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Big data analytics

Big Data Analytics Framework Empowered an Oil and Gas Company to Reduce Operational Costs by 37% Through Continuous Process Improvements

The client is a leading multinational oil and gas industry player, based out of Austria. The company boasts an impressive number of 6,000+ clients worldwide and employs over 10,000 employees. The oil and gas company wanted to devise a big data analytics framework that supports data acquisition, aggregation, transformation, and cleansing to create an enterprise data management platform that will serve as a single source of truth and help make crucial business decisions.

The Business Challenge

Modern big data analytics tools and cognitive technologies have been proven to be useful in maintaining and analyzing huge troves of data sets generated by businesses across industries. In the oil and gas industry, big data analytics helps improve data management, identify and map oil reservoirs, and optimize operational costs. With the generation of huge volumes of unstructured data sets from sensors, oil and gas companies have found themselves in a fix and are looking at capitalizing on new opportunities by leveraging advanced big data analytics solutions. Apart from offering actionable insights to improve decision making, big data analytics helps oil and gas companies to leverage big data to improve recovery rates, reduce environmental impacts, and avoid accidents.

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The current data management platform deployed by the client was expensive and offered limited scalability, due to which data was being underutilized and choked in their data storage systems curtailing its processing ability and giving rise to challenges such as high processing costs, limited analytical access to data, constrained EDW capacity, and inability to process unstructured datasets. To tackle these challenges, they wanted to leverage Quantzig’s big data analytics solutions and deploy a robust data management platform to effectively store and manage the growing complexities of the unstructured data sets. 

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Solutions Offered and Value Delivered

We adopted a comprehensive three-pronged approach to help the client tackle the challenges around data storage and data processing.

Phase 1

Partnering with the client’s data management team, we undertook a detailed industry assessment to understand their current big data analytics capabilities, scalability bottlenecks, and other factors hindering growth.

Phase 2

Our big data analytics team collaborated with the oil and gas client to devise an end-to-end big data analytics framework to tackle their challenges.

Phase 3

The third phase of this big data analytics engagement focused on deploying a big data platform to help them improve their analytical capabilities, understand the customer needs better, and generate more revenue opportunity.

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Our big data analytics solutions also empowered the client to:

  • Store, maintain, and process data as they moved through various stages of the data lifecycle
  • Achieve a 37% reduction in operational costs with continuous process improvements
  • Develop a big data analytics roadmap to scale business processes and analyze unstructured data sets
  • Demonstrate significant improvement over current ad-hoc analytics capability

Why Choose Quantzig as Your Big Data Analytics Solution Provider?

The staggering volumes of unstructured sensor data have given rise to several data management challenges in the oil and gas industry. To tackle these challenges, businesses must comprehend the pieces of information and extract actionable insights. Backed with powerful big data analytics tools and over a decade of experience in working with clients across industries, our big data analytics solutions can help you do just that by breaking down data silos to unearth valuable insights to act upon.

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Top 3 Benefits of IoT Analytics That Businesses Can’t Ignore

From consumers in a digitally rich era to enterprises harnessing new technologies, everyone and everything is connected in an internet of things world. IoT analytics has made it possible for businesses to gain actionable insights that can help them improve decision making effectively. Almost all businesses today are looking for real-time data to decide their next step that delivers a competitive advantage. IoT analytics is an analytical approach that involves analyzing data generated by sensors. The potential of these sensors can be deciphered from their ability to collect data about the physical environment. These data can be then combined and analyzed with various other forms of data to identify valuable patterns that can further help businesses in different aspects such as understanding customers behavior, customer sentiment analysis, detecting fraud, and ensuring security.

At Quantzig, we understand the importance of applying analytics to the incoming streams of data and the impact that it can create for businesses when the volume of data is growing continuously. And to help companies understand and leverage IoT analytics for their business operations, our team of experts have highlighted a few important benefits of IoT analytics.

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Benefits of IoT Analytics

Benefit #1: Helps in identifying inefficiencies

Implementing IoT analytics can enable businesses to turn the big data that they gather into potential revenue. For example, If IoT analytics is used in mining businesses to monitor their processes of production, it can verify accurately whether automated mining systems are performing well or not. If performance is not as expected, it can further help in identifying the part of the process line that fails to yield the expected results.

