Data for Data-Driven Decision-Making in Construction with LoadSpring ProjectINTEL™

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Do you want to know the risk of construction materials not being delivered on time, allowing you to reschedule to avoid project delays? Should your company invest capital in fully owned earthmovers or only hire them when needed? What are the best construction market opportunities to pursue? Data-driven decision-making can help you get the correct answers.

Enterprises are now “data richer” by orders of magnitude compared to a mere decade ago. However, many are still “information poor.” Common difficulties include knowing how to leverage data and skepticism about the results of data analysis. LoadSpring ProjectINTEL™ can help you overcome these challenges by setting your data up to support you in successful decision-making. The difference is the transparency of data analysis and transformation.

Impact of Bad Data

In a world that now overflows with bits and bytes, a recent survey from Vanson Bourne/SnapLogic found that 77% of respondents did not trust their organization’s business data. Here are the three main reasons for the lack of data trust:

  1. Poor data quality and flawed data formatting
  2. Disjointed silos of data (lack of data integration)
  3. Data becoming stale before ever reaching analysis

A study by Autodesk and FMI Corp. put losses in construction worldwide due to insufficient data at as much as 14% of avoidable rework or $88 billion in 2020. Overall, the impact of bad data on construction worldwide was estimated at $1.8 trillion. That was 16% of the total revenues for an industry that accounted for 13.2% of the global GDP of $84.5 trillion.

Or, to put it another way, for every $1 billion that you might generate as a contractor, expect to lose as much as $7.1 million in avoidable rework and $165 million in general from flawed data.

Obstacles to Data-Driven Decisions

To overcome the obstacles of bad data and data distrust, you need data integrity. Good data requires a strategy for data improvement. If not, “garbage in, garbage out” will always apply. If you want to make data-driven decisions, you should be able to demonstrate that insights and conclusions from your data are valid and usable.

Additional factors affecting the reliability of your results include the diversity of your data sources. Too narrow a data selection may make it impossible to reach viable conclusions. Also, consider the freshness of your data. Shelf life can vary, and some data can go stale quickly. Software systems and data analytics applications should let you derive insights and conclusions without delay, making it easy to ingest data with varying formats and from a range of sources.

Use Your Data to Predict What Will Happen

When you know which opportunities and difficulties are likely to arise, you can prepare your projects and your enterprise for better risk management and competitive advantage. Predictive analytics uses the parameters that drive outcomes to show you the future.

For example, in a recent report by Deloitte, 75% of engineering and construction firms indicated that longer lead times or shortages of material were causing delays in projects. As a result, from January through July 2021, prices of critical construction materials rose sharply. Supply chain disruptions and volatility were identified as significant challenges for 2022.

Define the outcomes you need and use predictive analytics to see where things are currently headed. Execute the actions and measures required to get you back on track for your desired outcomes.

Get to It with LoadSpring ProjectINTEL

As part of the overall LoadSpring analytics offering, LoadSpring ProjectINTEL is a platform to facilitate your data preparation in three phases and three processes: extract – clean – build. During each phase, there is remediation and refinement.

  1. EXTRACT. In this first phase, you identify your business questions or problems and your corresponding data sources. With LoadSpring ProjectINTEL, you extract each data source into a data pond of raw data.
  2. CLEAN. Filter your data with the LoadSpring ProjectINTEL cleaner. Each data record is tagged as clean, dirty, or ignored. Dirty data can be remediated and added back for extended data integrity.
  3. BUILD. After inspection of the clean/dirty project data, LoadSpring ProjectINTEL performs data integration by building project records using the component data from each project application. The outcome is homogeneous aggregate project records in a data lake, ready for subsequent analysis.
  4. Data exploration. Identify possibilities to remediate dirty data, correct processes leading to dirty data, and refine extract-clean-build configurations.

How LoadSpring ProjectINTEL Builds Data Trust

The potential of data-driven decision-making is enormous. However, decision leaders may need evidence that they can trust the results, especially if data was previously considered untrustworthy, irrelevant, or unable to provide statistically significant results.

LoadSpring ProjectINTEL helps you overcome such skepticism, building data trust by enabling suitable data quality and formatting. It reinforces relevance by bringing data together from diverse data sources. And it supports statistical significance and predictive analytics by increasing the amount of clean, usable data to drive business decisions—all with the performance, efficiency, and expert support for which LoadSpring is legendary.

Contact us today to see how LoadSpring ProjectINTEL can contribute to your business advantage.

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