Your company has collected data for decades. Do you know how much data you have? Or if that data is accurate and up-to-date? The question becomes, how much potential value are you losing due to inaccurate data?
If you’re asking yourself these questions, you’re not alone. Oil and gas operators have collected subsurface geological data for generations, but this data faces challenges. Often, data collects dust and goes unused. It may be inaccurate, mislabeled, or in multiple different formats. Many operators are amassing E&P data daily across multiple programs and domains without integrating them. They may be missing out on critical discoveries that could be found by connecting their data on a single platform. How can you make this data work for you and potentially save your company millions?
All of these challenges can be solved, starting with a data audit. Then you’ll know the quantity of data at your disposal, as well as the quality and accuracy of this valuable subsurface information. Beyond this, conducting a data audit and data quality assessment helps a company determine the next steps in their digital transformation. Does it make sense to use data management services? Should you follow through with a full digital transformation? The answer to these questions will become clear throughout the data audit process.
What are the Benefits of Conducting a Data Audit?
Better Decision Making
Strategic business decisions rely on accurate, up-to-date data. What if you’ve been making decisions based on bad data? IBM estimates that bad data costs US companies $3 trillion per year. For exploration companies, this means missed operational efficiencies, increased HSE incidents, and leaving money on the table from financially viable assets. A data audit will help you determine the quality of your data and inform your data quality plan going forward.
Lately, operators have combined old data with new technologies. Combining legacy data with incoming data can lead to discoveries that create value today. Old assets contain geological and geophysical information. Could you use this data combined with the technologies of today to extract more than previously thought possible? What about marrying old data with new data on one platform? Using new analysis methods for old data leads operators to return to old plays and extract more hydrocarbons.
Prepare for the Future
Knowing your data and how it’s being used is necessary to prepare your organization for cloud data management and other digital technologies. Operators can take advantage of advanced data technologies, like automation, IoT, machine-learning and big data analytics once they’ve transitioned to cloud data management. These technologies will bring greater efficiency and improved operations.
Remember: your decision-making is driven by data. Don’t you want that data to be as accurate as possible?
Consistency and Data Confidence
Another benefit of data audits is the standardization of data storage. Operators may have data residing on legacy media, different servers and programs and maybe even purchased data. The data audit allows an organization to adopt a single system of labeling, tagging and indexing. The outcome ensures final data asset reports are all consistent. Data verification becomes quick and easy once your data storage process is standardized. When you know exactly where your data is located, you can access data more quickly, and know that your data is future-proofed.
What is Data Quality?
The work doesn’t stop at the end of the data audit. Achieving quality data is an ongoing process.
Data quality refers to the fitness of a data set to serve its intended purpose. The key components of data quality are accuracy, reliability, completeness, adherence to a standard format, consistency, lack of conflict with other data values and lack of duplication. These aspects are generally agreed upon, but experts debate that further criteria are necessary to deem a set of data “high quality.”
Each company should define data quality for themselves by considering their goals, business purposes and the data in question. Once established, the guidelines will drive your data quality management plan. This plan will enhance data usability and guide your data life cycle from its collection, to indexing, analysis, and sharing within your data framework. The foundation for this framework should be the Professional Petroleum Data Management Assocation’s (PPDM’s) well-established and widely-used data model. The PPDM data model is developed and maintained by the organization to help the industry manage their subsurface data.
Data quality issues increase with volumes of data. By actively managing your data and adhering to the data quality rules set forth in PPDM’s data model, bad data can be minimized and eliminated, even as we continue to collect more data at an exponential rate.
Data quality leads to confidence. Analytic tools are only as good as the quality of the data provided. No matter the algorithm, bad data gives incorrect results. The higher quality info you can give the algorithm, the more confident you can be in discovery results.
What Happens in a Data Audit?
The process of a subsurface data audit includes validating the physical data items received, verifying ownership and devising a plan with the next steps. Which data sets should be restored and indexed in a data warehouse? Which should be discarded because they are no longer relevant? Should any of them be cleansed and made marketable to sell?
Each company’s data management audit may vary depending on their needs, but should answer these questions:
- What do we have?
- Where is it, and how do we access it?
- How is it currently being used?
- Is it worth transforming?
- How do we transform it?
- What are our data quality rules?
- Is this data secure?
Tapes, cartridges, removable disks and other legacy media are submitted to be transcribed or the data recovered, if suffering from issues like stiction. Data management experts can manage this data quality process or work alongside in-house data managers.
It’s essential to interview data managers, data scientists and other key stakeholders throughout the process to learn how they use data. It’s possible that data is hidden on individuals’ hard drives or abandoned on private servers. You’ll want to explore all potential areas where information is living.
Once you’ve determined what data to keep, you can decide on a data management plan. You might hire employees to manage it in-house or outsource it to data management experts. Data management experts can provide the right tools and a platform to manage and maintain your data and the quality, efficiently and to your standards.
Monetize your Data
If you no longer need data assets, you might be able to monetize your data by selling it to third parties. Some data is no longer relevant to your company’s goals but could be valuable to others. According to American Geosciences Institute, “the oil and gas industry has provided more than 2,500 square miles of seismic data to Louisiana universities to assist with research into the causes and effects of subsidence in coastal wetlands.” Years ago, we recognized an industry-wide demand for a seismic data marketplace. So, we created SeismicZone – the first online marketplace to sell, trade or remonetize seismic data.
No Data Audit is Fruitless
In an uncertain oil and gas landscape, now might be the right time to conduct your data audit.
Operators may be wary of undertaking new operational projects, but furthering, or beginning, your digital transformation will undoubtedly bring value. After all, with mountains of data created each day, the task will only become more arduous the longer a company waits. The transition might be difficult, but the results will pay dividends in time saved, and strategic insights revealed. At a minimum, you’ll end up more organized and clear up storage space for your future (high-quality) data.
To get started, give Katalyst Data Management a call. We provide the only integrated, end-to-end data management and consulting services specifically designed to help companies embark on their digital transformation by starting with a data audit.
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