Tag: data visualization

data mining and analysis

Data Mining and Analysis is Crucial to Your Business. Learn How!

data mining and analysisWhat is data mining?

Data mining is an analytic process which is designed to inspect large amounts of data. It helps in searching systematic relationships and consistent patterns between variables. The final goal of big data mining is the prediction. In fact, predictive data mining is one of the most common types of data mining and has the most direct business applications. The process of big data mining basically comprises three stages:

  • The initial exploration
  • Model building or pattern identification with verification and validation
  • Deployment

Are you facing difficulty in multi-dimensional data aggregation and visualization? Get in touch with our experts to check out how our data mining and analysis solutions can help.

Benefits of data mining and analysis for businesses 

Fast and feasible decisions

Searching information from the huge amount of data is time-consuming. But data mining and analysis helps to get the information much faster eliminating the possibility of making mistakes and incorrect decision making. It also helps in comparing the fetched information with various factors, so the decisions become more authentic and reliable.

Build powerful strategies

Data mining and provides reliable information which further helps in making different strategies. In other words, by analyzing information in different dimensions it becomes easier to make effective strategies and implement them. This can help companies to effectively expand their business boundaries and make authentic decisions.

Actionable Insights

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Gain a competitive advantage

Data mining and analysis help in comparing own data with that of the competitors in different aspects. Competitive analysis is an important aspect of data mining that helps businesses gain a competitive advantage. 

Our latest success story showcases an excellent example of how data mining and analysis helped a leading company in building a successful predictive model to predict future outcomes. Read the complete story here.

What are the different data mining and analysis techniques?

Classification analysis

Classification analysis is a very important data mining and analysis technique which is used to retrieve relevant and important information about data, and metadata. It categorizes datasets in different classes. In classification analysis algorithms are applied to decide how new data should be classified. An excellent example of classification analysis would be our outlook email. In outlook, certain algorithms are used to characterize an email as legitimate or spam.

Association rule learning

Association rule learning method in the data mining and analysis process helps in identifying interesting relations between different variables in large databases. This technique helps in uncovering hidden patterns in the data that can be further used to determine variables within the data and the concurrence of frequently appearing variables in the dataset. This is one of the data mining techniques that is used for examining and forecasting customer behavior and is highly recommended in a detailed retail industry analysis.

Are you wondering how to segment the data and evaluate the probability of future events? We’d love to help. Request a free proposal now to know more about our portfolio of services.

Anomaly or outlier detection

This technique in the data mining and analysis process refers to the observation for data items in a dataset that do not match an expected behavior or an expected pattern. Anomalies are also known as novelties, outliers, noise, exceptions, and deviations. Often, they provide actionable and critical information. This is one of the data mining methods that can be used in a variety of domains, such as system health monitoring, intrusion detection, fault detection, detecting eco-system disturbances and event detection in sensor networks.

Clustering analysis

Cluster analysis in data mining and analysis process is actually a collection of data objects those objects are similar within the same cluster. That means the objects are similar to one another within the same group and are unrelated or dissimilar to the objects in other clusters. Clustering analysis is the method of data mining and analysis that help in discovering clusters and groups in the data in such a way that the degree of association between two objects is highest if they belong to the same group. The result of this analysis helps in creating customer profiles.

Regression analysis

Regression analysis is the data mining and analysis technique that helps to identify and analyze the relationship among different variables. It also aids in understanding the characteristic value of the dependent variable changes. This data mining method is generally used for prediction and forecasting.

We understand the challenges faced by companies when it comes to data visualization, data mining and analysis, reducing customer churn, and increasing customer loyalty. Our data mining and analysis techniques provide best-in-class frameworks to facilitate a better understanding of new business opportunities, competitive situations, and complex challenges. Request a FREE demo for more insights!

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Big Data and Analytics

Data Analytics Services are Crucial to Your Business. Learn Why and How!

Without data analytics services, business leaders are blind, wandering onto the web like a deer on a freeway.”

