Data Automation: What It Means for Construction Managers
The task of gathering and preparing big data for meaningful analysis is easier said than done. It’s estimated that data scientists spend up to 80% of their precious time on "data wrangling"– that’s the processes of sorting, labeling, and transforming raw information into the right digital formats for analysis.
Data automation greatly accelerates tedious preparation steps like entering new project data into software like Viewpoint or Sage. This frees up valuable time to work on more sophisticated tasks such as defining business-critical objectives and interpreting the results of data analysis. Moreover, automation can exponentially boost the speed and scope of the analysis itself, generating more project visibility for PM’s into any given project estimates.
Here are a few of the ways that automation can help with each step of a data analytics project.
During the initial phase of data mining, relevant information is gathered from a diverse range of sources. Excel spreadsheets, PDF’s, change order software, you name it. Data automation at this stage involves equipment and processes that can include:
- Natural language processing (NLP) programs to automatically "read" through text files for relevant data extracts that data so that you can leverage it
- Automating the efforts behind creating charts and graphs, effectively answering the questions you have about your business
- Combining troves of Excel reports across IT, Finance, and the field
Automation can dramatically accelerate the cleaning, sorting, and warehousing of raw data. Instead of hiring data scientists to do these rote tasks, companies can use automated technologies such as:
- Construction management software that can automatically label data, as well as train and validate machine learning models
- Data integration applications that enable and schedule the bulk migration of data from legacy systems to the target database format, such as SQL
- Data validation tools that check for typographical errors and missing data values, detect invalid content and formats, and automatically transform data into the proper format
At present, data analysis persists as a complex endeavor that cannot be entirely outsourced to machines. Responsibilities such as creating and validating data models, assessing business value, and deriving meaningful insights from the results of analysis still require the human intervention of highly trained data scientists. This is especially important when it comes to layering on the intelligence gained through years of experience in the construction management software industry.
However, analytics also involves plenty of tedious, repetitive tasks well suited to the algorithmic operations of data automation. In fact, automation routines can easily augment and surpass the capabilities of human analysts in these areas:
- Automation software can be used to generate charts, graphs, dashboards, and reports for visual illustrations of data that drive insights.
- Artificial intelligence (AI) and machine learning (ML) techniques can be used to train data models to process massive amounts of information statistically and detect significant trends and patterns in data sets so it’s easy for you to identify when something is ‘out of the norm’.
- By assisting with the heavy lifting of statistical analysis, AI and ML allow data scientists to experiment with a greater number of use cases and data models, extending their capacity to discover new data-driven insights that actually pertain to business health.
- AI and ML automation can even empower not just data scientists but people with less technical training to uncover actionable insights embedded in large data sets. For example, the chief accounting officer of a construction firm can leverage automation on decades of financial reports to discover meaningful cost patterns and forecast the best strategies for increasing revenue in the upcoming year.
In short, data automation yields increased efficiency and productivity, fewer dollars spent on high-in-demand (and very expensive) data scientists, and much better ROI overall for a company investing in big data analytics.