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Data Analytics: What Does the Oil and Gas Industry Stand to Gain?

Big data and data analytics are changing the game for nearly every industry, and oil and gas is no different. Although some are resistant to change, some experts argue the only thing holding them back is they resist cultural change — in other words, an attitude of “if it isn’t broken, don’t fix it.” What this means, however, is companies whose leaders do take the leap are at a significant competitive advantage. For example, some oil and gas executives are harnessing Master Data Management (MDM) solutions to begin organizing and removing data from silos, putting them on a path to more mature data analytics. These leaders are harnessing data analytics to help boost production capacity, and, in some cases, are seeing ROI multiplied by as many as 50 times.

For oil and gas companies, big data is about more than just high data volumes. In addition to traditionally structured data, industrial operations generate unstructured data, which may be disjointed and nearly impossible to use without advanced analytical software. For instance, those inside some geophysical firms use unstructured seismic data to locate oil deposits while others use data for predictive analysis, which can help these professionals avoid or prepare for accidents or disasters.

Using these and other big data analytics applications is already proving essential for helping company leaders decrease costs, increase efficiencies, and reach maximum production potentials. Companies like BP equip their wells with cloud-connected sensors, each “dumping” roughly 500,000 data points every 15 seconds into a software program.

GE and BP leaders aren’t the only ones noticing big data’s advantages. A 2015 survey by Accenture and Microsoft Corp. reported that nearly 90% of respondents in the oil and gas industry believe they could increase their businesses’ values by improving their analytical capabilities. And that number will likely increase as AI and machine learning continue evolving and disrupting the oil and gas industry. But how can company leaders successfully employ data analytics and stay ahead of the curve? According to a McKinsey & Company article, here are five factors business leaders should consider:

  • Data availability — Most leaders of major oil companies have vast amounts of unstructured and structured data already at their disposal; the question now is how they can best harness it since many are underutilizing this valuable resource. In fact, according to a Master’s in Data Science article, oil and gas information “streams in from a dizzying array of sources – exploration, production, transportation and distribution,” but industry insiders often struggle to organize and leverage it.
  • Infrastructure — Many analytics tools are easy to access, and plenty of services are available to help business leaders get started on big data analytics, even in these early days of oil and gas industry implementation. According to the McKinsey & Company article, “today’s powerful tools use a combination of state-of-the-art engineering, data science, and computing power to identify superior solutions to complex production optimization problems.”
  • Analytics skills — It’s essential oil and gas executives employ skilled data scientists, creating a foundation of analytics excellence for their companies. Data team members should understand the connections between business problems and analytics solutions.
  • Redesigned work and governance — It’s also important for company leaders redesign their work processes to increase efficiency and optimize production. When it comes to analytics, they should consider end users to achieve the best results. According to the McKinsey & Company article, one North Sea operator employed data scientists to find major bottlenecks by analyzing data, pinpointing key areas ripe for process improvements.
  • Business-driven agility — IT infrastructure designers should not do everything all at once. Instead, they should build momentum for advanced analytics programs via short, metrics-driven pilot projects. From there, designers and company leaders can develop a long-term vision for how analytics can reshape their business.

While change can be costly and the unknown met with hesitation, the potential costs of production losses and operational expenses for those without smart data is too great. Savvy industry experts who invest in modern-day technologies to make optimal decisions and streamline efficiencies are already proving big data’s worth. It’s time for oil and gas leaders to embrace this transformation to not only stay ahead of the curve but also help their companies thrive.

There’s no doubt about it: Analytics are an industrial game changer. To keep pace and maximize your competitive advantage, you need to work with a trusted IT solutions provider. Visit Value Global online or contact us today to get started.

Descriptive or Predictive? How to Evaluate Your Data Maturity

How are advances such as artificial intelligence, machine learning, and big data shaking up the ways tech leaders do business? Technology disruptions are progressing at a rapid pace and will likely continue in years to come. But what are these advances, really, and how can professionals harness them for the good of their businesses? As they dive deeper into their big data, how can company leaders better position themselves for information and analytics?

Evaluating data maturity

The first step is assessing your analytics environment using a Data Analytics Maturity Model. The Maturity Model is a guide designed to help organizational leaders understand where they stand with respect to achieving intelligence from their data. Using the Data Analytics Maturity Model to assess current and necessary future business tools, tactics, and technologies can help business leaders create paths forward for successfully implementing and utilizing information analytics.

At the core of any Data Analytics Maturity Model is a visual tool for better understanding which types of analytics fall under the information analytics umbrella. Although descriptive and diagnostic analytics can and do provide useful information such as what happened and why, predictive and prescriptive analytics offer insights into what will happen and how business leaders can better plan for the future. By asking the right actionable questions about problem prevention and elimination, business professionals can develop strategies that could make information analytics an invaluable tool for competitive advantages.

Traditionally designed around five dimensions, this analytic model includes five stages of maturity. A Transforming Data With Intelligence (TDWI) assessment describes the five categories.

  1. Organization If at all, how much does your business support an information analytics program? Do you have enough organization within your company to successfully employ information analytics, or do you need to do more work?
  2. Infrastructure How advanced and coherent is the architecture supporting your information analytics initiatives? What supportive structures do you have in place, and how did you integrate them into the existing environment?
  3. Data management How extensive is your data volume, variety, veracity, and velocity, including data quality and processing?
  4. Analytics How advanced are you and fellow company leaders in analytical use? Are you taking predictive or prescriptive approaches yet?
  5. Governance How coherent is your company data governance strategy?

By assessing company strengths in each of these fields, leaders can determine how prepared their companies are to utilize information analytics.

From descriptive to predictive

Analysts assess company data maturity in stages — described by its organizational leaders’ experience with and dedication to using information analytics. Those in pre-adoption companies have little existing support for big data or are only beginning to invest in or research big data utilization. Early adopters, on the other hand, have dedicated teams for information analytics and even proofs of concept, depending on the team experience. Corporate adopters and leaders in mature companies have fully adopted and invested in big data, using prescriptive analytics for optimal decision-making. Those with full maturity are using big data to transform the ways in which they operate their businesses, using smart data in a variety of disruptive ways.

Business decision-makers who haven’t yet integrated information analytics into their businesses — those “pre-chasm” companies — may take preliminary steps to identify actionable problems and assess data capabilities. However, their maturity will restrict them to descriptive and diagnostic analytical tools. Of course, it would be problematic for those at this stage to attempt a leap to full maturity; a gradual approach is best for most. Fully mature companies such as Facebook and Amazon have arsenals dedicated to optimizing big data and often feature company cultures built around innovation that took years to cultivate.

At mature companies, leaders use information as a strategic asset and data drives continuous innovation. Business leaders implement machine learning and automation to streamline operations and they also embrace cultures of investigative insight that put new ideas into action. This imbibes a scientific decision-making process instead of an artistic one.

The maturing process requires company leaders to align business strategies with technical strategies, action-based problem solving, and customized road map development.

Advancements in information analytics are constantly evolving, and it can be difficult to keep up. If you’re not sure where to start or which stage in your maturity comes next, it could be time to partner with an expert who can guide you to analytics maturity. Contact the professionals at Value Global to get your assessment done.
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