The (Real) Cost of Bad Data for Oil and Gas Companies

The (Real) Cost of Bad Data for Oil and Gas Companies

The world has become receptive to the use of data science and analytics technologies. Oil and Gas companies are no different and have been using advanced technologies for data-driven decision-making. These companies can improve production by 6-8% with the adequate use of data technologies. These technologies are also useful in achieving higher accuracy in drilling methods and oil exploration.

However, to ensure the success of big data and analytics initiatives, the most critical pillar is the quality of data. Not all data is good and useful, there is bad data too. It refers to the data sets that have quality issues, such as inconsistencies, duplicities, or incomplete information.

According to a study, bad data can cost 15-25% revenues for many companies. What are the other implications of bad data? How can it hamper a company’s operations and profitability?  Let’s check out

Bad Data – The Real Implications

Financial losses (revenue impacts)

Loss of revenue and negative financial impacts are the direct cost implications of bad data for oil and gas companies. Data-driven decision-making includes several business operations conducted as per actionable insights from identified data patterns. With the involvement of bad data, these decisions are not correct and there is a significant time, costs, and effort put in by companies to correct the errors. The data collection and analysis procedures are executed again considering other reliable data sources to clean up the mess. Such corrections can take months to execute, and the rest of the business activities and their continuity can be impacted in the process.

IBM stated that the involvement of bad data causes an annual loss of $3.1 trillion to the US economy.

Safety and environmental impacts

There have been major safety and environmental impacts due to the incorrect data-driven decisions by the oil and gas companies. One such incident was recorded in September 2020 when the Arab ministers released warnings of oil spill disasters in the Red Sea. International and regional bodies were invited to remain cautious and maintain enhanced maritime security in the areas.

Deepwater Horizon Oil Spill is another industrial disaster that was initiated in April 2010. The incorrect decisions on the cement base and the lack of effective testing and communications were the primary reasons cited for the explosion.

As seen in these examples, the impact of disasters at oil and gas companies on the environment is irrevocable and continues to cause health, safety, and environmental hazards for several years. Oil spills can kill marine birds and animals and these are also hazardous for marine plant life. Humans do not remain shielded from such issues as health risks are evident with the contamination of the water bodies. The oil and gas companies involved in such disasters are accountable for these impacts and bear the costs of multiple lawsuits.

As data-driven decision-making becomes a norm, oil and gas companies need to ensure that only high-quality and accurate data is fed to their analytics systems.

Reputational damage

Reputational damage and deteriorated brand value also result from the involvement of incorrect decisions taken based on wrong data. The incorrect decisions negatively impact the business stakeholders. The occurrence of industrial hazards as a result of these issues can have adverse implications on the organization’s reputation.

The cost impacts result from the poor reputation in the market. The oil and gas companies can lose out on the major clients as an outcome.

Missed opportunities

The oil and gas companies can also miss out on the key opportunities. The occurrence of industrial disasters in the past, revenue losses, reduced customer trust, and poor market reputation are the factors that may provide a competitor with an edge.

Key Strategies for improved data quality

Oil and gas companies need to adhere to some of the data properties to avoid the issue of bad data. Accuracy, completeness, and consistency are the attributes that need to be maintained. A well-structured data quality program is essential to maintain high-quality data. With such a program, every oil and gas company will have the ability to standardize, monitor, and audit its procedures.

Significant change is also essential in the employees’ attitude and organizational culture. The concept of bigger data is better data is a myth. Automated systems for data analytics, data clean-up, responsible data collection, and identification of reliable sources are some aspects on which the employees shall be trained. The use of automated systems and technologies must be as per the actual requirements of the organization. Blindly following the technology trends without effective planning can do no good.

The Road Ahead

Oil and gas companies, like every other business sector, have adopted several advanced technologies to improve their operations, innovate faster, and create a competitive edge. Data analytics powered by big data and data science is one of the key technologies used by these companies to make decisions about business operations, customer strategies, marketing, and sales processes, project-related decisions, and a lot more. These technologies can backfire with the involvement of bad data. Cost implications of bad data can be huge due to incorrect decision-making causing revenue loss, increased changes and reworks, reputational damage, and industrial disasters resulting in safety and environmental issues.

It is, therefore, essential for oil and gas companies to improve the data quality through logical, administrative, and technical processes to avoid the use of bad data.