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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.

What Top 2018 Tech Trends Could Impact Your Business?

We’ve hardly scratched the surface of 2018, but it’s never too early to anticipate and prepare for technology trends and developments promising to shape the future of managed services — and the world. Although many of these developments pose beneficial opportunities for tech company leaders, they may also present challenges to those not ready for impending industry changes.

Here are the biggest trends of 2018 you need to know about and how they may impact your business.

Artificial intelligence and machine learning

It’s no secret implementing smarter, faster programs into company workflows can offer sweeping benefits. From accurate, adaptable analytics to automation and beyond, some major disruptive developments entail artificial intelligence (AI) and machine learning. Gartner researchers predict having the ability to use AI will “reinvent business models” and user experiences over the next several years. Although virtual assistants like Alexa are mainstreaming AI, experts agree the greatest changes will occur through implementing narrow AI, which utilizes machine learning to fulfill specific, data-intensive tasks. For companies whose leaders utilize this technology, this means advances in predictive decision-making and augmented analytics, which use machine learning to prepare data and discover deep insights for a wide range of solutions.

Smart devices and the Internet of Things (IoT)


As with AI, advances in IoT applications promise to revolutionize analytics and networking. By increasingly and constantly connecting billions of IoT devices, business insiders are making leaps in data management and service automation. Perhaps more important, the amount of information and speed of analysis IoT offers is enabling many business professionals to work more efficiently and profitably than before. For example, truck manufacturers reduced their 180,000-truck fleet management costs by 80% thanks to versatile datasets they created using connected technology. IoT implementation benefits have prompted many industry power players, such as those at Microsoft, to invest heavily in IoT analytics, and savvy tech leaders are following suit.

Augmented world

augmented reality

It has become exceedingly clear in the last few years that virtual reality is useful for more than just video games. The technology underlying immersive digital worlds has disrupted many areas from user experiences to assistive technologies. Many major developments in augmented reality (AR), which provides visual overlays on physical objects, are proving their worth in the business arena. Gartner researchers predict that “mixed reality,” a technology that combines digital and real-world objects to create a virtual user experience while keeping users rooted in the present, is a key aspect of AR worth watching over the next five years.

Another AR-adjacent trend is the continued development of conversational platforms. Thanks to advances in AI and machine learning as well as AR, some company leaders have turned to chatbots for automating and streamlining their customer service interactions. Early conversational technology implementations were limited to highly structured, simplistic answers. However, according to the Gartner article, these platforms will likely evolve in the coming years as they become able to incorporate complex vocal and even visual data to complete user requests. More robust conversational technology stands to benefit those whose jobs span multiple industries by delivering advanced user-friendly interactive experiences.

Cloud services

Cloud services are becoming an obvious choice for many company leaders looking to streamline business operations. As a result, more and more business decision-makers are turning to MSPs, helping them to better understand, implement, and manage their cloud-based services with the added benefit of ongoing support. As a means to save costs, using public clouds is also becoming a go-to option for many. This, in turn, is driving an even greater demand for hybrid clouds. And as more business professionals better understand hybrid cloud advantages, flexibility, and value, many dub 2018 the “year of the hybrid cloud.” Based on increasing successes and demand, it’s likely cloud platforms will remain a force used for consolidating or eliminating antiquated on-premises business systems.

2018 should be an exciting year as many business processes are ripe for disruption as more and more company leaders recognize the benefits behind these technologies. Many are shifting their mindsets from the notion that these are simply buzzwords to understanding, realizing, and utilizing their value. Smart company leaders are already taking steps to corner these emerging technologies, working to innovate and increase value for their customers.

For more on the latest trends and developments shaping managed services and disrupting businesses, or to learn more about innovative managed services solutions, visit Value Global online.
Information Technology

Artificial Intelligence Is Making Automated Testing Faster and Better than Ever

In case you haven’t noticed, we’re in the midst of an artificial intelligence (AI) revolution — but not the scary Hollywood-movie kind, of course. Rather, AI is emerging with disruptive technologies like self-driving cars and mobile assistants, quickly leaving the realm of science fiction and entering into our everyday lives. Such disruptions already extend to the software development sphere and, as developers continue implementing and innovating with AI and lightning-fast software, the time will surely come for testers and developers alike to adapt.

When we think of software testing, we tend to picture a rigorous and often mind-numbing process hard on quality assurance (QA) professionals’ fingertips — and even harder on developers’ wallets. According to a report, researchers estimate developers still perform 90% of testing manually at a price tag of $70 billion, requiring two billion human-hours. And many test automation tools implemented throughout the last decade rely on virtually the same outdated workflows as manual testing without any substantial gains.

Software testing is known to take up significant time and resources. And successful advances with AI and machine learning in other industries make automation and utilization in the testing domain a no-brainer.

Automating software testing with AI

Software testing

AI-driven testing (AIDT) development and implementation allows users to leverage machine learning and smart algorithms to rapidly generate and run thousands of test scripts to report functional, performance, and security-related results. Since early 2017, leaders of several large companies have incorporated AIDT into their testing workflows. They’ve made vast improvements compared to the work traditional QA engineers produce.

Some notable strides include the following:

  • Test coverage has grown from about 50% to over 90%.
  • Scripting speeds have increased as “AI can generate 1000 scripts in a few seconds versus 3.6 million seconds for humans.”
  • Real-user representations are much more attainable.
  • False positives are “almost nonexistent.”

The goal for those developing — and using — AIDT is to reduce false-positive rates, improve efficiency, lower costs, and increase productivity. By outsourcing testing efforts to AI software, QA professionals and developers alike have more time to focus on analyzing and improving product quality. The financial savings are equally far-reaching. According to the eBook, IBM report researchers estimated bugs QA professionals discover can cost $1,500, which can rise to over $10,000 or more plus damage to company reputations if end-users discover them, compared to only $100 for bugs found early in development. AIDT productivity gains could result in millions or even billions in savings, depending on company size.

The future looks bright — and far more efficient

Software testingPrevious automated testing generations laid the groundwork for today’s AIDT systems; however, their script-by-script workflows have gone mostly unchanged over decades. This means QA engineers still create and debug scripts at slow rates relative to applications’ increasing complexities.

Combine that with human error and you have workflows that simply cannot compete with AIDT in terms of accuracy and speed. Those who continue to incorporate AI into their products, services, and systems will need to change their operations to match.

Consider the current user analytics approach. QA testers traditionally mimic user behavior based more on business analysts’ assumptions than on real user data. A thorough user behavior analysis would require immense humanpower and countless permutations — making AIDT even more attractive. AI-driven software utilizes different metrics than traditional software QA engineers might use. Deep neural networks in many systems allow software to assess vast quantities of real user data and identify potential errors based on the low-level features their algorithms detect. In other words, when it comes to representing real users, AIDT software can better predict as well as outwit and outperform existing systems with impressive accuracy by using self-learning algorithms.

AI-driven software testing may be new to many, but its benefits are already noteworthy. It’s proven to attain high test coverage with ease while simultaneously driving agile development operations. Its efficiency and accuracy — thanks in large part to its machine learning implementation — make it a cost- and time-saving measure that disrupts dated operations.

Interested in learning how automated testing can improve your business? Contact us today!
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