21 January, 2022

GIGO: Garbage In = Garbage Out

WE’VE ALL HEARD THIS SAYING BEFORE, BUT WHAT DOES IT MEAN?

Well, bad data can massively influence engineering judgment and impede analytics, to a point where it can be harmful towards the intended outcome. In other words – data’s quality coming out of a given system or process cannot be better than what went in.

Our experience shows that most data sources suffer from issues such as:

  • Spelling and quality errors
  • Inconsistent formats
  • Fake and false data
  • Missing data
  • Duplication or repetition
  • Loss of recorded data
  • Uncontrolled copies being made

In nearly all cases, the above is caused by human error, deliberately (through misinformation) or due to poor data collection and verification methods.

In high-hazard industries, there are imperative reasons for ensuring the data we collect and record is of high quality and reliability:

  • The industry is diverse and has various unique strategic requirements
  • There are major safety and environmental implications of making the wrong decisions
  • Given the current economy, most major operators are seeking to optimize their business models whilst reducing cost. Whichever way this is achieved, the right decisions can only be made when based on accurate and reliable data

A 2011 study by Gartner Inc. identified that [1] :

  • Poor data quality is a key reason for 40% of all business initiatives failing to achieve their targeted benefits
  • Data quality affects overall work productivity by up to 20%
  • As more business processes become automated, data quality becomes the limiting factor for overall process quality

HOW CAN WE PREVENT OR LIMIT BAD DATA COLLECTION AND HANDLING?

First, we need to recognize the importance of this stage. Secondly, a clear data collection, analysis and management strategy must be deployed.  The aim is to make the process as structured and simple as possible to limit poor data collection whilst maximizing engineering judgement and the opportunity for analytics.

Example:

You want to conduct watchkeeping, regular checks, on chemical storage tank T-NK-01A which requires an operator to physically check the levels every day and record the findings on a paper form. The forms are collected weekly and transferred to a spreadsheet with the aim to plan when the next stock level is required, determine high usage periods, and when a shutdown would be most suitable.

This is open to many data collection pitfalls:

  • Failing to take the reading at the required interval
  • Taking incorrect level readings (human error)
  • Reading the wrong tank
  • Abbreviated tank details: Tank 01A
  • Date collected written in inconsistent formats MM/DD/YY vs DD/MM/YY
  • Not noting the level indication calibration number/date
  • Lost / damaged forms
  • Errors in data transfer from form to spreadsheet

Over time, this could result in wrong decisions being made on the order of stock levels and maintenance planning, ultimately impacting confidence on the data collection process across an asset.

THE SOLUTION

The operator uses a mobile device loaded with structured and controlled digital forms which only allow data in standard formats to be submitted.

The asset is geopositioned on a map so the chance of inspecting the wrong asset is removed. Digital workbooks can also be developed if a large tank farm is to be monitored. This ensures the task is targeted to the site technician.


Mobile app features:

  • Asset geopositioned on map view
  • Asset already selected on the device so no incorrect asset labelling
  • Date auto-populated
  • Mandatory fields (submission not allowed if not completed)
  • Data is updated live once submitted. No lost data or errors in transferring from paper to digital spreadsheets
  • Operator efficiency and quality is recorded for later analysis
  • Possibilities for further data collection through leveraging the use of a digital device.

Once you have structured data collection methods, the integrity of the collected data increases, along with increased confidence in decisions made based on the data analytics.

At AIE, we utilise the Veracity App to carry out infield data collection. The app was developed by AIE and is based on forms which can be created for any data collection task.

Data collected is uploaded live directly to our Veracity software platform, where auto-analytics is carried out along with dashboards presenting the data visually. Data analytics can then be used to holistically view a site, or be more detailed at equipment level, thus providing enough confidence to make key engineering decisions. The analytics and dashboards can be set up to show the required information depending on the user needs.

AIE’s industry-leading Veracity App is currently deployed across a range of heavy industries including oil and gas, petrochemical, utilities and mining.  Data is being collected to support various programs such as corrosion and chemical management, pressure systems inspection, pipeline integrity, maintenance and much more.

If you are interested to receive further details on the Veracity App or our Veracity software modules, please get in contact or visit our website.

REFERENCES:

[1] M. S. Ted Friedman, “Measuring the Business Value of Data Quality,” Gartner, Inc., 2011.

We are immediately available to support any requests or
enquires relating to our asset integrity products and services.

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