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Technology

How Can Retailers Harness Big Data?

Big data analytics are becoming increasingly indispensable across most industries, including retail. When retailers clearly understand their industry by leveraging key information, they can improve their marketing efforts, demand forecasting, inventory planning, and much more. However, those looking to big data for improvements must overcome certain challenges and develop specific strategies before implementing big data analytics into their businesses. With a range of potential opportunities, what are some business applications for big data in the retail sector?

What are retailers doing?

Retailers can leverage big data in several ways to gain an edge. Here are five key areas worth mentioning:

  1. Personalization — Many retailer websites feature recommendation engines that use customer preferences to tailor selection results. However, it can be challenging for these engines to make recommendations effectively without being obtrusive. For instance, generating irrelevant offers can irritate potential customers. Through big data analysis and machine-learning capabilities, retailers can use customer information to train their engines. For example, they can improve relevance by implementing a control loop that compares generated recommendations to response rates. In other words, retailers are using big data to improve personalization with increasing accuracy.
  2. Pricing — Product prices can fluctuate throughout the year, especially during high-demand periods. How do retailers adapt? Through big data analytics, retailers can employ dynamic pricing, a pricing method that automatically adjusts their prices in response to their competitors’. Retailers can use analytics software that monitors prices and creates rules so the software adjusts prices accordingly. However, if retailers don’t check or limit their dynamic pricing controls, they can become problematic; if a competitor drastically discounts a product and the retailer hasn’t set restrictions on his or her dynamic pricing system, it can devastate margins. Therefore, big data has the capacity to make real-time price matching easy and effective — but only if retailers set clear boundaries.

  3. Inventory — It’s important for any retailer to have a clear view of his or her inventory, especially if he or she oversees more than one location or channel. In the past, industry insiders relied on physical observations and manual inventorying. Now, a variety of digital touch points makes inventory management simpler, even for those with multiple channels. Big data analytics can help retailers track their inventories and understand trends, which, in turn, can help them better — and more accurately — prepare for changes in demand.
  4. Competition — As with inventory, previous competitor assessment methods were relatively low-tech. Retailers can do more than set competitor pricing alternatives based on real-time data: They can also use competitors’ success to their own advantages by monitoring which competing deals are the most profitable and localizing prices based on what competitors’ customers are buying.
  5. Sensor analytics — Through increasing Internet of Things (IoT) connectivity, retailers have more opportunities than ever to connect with and attract customers. This year, more retail owners will likely “profit from an increase in sensors and data coming from various customer-owned devices.” This, in turn, is generating greater interest among global retailers hoping to take advantage as 70% are “ready to adopt the Internet of Things to improve customer experiences.” By leveraging customer information on everything from loyalty cards to social media platforms and store apps, retailers can use these countless data points to gain an edge.

It’s clear that, when it comes to harnessing big data, there’s plenty of room for retail success. To truly unlock big data’s potential, retailers will likely invest in more IoT, machine learning, and automation technology. However, careful strategic planning and preparation are essential for any business leader planning to tap into big data. Retail executives should understand what their goals are for leveraging big data, what information they will need, and how they will turn insights into actions moving forward. Without proper planning, it is easy to become overwhelmed — and more information can hinder rather than help business operations. Data has always driven decisions: Big data technology is exciting because it drives smarter, faster, more accurate decisions based on real-time information from many, many data points.

The experts at Value Global understand the retail landscape and offer many customized IT solutions. If you’re looking to leverage smarter ways of doing better business, visit Value Global online or give us a call at 281.713.9895.
Oil and Gas

How AI Is Transforming the Oil & Gas Industry

Leveraging artificial intelligence (AI) technology is proving not just worthwhile but also transformative for those working in a multitude of industries. Oil and gas executives are no exception: Using disruptive technology applications like machine learning (ML) and predictive analytics can help industry insiders improve operations and could cut costs by $50 billion in upstream oil and gas activities alone. However, AI’s impact on the industry extends beyond cost savings, offering several potential benefits. It’s vital oil and gas executives understand what’s at stake — and what they stand to gain.

How AI shines

Big data is a core element of AI functionality, going beyond dated information buried in spreadsheets and moving toward up-to-date actionable intelligence. Oil and gas executives can collect and leverage big data in many ways to gain an edge. For example, according to the Engineering360 article, data-driven technology solutions such as wireless networks, remote sensors, and analytics software help industry insiders gather, track, and interpret data to maintain optimal production and efficiency. However, while incredibly valuable, AI goes far beyond smart data.