To have a good data management strategy into place, underpinning all business processes, is an apparent requirement for all business professionals. But it is tougher than you would think to get the data management process right. Almost every business has a need for data-driven insights today, but many are not yet realizing that ambition. Consequently, data management tends to get disintegrated and driven by multiple stakeholders. This results in high degree of disparate and inaccurate data for organizations and they face a number of challenges in maintaining it. Additionally, with the sheer volume of data, it has become very difficult for organizations to aggregate, manage, and create value from data. Therefore, businesses need to identify a more sophisticated approach to data visualization and the importance of good quality data. Right from building better relationships with customers to overcoming external and internal data visualization challenges, there is a need to revamp and evolve enterprise data management practices.

This is where data analytics services can help. If you are looking for ways to transform raw numbers into targeted solutions by gathering and analyzing day-to-day data within your company, it’s high time that you leverage data analytics services. Also, digital analytics can help you unveil hidden correlations by segmenting your market and customizing your products or services with the help of proper data frameworks. With data analytics services, a huge amount of unstructured data become concise and comprehensive data that are helpful in modeling and predicting your market opportunities. Whether you are in the healthcare, media and entertainment, insurance or chemical industry, data analytics services have become an essential requisite for survival. With that being said, let’s take a look at what is data analytics below and how it can help your business reach the pinnacle of growth.

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data modeling best practices

3 Data Modeling Best Practices to Boost Your Business Results

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To solidify important business decisions with big data, it is important to understand data modeling. With new probabilities for businesses to easily access and analyze their data to boost performance, data modeling techniques are rapidly changing too. More than arbitrarily organizing data relationships and structures, data modeling must connect with end-user questions and requirements, as well as provide guidance to help ensure the right data is being used in the proper way for the desired results. In this article, we have explained some of the data modeling best practices that businesses must follow to improve their outcomes and save time.

Data Modeling Best Practices

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data science

How Will Virtual Reality Transform Big Data Visualization?

Data is the new gold mine for any business. Modern companies are on the constant lookout for intelligent techniques and methods through which they can better analyze the data available to them. The traditional techniques of bar graphs and pie charts have almost become obsolete, especially as the volume of data is surging. Though there are a multitude of technological capabilities that enterprises have been using to retrieve meaningful outcome from the available data, the use of virtual reality in transforming big data visualization is a relatively new concept. The huge amount of data generated by a firm can be understood only through a series of analytic steps, and visualization plays a vital role in enhancing this strategy. Take the example of how VR technology solved Goodyear’s woe through data visualization. Goodyear, a popular tire manufacturer, wanted to know why they were losing races. The company built a complete simulation of its racing tires by looking at the entire historical data set.  With the help of simulation, it was possible to check the tires’ performance in real-time for minor adjustments.Free demo

Here are some of the other ways in which virtual reality can transform big data visualization :

Dynamic analysis of data

Seeing data life-size in front of your eyes is a more interesting and engaging method to understand data when compared to the traditional ways in which data analysis is carried out. Also, interactivity is the key to understand and handle big data properly. Data visualization through virtual reality can aid in interactivity and dynamic analysis of data because you can actually reach out and touch data. Interactivity further helps in comprehending and manipulating data.

Immersive data visualizationdata visualization

Immersive data visualization is the new buzz word among data scientists. This technique is a step ahead of 3D data and is used to perceive and manipulate data in VR. This technique is especially useful in the case of large datasets. There are specific tools to immerse data visualization through virtual reality, which when coupled with haptic or kinesthetic interfaces makes it possible to capture body language, human touch, and expression, making it an entirely different art and science.

Cross-referencing data

When data is put on paper or a digital screen, we often overlook certain details. But when there is access to data visualization through VR technology companies can capably stack relevant data, pair it and create visual cues that can be cross-referenced instantly and large amounts of data can be viewed at a glance. Virtual reality will also help analyze and fine-tune the understanding of the data to make it more efficient.

 Guide the subconscious minddata visualization

The human mind is truly incredible, but sometimes it becomes difficult to process vast amounts of information. One of the most exciting things that VR technology can do is to guide the subconscious mind. The immersive features of VR make it possible for it to understand the subconscious mind. And did you know that it is not just the body that gets tired, the brain also experiences fatigue? In such situations, the technology adapts itself to areas that make it easier for the user to understand and comprehend.

