Retail banks use data extensively to understand how their customers manage their accounts and to help identify security risks. They are also leveraging big data in location intelligence to determine how to most efficiently manage their branch locations and set location-based performance goals.
Beyond retail banking, it’s no secret that investment banks are now very interested in using big data to understand the markets and make smart trading decisions. It can also improve and strengthen online security.
Big data can be used to predict cyber crimes like identity theft and card fraud by analyzing the transaction histories of their clients. Any strange transaction may help the bank identify any unusual purchasing patterns that could indicate a security breach. Additionally, analyzing previous patterns of cyberattacks may help businesses predict future attacks and implement strategies to minimize their impact or avoid them completely.
Various software such as SAS AML uses data analytics to detect suspicious transactions and highlight any money laundering activity. These analytical tools can also underline venture credit hazard treatment, misuse of credit/debit cards, customer statistics alteration, risk mitigation, and much more.
Other industries may also use big data to improve security. For example, the healthcare industry may use big data to predict cyberattacks that may compromise confidential patient medical information.
Today, people have access to various digital gadgets with an array of entertainment sources hence the generation of a large amount of data. Huge benefits can be extracted from this big data in the media and entertainment industry.
In the entertainment industry, customers’ behavioral data is analyzed to create detailed customer profiles that can be used to create content for different target audiences, recommend content on-demand and measure content performance.
For instance, a video streaming site may tap into an individual’s viewing history and track the type of content being consumed by them frequently. Based on that, the site may recommend videos with similar content or that are viewed mostly by a lookalike audience. By promoting content that aligns with the viewer’s interests, the site can enhance engagement and increase its revenue.
Local news channels and media houses can also utilize big data to maximize their viewership and keep the existing ones engaged. They can analyze the viewer’s content preferences so they can continue to produce programs that best appeal to their target audience.
Let’s take a look at a couple of real-time examples:
The healthcare industry can improve the quality of patient care using big data. Healthcare practitioners can track a patient’s medical history and analyze risk factors related to their illness, prescribe medical procedures and medication. Further, they can also deliver personalized care to their patients.
Data has long been crucial for doctors seeking to figure out simpler themes such as which blood pressure range is normal or how much sugar should a person consume each day. Now, the healthcare industry is using data analytics to answer bigger, more complex questions.
Another application of big data in medicine is to track and predict public health risks. For example, scientists who study epidemics may use big data to analyze the potential for the spread of disease at the local, community, regional, or even global level. Understanding the risk potential for the spread of disease may allow these scientists to develop interventions to prevent the spread of illness or treatments to reduce their impact on the population.
Going forward, data collection through devices like smartphones and wearables will be able to help doctors understand their patients at an even deeper level, which means saving money and delivering better care. For instance, Apple has come up with Apple HealthKit, CareKit, and ResearchKit. The aim is to empower iPhone users to store and access their real-time health records on their cellphones.
The education industry is flooded with massive amounts of data. The data is generally related to students, faculty, courses, results, and whatnot. The proper insights into these data can be used to improve the operational effectiveness and working of education institutes.
Many higher education institutions use big data to track instructor performance and student engagement. Colleges and universities may use an extensive database to store student information like their credit hours, grades, and GPA. They may also collect information on how often students log in to their learning and management system, how much time they spend viewing pages, and their course progress.
Higher education institutions may study instructor effectiveness based on collected data. They can identify student growth and compare student successes between instructors. This data may help institutions identify their instructors’ strengths and areas for improvement, which may help them develop programs to increase teacher effectiveness, evaluate educational standards, and refine teaching methods.
A real-time implementation of data analysis can be taken from The University of Alabama which has more than 38,000 students. There was no proper cohesive system to maintain such a vast amount of data. But now administrators are using data collection, analytics, and visualization techniques to learn and benefit from the big data.
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Increasing demand for natural resources, including oil, agricultural products, minerals, gas, metals, and so on, has led to an increase in the volume, complexity, and velocity of data that is a challenge to handle.
The manufacturing industry is switching, advancing, and changing every second, similar to the technology industry. Big data is a large contributor to this ever-changing and competitive platform as 67% of manufacturers have reported increasing their competitive nature due to big data analytics.
Big data has helped manufacturers gain a competitive advantage by increasing the efficiency of their supply chain. This includes identifying cost-effective reliable materials, improving transportation logistics, and improving the efficiency of manufacturing processes.
