Big Data Analytics Examples
Big Data Analytics Guide > Examples
Data Analytics Industry Examples
The scope of big data analytics reaches many different industries, with unique benefits throughout. Any business that needs to understand its target consumer more effectively to stay competitive can benefit from big data analytics. In fact, many industries which once relied on traditional tools are being reshaped by enterprise analytics and insights from big data.
The following are some useful big data analytics examples:
Big Data in the Airline Industry
Airlines collect a large volume of data that results from categories like customer flight preferences, traffic control, baggage handling and aircraft maintenance. Airlines can optimize operations with the meaningful insights of big data analytics. This includes everything from flight paths to which aircraft to fly on what routes.
Big Data Visualization Example:
Big Data Analytics Banking Examples
Big data search analytics helps banks make better financial decisions by providing insights to massive amounts of unstructured data. The information is available and analyzed when it’s most needed. The process avoids reliance on overlapping systems.It also focuses on fraud detection using big data analytics.
Big Data in Government
Government agencies face a constant pressure to do more with less resources. Public safety agencies are expected to combat crime and budgets do not always rise in conjunction with crime rates. Big data analytics allows law enforcement to work smarter and more efficiently. It is also used for combating census undercount. And it allows any government agency to streamline operations and better target resources for maximum results.
Bigger Data, Better Insights
Examples of Big Data Analytics in Healthcare
The volume of patient, clinical and insurance records in healthcare generates mountains of data. Data analytics lets hospitals get important insights out of what would have been an unmanageable amount of data. The ability to extract useful information out of structured and unstructured data can lead to better outcomes in patient treatment and organizational efficiency.
Big Data in Manufacturing
The supply chains of manufacturing are complex and big data analytics allows manufacturers to better understand how they work. Machine learning big data analytics give companies a competitive edge by facilitating advanced problem solving in every area. It is also used for preventative maintenance of equipment, such as detecting anomalies before a failure.
Big Data in Retail
With big data analytics, retailers are able to understand customer behavior and preferences better than ever before. Transaction data based on buying habits allows retailers to cater to specific customer demands. Advanced analytics of customers gives retailers the ability to predict trends and create more profitable products.
Big Data in the Sciences
Clinical research trials commonly fail, even after using a lot of resources and time. Big data visual analytics provides the insights researchers need to try more trials faster. It allows for automated solutions that affect speed and efficiency.
Big Data Analytics Examples in Education
Today, big data helps educators go far beyond keeping A's, B's and C's in a book, tracking information across schools, districts, and states such as performance, background, behavior and emotional learning. By collecting big data from many channels, educators can use tools for big data analytics to develop personalized education goals and plans based on student performance on assignments and tests.
Big data also helps educators gain student attention and track performance to provide personalized feedback. By customizing approaches and programs, schools and educators can minimize attrition and maximize results using big data analytics.
Examples of Big Data Analytics in Sports
Big Data enables sports companies and teams to make better predictions, remain up to date on performance, and stay on course while in play. Results are what moves the professional sports industry, and they are only achievable with detailed, pooled data on player performance, stats, and comprehensive skills. For example, Liverpool FC has successfully dominated rivals with data science in recent years, with data science influencing Liverpool coaching decisions in real-time.
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Data Analytics Dashboard Examples
Looking through data analytics dashboard examples can help a team select data software that meets their visualization and data analytics needs. Given the breadth of big data that most companies deal with today, data dashboard software is essential for analyzing, monitoring, and controlling even internal activities, let alone big data about competitors or customers.
Ideally, a good data dashboard example should be easy to use, simple to read and understand, and be generated by a tool with self-service capabilities. This empowers even non-technical users to create their own visualizations. Furthermore, the data analytics dashboard tools should allow easy communication, integration, customization, and portability.
These data analytics dashboard examples illustrate how professional dashboards should appear, providing simple, clear information such as the KPIs and metrics management needs to make decisions through data visualizations. Each of these data analytics dashboard examples answers key business questions with simple, meaningful information at a glance.
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Big Data Analytics Applications Examples
Time is absolutely essential in defense and intelligence, where milliseconds can make or break the mission. Real-time defense big data analytics offer a distinct tactical advantage for military operational readiness. Big data analytics has a natural role in defense, because intelligence from modern public sector big data is time-sensitive.
This is why the Intelligence and Department of Defense (DoD) communities gather such extensive military big data, including IoT sensor data, logistics, social media data, and signals metadata. Yet the scale of this data means it is too massive for real-time visualization or analysis. Existing GEOINT software solutions and military geospatial technology keep pace with the proliferation of big military data.
Big data analytics applications should offer full rendering support for visualization. And to ensure actionable IC & DoD data science insights they must deliver sufficient acceleration to allow analysts to layer geospatial data as it arrives and upgrade their GIS for defense and intelligence.
As the public sector struggles to move online and adapt to pandemic working and service delivery conditions, the gap between legacy data analytics tools and the pace of incoming streaming data has become more apparent. But an inability to analyze public sector data from the census, Internet of Things (IoT) sensors, social media, public health sources, and imagery data sources only leads to increased costs and hardware footprints, delayed time to insight, and inaccurate assessments and policy decisions.
Another good big data analytics application example is the modern telecommunications industry. From customer churn management to robocall fraud, there is no shortage of use cases that require data infrastructure and analytics for telcos to get ahead.
For each of these examples, the right application of big data analytics can help visualize complex polygons and billions of points at unparalleled speeds, and perform advanced SQL queries with a click. Big data analytics applications also allow users to parse the depths of data sets in near real-time for exploratory data analysis and visualization, accelerated data science workflows, and interactive defense geospatial intelligence at scale. Even at public sector scale, the right big data analytics applications can produce sufficient GPU-acceleration and rendering to deliver fast and interactive geospatial visualization with insights.
The HEAVY.AI Advantage
Compare what users can accomplish with HEAVY.AI, a user experience purpose-built for modern data, versus common business intelligence software with a backend from industry-standard data warehouses. For example, HEAVY.AI versus a Tableau frontend paired with a Snowflake backend, two prevalent technologies in the data & analytics marketplace which resemble a legacy approach with disparate technologies for visualization and computation.
Adjusting technical details to define our HEAVY.AI, Tableau and Snowflake environments and starting at the same place, how quickly and easily can users build a dashboard in HEAVY.AI versus Tableau and Snowflake, without compromises?
HEAVY.AI delivers much greater speed and performance, including instant interactivity with the most extensive set of observational data in chart, map, and graph form in seconds, driving action and faster decisions. All of these differences mean less time to value and an accelerated process of loading data, building maps, charts, and graphs, visually analyzing and exploring information, and moving on to other essential tasks. This reduces overall cost substantially—in the realm of $387 per hour before Tableau licensing for the Tableau with Snowflake solution compared to $43 per hour for HEAVY.AI with licensing included.