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Benefit #2: Assist in predicting future trends

IoT analytics solutions are leveraged by many industries today to identify future trends. The energy industry is perhaps one of the major industries that is taking advantage of IoT analytics today. Internet of Things is being used in several ways to improve this industry. One such application revolves around tracking the usage of energy to create reliable estimates of demands in the future. Also, analytics in IoT can help in tracking the performance of different business functions. This is a huge benefit of IoT analytics for the manufacturing industry.

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Benefit #3: Improves customer analysis and engagement

IoT analytics can help in identifying the trends of customers buying behavior and patterns. By combining Big Data with IoT analytics solutions businesses can track their customers’ buying patterns and their preferences. Also, it can help in sentiment analysis and provide customers the services they are looking for. Additionally, it also enables businesses to promote targeted advertisements to better serve their customers by offering a personalized experience.

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How Advanced Analytics is Changing the Business Landscape in 2019?

What is Advanced Analytics?

Advanced analytics describes data analysis that goes steps ahead than simple mathematical calculations such as sums and averages or sorting and filtering. Advanced analytics leverages statistical and mathematical formulas and algorithms to recognize patterns, generate new information, and to forecast outcomes and their respective probabilities. It can be applied to several cases, ranging from the prevention of contract termination, classic customer value and success forecasts, or sales, price, and demand forecasts to new tasks such as the machine failure prediction, monitoring of social media and evaluation, and predictive policing. Advanced analytics enables both innovation and optimization. It has the potential to improve existing processes.

Are you finding it difficult to make informed business decisions, while ensuring information privacy and security? Get in touch with our analysts who can complement you with a shared need to discover and build those capabilities and drive continuous market excellence.

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What are the Different Advanced Analytics Techniques?

In advanced analytics, “advanced” refers to quantitative methods such as algorithms, statistics, and stochastic processes. All the advanced analytics techniques are not predictive, but they are future-oriented as the core idea of the techniques is to support data-driven decisions in the future. Furthermore, the advanced analytics techniques can be categorized into three functions:

Descriptive analysis

The descriptive analysis focuses on understanding an underlying process or phenomenon. The descriptive analysis has the potential to answer questions such as, “Which products do consumers usually purchase together?” or “What are the typical characteristics of customers who tend to churn?”

Predictive analysis

The predictive analysis aims at studying hidden relationships between factors and outcomes. The data derived is then used to forecast or estimate an unknown value. For example, a predictive model will allow us to estimate how much revenue will be lost if temperatures drop 10 degrees or forecast which customers are going to churn.

Simulation and optimization

Simulation copies the operation and characteristics of a process and condenses the outcome whereas, optimization prioritizes the options of decision-based on a key performance index.

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Are you struggling to manage the high volumes of data generated by high-tech sensors? Leveraging advanced analytics solutions may help. It has the potential to generate predictive insights that facilitate proactive business decisions and pre-emptive planning. Request a free proposal here to check out our portfolio of advanced analytics solutions.

 

Why Advanced Analytics Is Becoming So Important for Businesses? 

In this era of digitalization  new technologies and analytic methods are improving business operations and are helping businesses to maintain a competitive advantage in the following ways:

  • New big data technologies enable processing and analysis of large amounts of data and cost-effective storage
  • Modern and intuitive user interfaces allow user groups to derive informed insights and make better decisions
  • Advanced analytics software facilitates better analysis of collected data, relationships, and future events.

Today, modern technologies like predictive analytics accelerate and improve decisions and processes along the value chain. Therefore, you should carefully evaluate how advanced analytics can be used within your company in order to excel in the competitive landscape.

Key Benefits of Advanced Analytics

Detailed analysis helps in personalized marketing

Advanced analytics helps identify patterns from less aggregated datasets, such as at the store level or at an individual customer level. For example, advanced analytics can help businesses to capture the characteristics of every individual customer and offer him or her customized products by analyzing the pool of data. This eliminates the method of simply concluding the overall market demand of a customer segment and then offering the full audience’s favorite product. By leveraging big data analytics solutions businesses can significantly improve marketing effectiveness as well as customer satisfaction.