AI technology really shines in its potential to enhance human worker capabilities, freeing them up for greater strategic thinking and enabling smarter decision-making. Thanks to machine learning capabilities, AI-driven software can take vast amounts of unstructured data, such as seismic activity, and not only predict potential problems but also recommend solutions. In cases where data is incomplete or unreliable, AI-driven systems can use “fuzzy logic” to overcome data information gaps — essentially making sense of inconsistent data, a task that’s likely harder for humans to accomplish with the same accuracy levels. AI technology has the potential to propel the oil and gas industry into the future, reaching many aspects of the oil and gas production pipeline.

Making a business case

So, how are industry insiders employing this technology — and what are some specific use cases? More complex and seemingly popular AI applications in the industry include virtual assistants and intelligent robots. These robots, for example, are “designed with AI capabilities for hydrocarbon exploration and production, to improve productivity and cost-effectiveness while reducing worker risk.” Exxon Mobil Corp. executives are using robots to detect natural seeps on ocean floors, helping to preserve the ecosystem while they discover new resources. Others have introduced AI-driven platforms, called “cognitive workers,” that support operations in the field. These platforms, like IPSoft’s Amelia, help human workers complete tedious, hard-to-replicate, or even dangerous tasks.

Leaders of the AI software company Nervana Systems are harnessing AI to revolutionize oil exploration. They use deep learning to train an “artificial neural network how to find oil and gas using data from geoseismic studies.” Humans give examples using data sets, and the network then learns by example. Those at intelligent software technology company Kpler are geotracking energy vessels and using deep machine learning to model trends, according to the OilPrice.com article.

Oil and gas executives in other companies incorporate digital oil field technologies into their production processes. By setting up IoT field devices that measure different data points, analysts can use AI technology to gather predictive intelligence, which can help to maximize efficiency and safety by pinpointing unsuspected safety problems and predicting potential failures.

While business use cases can highlight specific benefits, what are the overall advantages oil and gas leaders stand to gain? Here are four key ways in which AI technology can positively impact oil and gas operations:

  1. Improve operational efficiency.
  2. Employ greater safety measures and tactics.
  3. Elicit more cost savings.
  4. Leverage vital predictive intelligence.

The time is now: Industry-specific AI technologies are in place for savvy oil and gas executives looking to gain an edge. The first step to leveraging AI is to harness your company’s data. When starting a data analytics practice, consider master data management (MDM), system integration, and creating a data lake. AI requires smart data; once you’ve cleaned and organized your data, you can begin your journey toward AI-driven analytics, helping you achieve more strategic operations and greater efficiencies.

For more on the latest trends and developments shaping the industry and disrupting business, or to learn more about innovative solutions, visit Value Global online.
Information Technology, Oil and Gas

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.
Information Technology

How Can You Make Your Smart Data Smarter?

There’s no denying savvy company leaders rely on data for optimal decision-making in nearly all aspects of their business. From consumer preferences to industry trends and future forecasts, smart data is taking business intelligence (BI) and interactive analysis to “sophisticated” new levels. But buyer beware: Not all data is smart data.

Information quality

One could compare effective data analytics to sifting sugar. If you don’t sift your powdered sugar, you end up with lumps in your frosting. If you don’t sift your data, it “clumps” bad data with good data, giving you poor analytic results. By sifting all your data first, you can put only the good data into your program, avoiding clumps to get the results you need. While the rise of data discovery and access to multistructured data are disrupting the traditional throw-it-all-in model, sifting through and determining the most meaningful data still presents problems for many. According to the press release, Gartner Research Vice President Rita Sallam stated, “Data preparation is one of [the] most difficult and time-consuming challenges facing business users of BI and data discovery tools, as well as advanced analytics platforms.”

Existing business data analytics models face a common problem: analytic sprawl, “an inconsistent or incomplete use of data, capricious development of metrics and formulae, and either too-restrained or unrestrained sharing of results.” And with company leaders facing information overloads, they face greater risks for drowning in a sea of uselessness and noise, overlooking what’s really important. Efficiently utilizing data without falling victim to these problems requires business leaders to identify two key factors: what information they need and how to capture it.

Determine what you need — and what you don’t

The definitions of smart or useful data types can vary based on company needs. However, many methods for determining important data are universal. Business analysts should consider what insights and information are most valuable, where or which systems store them, and why any existing analytics models have hindered data collection thus far.

For instance, ask what information will help pinpoint customer retention factors or otherwise further business goals and objectives. Analysts also recommend developing a maintenance plan. As analytic tools improve and standards change, analysts must equip systems that handle big data to adapt to those changes.

