{"id":524,"date":"2019-09-19T12:18:11","date_gmt":"2019-09-19T16:18:11","guid":{"rendered":"https:\/\/www.seismiczone.com\/?p=524"},"modified":"2019-09-19T12:28:30","modified_gmt":"2019-09-19T16:28:30","slug":"how-data-audits-can-save-companies-millions","status":"publish","type":"post","link":"https:\/\/www.seismiczone.com\/how-data-audits-can-save-companies-millions\/","title":{"rendered":"How Data Audits Can Save Companies Millions"},"content":{"rendered":"

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\"\"Your company has collected data for decades.<\/h3>\n

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?<\/p>\n

If you\u2019re asking yourself these questions, you\u2019re 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?<\/p>\n

All of these challenges can be solved, starting with a data audit<\/a>. Then you\u2019ll 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.<\/p>\n

What are the Benefits of Conducting a Data Audit?<\/h3>\n

Better Decision Making<\/h4>\n

Strategic business decisions rely on accurate, up-to-date data. What if you\u2019ve been making decisions based on bad data? IBM<\/a> 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.<\/p>\n

Valuable Insights<\/h4>\n

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.<\/p>\n

Prepare for the Future<\/h4>\n

Knowing your data and how it\u2019s being used is necessary to prepare your organization for cloud data management and other digital technologies.\u00a0Operators can take advantage of advanced data technologies, like automation, IoT, machine-learning and big data analytics<\/a> once they\u2019ve transitioned to cloud data management. These technologies will bring greater efficiency and improved operations.<\/p>\n

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Remember: your decision-making is driven by data. Don\u2019t you want that data to be as accurate as possible?<\/strong><\/p>\n<\/blockquote>\n

Consistency and Data Confidence<\/h4>\n

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.\u00a0When you know exactly where your data is located, you can access data more quickly, and know that your data is future-proofed.<\/p>\n

What is Data Quality?<\/h3>\n

The work doesn\u2019t stop at the end of the data audit. Achieving quality data is an ongoing process.<\/p>\n

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 \u201chigh quality.\u201d<\/p>\n

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.\u00a0 The foundation for this framework should be the Professional Petroleum Data Management Assocation\u2019s (PPDM\u2019s) well-established and widely-used data model.\u00a0 The PPDM data model<\/a> is developed and maintained by the organization to help the industry manage their subsurface data.<\/p>\n

Data quality issues increase with volumes of data. By actively managing your data and adhering to the data quality rules set forth in PPDM\u2019s data model, bad data can be minimized and eliminated, even as we continue to collect more data at an exponential rate.<\/p>\n

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.<\/p>\n

What Happens in a Data Audit?<\/h3>\n

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?<\/p>\n

Each company\u2019s data management audit may vary depending on their needs, but should answer these questions:<\/p>\n