Better performing advanced analytics techniques improve accuracy

Most of the advanced data analytics techniques used today are more robust, faster, and accurate. For example, artificial neural networks greatly enhanced pattern recognition performance by generating a collection of functions that transform the input variables to approximate the required outcome. Predictive analytics and deep learning algorithms are applied to create more powerful hierarchies. Additionally, big data solutions allow machines to learn the complex business world in a much more flexible way. As a result, the prediction from analytical models is becoming more accurate and reliable.

Identify the level of risk in the implementation of the decision

By leveraging predictive analytics solutions, businesses can gain a confidence level for their prediction and select models based on performance measures. Furthermore, it helps companies by conveying the level of risk involved in the recommended action. Additionally, advanced analytics benefits businesses by conveying the risk level of implementing the recommended action. There are circumstances when there is a high cost for acting on false-positive or false-negative predictions. In such cases, companies can adjust the parameters of the predictive analytics model to choose not to act on low confidence predictions or avoid high-cost false prediction.

One of our recent case studies highlighted how advanced analytics helped a consumer products manufacturer to develop an aggressive growth-centric data strategy. Read the full story here.

Final Thoughts

Advanced analytics can help organizations to deal with confusions and challenges by delivering future-oriented and fact-based insights. Furthermore, it strategically extends traditional analytics solutions from being descriptive to predictive, and ideally prescriptive.

We understand the challenges companies face in managing large volumes of data and set up analytical frameworks to derive real-time insights that facilitate more informed decisions. Our advanced analytics solutions provide best-in-class frameworks for multi-dimensional data aggregation and use visualization-based data discovery tools for insight generation. Request a free demo below for more insights.

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4 Effective Ways Healthcare Data Analytics is Reducing Healthcare Costs

Despite its promising growth prospects, healthcare service providers are hard pressed to find solutions to multiple complex issues including regulatory and policy changes, medicinal and technological advancements, rising costs, staff, and trained employees, maintain efficient operations and services, and support other healthcare initiatives. With increasing concerns for living healthier, longer, and lead more active lifestyles, healthcare costs have increased. Research reveals that the spending and healthcare costs often rise at rates more than the rate of inflation and is expected to increase even more in the years to come.

With the rising healthcare costs, co-pays and deductibles have become expensive and employers are burdened to take a bigger cut of their employees’ wages to pay for insurance premiums. This surge in healthcare costs will soon become a big barrier to the growth of the overall healthcare industry. Therefore, leaders must find alternative methods to combat rising healthcare costs. They must do the appropriate research to find funding, grants, and contributors to help them conduct research, set up programs and implement processes at the pace of change.

At Quantzig, we understand the impact that costs have on your business plans. And to help companies excel in an ever-competitive marketspace, our team of experts has highlighted four effective ways in which healthcare data analytics can reduce the Contact USrising costs of care.

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5 Ways Big Data and Analytics is Revolutionizing the Food Industry

The food industry is one of the most profitable sectors across the globe. However, with the increase in global competition, companies in the food industry have started facing numerous challenges such as reducing supply chain waste, optimizing supply chain efficiency, boosting sustainable growth, implementing green policies, and finding a competitive advantage. To deal with such challenges, businesses in the food industry need to focus on leveraging big data and analytics to keep close tabs on new supply chain trends and competitors’ market progress.

Inefficient supply chain leads to low-profit margins and more wastage. Furthermore, if the supply chain is not optimized, it can result in the inability to adapt quickly to a problem and can increase operational costs. Therefore, players in the food industry must leverage the benefits of technology like big data and analytics to gain better insights into the ability to track and monitor supply chain activities in real time.

At Quantzig, we understand that to ensure the efficiency and performance of your supply chains, from sourcing to manufacturing to delivery, optimization is required. And to help companies in food industry thrive in the competitive landscape, our team of experts has highlighted five ways in which big data and analytics can help companies in the food industry to become market leaders. 

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