Multi-dimensional data analysis

Data visualization through virtual reality helps to view data in a multi-dimensional format, which means there is not just the use of hands but ears as well in comprehending data. This enables the viewer to analyze the subject, location, and significance of a particular data source.

Natural interactiondata visualization

We use our hands to feel things around us; virtual reality allows doing this through data visualization. Companies can manipulate data streams, push windows around, press buttons and practically walk around data worlds with the help of VR technology. Consequently, it enables users to make accurate data analysis and facilitates faster decision-making process.


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Connected Cars

How Predictive Analytics is Driving the Future of the Connected Car Industry

The availability of a massive amount of data and exceptional mobile computing power has transformed the connected car technology. The traditional focus on vehicle management and infotainment systems has shifted towards developing the car’s ability to connect with the outside world. Multiple streams of communication such asFree demo IoT sensors, infotainment systems, and telematics systems in the connected car generate a colossal amount of data. Such datasets pave the way for the efficient use of predictive analytics tools to improve technologies associated with connected cars.

Ensure Passenger Safety

The AI system in a car has advanced to such a level that it can identify road signs, nearby cars, and road conditions to prevent a collision. The AI’s ability for collision prevention is augmented by employing predictive analytics in a connected car system. This can be possible by sharing vital information with nearby cars to warn each other when making abrupt maneuvers. Manufacturers are devising new ways to enhance collision avoidance systems by utilizing predictive analytics tools that utilize driver behavior data as an input.

Predictive Maintenance

The connected car system records and analyzes large data sets gathered from actuators, sensors, and machines. Such data sets can be useful in predicting requirement of full servicing, parts replacement, or other repair works. Apart from this, the data generated from connected cars can be shared with the manufacturer so that they can arrange logistics to deliver the spare parts to the required location by predicting its need.

Reinforcing Cyber Security

The idea of being able to control cars remotely may seem likely to invite security breach for malicious purpose and even terrorism. Predictive analytics is a powerful tool for identifying such cyber threats in its early stages. It utilizes user data, analyzes driver behavior to recognize patterns, and identifies behavior patterns that are inconsistent or different than the authorized user to stop unauthorized access.

Enter Quantzig:

Today, managers have access to a large stream of data, and decision-making on the basis of gut-feeling, the rule of thumb, and guessworks are largely eliminated with the advent of data analytics.

“Without big data analytics, companies are blind and deaf, wandering out onto the web like a deer on a freeway,” said a leading data analytics expert from Quantzig.

For more than 14 years, we have assisted our clients across the globe with end-to-end data management and analytics services to leverage their data for prudent decision making. Our firm has worked with 120+ clients, including 55+ Fortune 500 companies. At Quantzig, we firmly believe that the capabilities to harness maximum insights from the influx of continuous information around us is what will drive any organization’s competitive readiness and success. Our objective is to bring together the best combination of analysts and consultants to complement our clients with a shared need to discover and build those capabilities, and drive continuous business excellence.

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Oil and gas industry

Can big data revitalize the oil and gas industry by increasing overall efficiency?

The oil and gas industry is facing major challenges as the cost of extraction is on the rise and current reserves are depleting gradually. The emergence of green energy is also questioning the sustainability aspect of oil and gas industry. In a bid to remain competitive in the energy market, oil and gas industry is turning towards big data analytics to supply a low-cost energy source to the people across the world. The concept of big data is quite familiar Free demoto the oil and gas industry players. Natural gas exploration, development, and production activities generate a large amount of data including a model image of Earth’s structure, oil flow rates and pressures, and machinery performance. Here’s a look at how big data can revolutionize the oil and gas industry:

Locating Oil Reservoirs

Oil reservoirs are located 5,000-35,000 feet below the Earth’s surface. Geologists and petrophysicists use 3D seismic data that provides the picture of Earth’s subsurface; thus, eliminating the need to drill a hole to explore the rock contents. Hundreds of gigabytes of seismic data are generated, which are then processed through algorithms that create an accurate picture of the Earth’s surface.