Business analysts should also consider wide data. Wide data derives information from more than just traditional sources. It may incorporate social media information, customer behavior, and IoT data.

Although certain solutions are one-size-fits-all, it’s important to keep in mind that changes analysts make to existing systems can also present limitations. Reconfiguration can be costly, especially when accounting for analytics training: Separated and scrutinized databases can cause latency, and certain analyses can slow down systems. In short, growing data types and uses have forced professionals to fundamentally rethink how they handle their data.

smart-data

Smart data, smart tools

Once company leaders understand what information they need and identify any limitations that may develop, their challenge becomes information acquisition. Luckily, according to the Gartner press release, the growing number of smart data discovery tools company leaders can use to obtain data are also getting smarter, helping users better identify key information while reducing complexity and time-consuming analysis. Analytic strategies must align with business strategies, meaning the process requires consistent communication between all parties involved.

One key process for handling big data is de-duplication, the identification and removal of duplicate records from a data set. Although removing redundant data may seem like an obvious step, it is perhaps one of the most effective ways to reduce the amount of data systems must sort through. According to the IoT Agenda article, another key step is to understand and properly sort structured and unstructured data as data arrangements are integral to those using different system types. Some systems are powerful for handling unstructured data, especially with some structured data, but not for analytics. Machine learning-enabled programs are proving very useful for sorting data and analytics. For instance, these programs can not only sift through data but can also offer valuable insights such as patterns and statistics to help analyze the data quicker and more efficiently.

The smarter your business intelligence is, the greater your ability to make savvy business decisions. Don’t assume all your data is smart. Be mindful in the ways you collect, analyze, transform and implement your actionable information — and ditch the rest.

The only constant in IT is change, and that includes the tools available to capture, filter, and interact with smart data. Contact Value Global online to understand how can you derive most value from your data.
Uncategorized

Oil and Gas Industry Leaders Leverage New Tech

Terms like “the cloud,” “big data,” “analytics,” and “the Internet of Things (IoT)” are making news, and insiders mention we’re in a new digital revolution. Why? Simply put, advanced technology developments are giving company leaders new levels of visibility, access, and efficiencies with their businesses and customers — and many are taking full advantage.

Big data describes the huge amount of information to which business leaders have access. The cloud is a means to store data in off-site servers, and analytics refers to the studying and using of the collected data. The Internet of Things (IoT), in its simplest form, refers to how computers and other machines connect and communicate with one another and allow for predictive real-time information transmission. This information and computing power is extremely valuable and has far-reaching application across industries as diverse as finance, manufacturing, and health care.

Data analytic software can take granular information and find substantial cost savings. Well-known manufacturer Caterpillar Corporation turned to IoT to dramatically increase return on investment by analyzing big data to save time, money, and — ultimately — equipment. For example, instead of running a few generators at capacity, Caterpillar employees discovered that running more generators at a lower power level saved them over half a million dollars annually. The Caterpillar Asset Intelligence platform integrates data and analytics to provide customers with fuel savings and preventive maintenance suggestions that add up to significant savings over time.

Although Caterpillar largely operates within the manufacturing and construction space, its strategy can be directly applied to the oil and gas industry, where information gathered from large, complex equipment is absolutely critical to exploration and production operations. Employing a monitoring strategy anchored in IoT, for example, would improve the integrity and efficiency of  performance data and help avoid costly shutdowns. But preventive maintenance isn’t the only way those in the oil industry can get at cost savings through data analysis.

It’s no secret oil and gas has seen a downturn in recent years. The addition of an improved, more innovative tech strategy has helped those in the business streamline processes and improve cash flow. Big data strategies in particular have been extremely effective for bottom lines, ultimately increasing profitability and helping companies to remain viable in a difficult market. In fact, Shell is already using big data to lower the cost of drilling as well as ensure machine efficiency through a “data-driven oilfield.”

Oil and gas may have been a little later to the game, but the reality is that oil and gas company leaders already overwhelmingly believe these new tech initiatives are valuable for the long-term, and most will continue to invest in big data, cloud, analytics, and the IoT to inevitably drive this industry in the near future.

Value Global experts have been immersed in the oil and gas industry for over 20 years. Our support and experience can help you leverage big data, the cloud, and the IoT to increase your efficiency and profits. We provide comprehensive oil and gas functionality in business intelligence, operations accounting, and land management fields. Contact us today so we can begin collaborating on your project.
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