Increasing Process Efficiency

An increase in process efficiency can result in major cost savings in the oil and gas industry. One way of doing so is by generating accurate production forecasts to determine the output of the reservoir to allocate resources optimally. The other aspect concerns using big data to analyze data gathered from machinery to ensure uninterrupted workings and minimizing breakdowns. Such equipment is fitted with sensors to collect data on its performance, which can then be compared with aggregated data to improve machine efficiency.

Optimize Distribution Network

Big data is being used by oil and gas industry players to streamline the transport, refinement, and distribution of oil and gas. All data points such as production cost, economic indicators, and weather patterns are processed using complex algorithms to determine demand, allocate resources and set end consumer pricing.

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workforce analytics

HR Analytics – Using Data to Optimize Workforce

Advancements in data analytics have changed the landscape of HR management. Organizations have adopted data driven decisions in favor of gut feeling and rule of thumb. Organizations adopting HR analytics have reported significant improvements in HR related outcomes. HR analytics is used in workforce planning, strategic planning,Free demo performance tracking, and is also finding application in multiple strategic processes. Companies are increasing their investment in building an effective HR analytics infrastructure to optimize their workforce and their performance. Here are some of the ways companies can deploy HR analytics tools to manage its human resources better:

Predictive Analytics

Every HR department possesses a massive amount of people data, and using machine learning algorithms could assist in developing a proven prediction model. Such models eliminate decisions made on the basis of gut-feeling and provide data-driven inputs to the management. Currently, only a handful of organizations are using predictive HR analytics tools but they are deriving great value from its use. These companies are using predictive HR analytics tools to forecast employee turnover, employee performance, and impact of people policies on employee satisfaction.

Performance Appraisal

Performance appraisal is an annual or biannual event in most organizations. HR analytics tools can help gather a lot of data related to employee performance, facilitating evaluations as and when required. HR metrics such as 360-degree feedback, forced ranking, human capital ROI, and net promoter score help the company to identify their workforce efficiency. Such metrics can help an organization to identify skill gaps within the workforce to take corrective action or make required strategic adjustments.

Visual HR Metrics

Companies adopting HR analytics tools can obtain real-time HR related data on their dashboard. The dashboard simplifies all numbers and data and presents it in the form of infographics to tell the complete story. Such visual data provides HR personnel with simplified information, which can be presented to the management to make strategic decisions.

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Telematics

Telematics and Data Analytics Drive Profitability in Business

Telematics and advanced data analytics solutions combine telecommunications and emerging transportation technologies with data and information processing. Telematics, in simple terms, can be defined as a technology that enables real-time tracking and monitor vehicle; thus, generating multitudes of data, which can be very useful with Free demothe help of data analytics. Telematics and data analytics drive innovation and expand market shares across several industries including insurance. Based on analytics and probability, the insurance industry can leverage advanced data analytics to gain accurate, in-depth data regarding consumer behavior, risk, and lifestyle patterns.

Telematics and Data Analytics Driving Growth in the Insurance Industry

By leveraging advanced data analytics solutions, companies in the insurance industry can eliminate guesswork and dependency on probability in calculating insurance premiums. These advance data analytics solutions enable insurance companies to offer rewards to customers, lower premium amounts, and discounts on product offerings. Insurance companies can also leverage technology to track their customer’s whereabouts and monitor their road behavior, which subsequently helps them to gain important consumer behavior information. Telematics companies sit on highly valuable information and customer insights that prove to be useful for several companies, especially the insurance industry. Organizations can sell its fleet data and consumer behavior insights to insurance companies to earn extra revenue and drive profitability.

Address Internal Issues with Advanced Analytics

Companies can leverage insights gained from advanced data analytics and telematics to improvise its internal processes, minimize costs, and drive profitability. Using advanced data analytics, organizations can visualize broad situations and find data-based insights to identify, analyze and mitigate several risks in the business processes. Telematics coupled with data analytics can create a positive impact on the businesses bottom-line growth, improve customer satisfaction, and operational efficiency.

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