Since many industries are dependent on natural resources like oil, gas, metals, minerals, and agricultural products, big data can help them determine the best way to access these materials while meeting their needs for quality, efficiency, and cost.
In the natural resources industry, ingesting and integrating large amounts of data from geospatial data, graphical data, text, and temporal data allows for predictive modeling to support decision making. Areas of interest where this has been used include; seismic interpretation and reservoir characterization.
In the government sector, the most significant challenges are the integration and interoperability of big data across different government departments and affiliated organizations. Generally, governments have to deal with massive amounts of data on an almost daily basis. They usually keep track of various records and databases of the cities, states, growth, energy resources, geographical surveys, etc.
In public services, big data has an extensive range of applications. It can be used for researching public health concerns, alternative energy sources, and environmental concerns. Further applications of big data include market analyses and identifying fraudulent claims.
Big data is being used in the analysis of large amounts of social disability claims made to the Social Security Administration (SSA) that arrive in the form of unstructured data. The analytics are used to process medical information rapidly and efficiently for faster decision-making and to detect suspicious or fraudulent claims.
The Food and Drug Administration (FDA) is using big data to detect and study patterns of food-related diseases and public health crises caused by medication misuse such as the opioid epidemic. This allows for a faster response, which has led to more rapid treatment and less death.
The Department of Homeland Security uses Big Data for several different use cases. Big data is analyzed by various government agencies and is used to protect the country.
In a survey conducted by Marketforce challenges identified by professionals in the insurance industry include underutilization of data gathered by loss adjusters and a hunger for better insight. Hence, this sector also has sheer importance for big data.
Further, a study by IBM found that insurance companies are taking a business-driven and pragmatic approach to big data. The most effective big data strategies identify business requirements first and then tailor the infrastructure, data sources, and analytics to support the business opportunity.
The organizations extract new insights from existing and newly available internal sources of information, define a big data technology strategy, and then incrementally extend the sources of data and introduce new technology over time.
A report by the European Insurance and Occupational Pensions Authority (EIOPA) found that the most significant role of big data in insurance today is in pricing and underwriting. A great example of this is motor insurance, where brokers can compare individual driving behavior with a big data set to accurately predict risk and tailor policies to each motorist.
Big data has been used in the industry to provide customer insights for transparent and simpler products, by analyzing data derived from social media, GPS-enabled devices, and CCTV footage. It also allows for better customer retention from insurance companies.
In New York’s Big Show retail trade conference in 2014, companies like Microsoft, Cisco, and IBM pitched the need for the retail industry to utilize big data for analytics and other uses, including reduced fraud, timely analysis of inventory, and optimized staffing through data from shopping patterns, local events, and so on.
In addition to marketing, big data has varied uses for retailers and wholesalers. Big data can help businesses identify their current staffing needs and predict future staffing needs based on shopping habits, business growth, local events, and seasonal patterns. For example, big data may help retailers predict an increase in customers around the holiday season so they can plan to hire seasonal staff to accommodate the increased demand.
Retailers and wholesalers may also use big data to manage their inventory. For businesses that stock or sell an extensive variety of products, good inventory management is essential for tracking sales and purchases of goods. It can also help in analyzing the cost of stocking their inventory and measuring it against their profit margin for specific goods. Businesses can predict which products may become more popular or profitable based on customer buying habits and changes in manufacturing costs.
Big data from customer loyalty data, POS, store inventory, and local demographics data continues to be gathered by retail and wholesale stores. Though data has been utilized in physical stores, it has a lot of potential use when it comes to digital platforms. And it continues to be slowly but surely adopted, especially by brick and mortar stores creating their digital presence. Social media is used for customer prospecting, customer retention, promotion of products, and more.
To learn more about how Ascend can help you grow your company and succeed in the big data age, contact us today!
In recent times, huge amounts of data from location-based social networks and high-speed data from telecoms have affected travel behavior. Regrettably, research to understand travel behavior has not progressed as quickly.
Big data has many applications in industries that use tracking in transportation services. In the private sector, businesses may use big data to plan the logistics of shipping goods, which can improve speed, reliability, and cost-effectiveness while reducing the risk potential of losing or damaging goods in transport.
For example, a company can plan transportation routes based on fuel efficiency. They might consider factors like weather conditions that may impede the transport of goods, which may affect delivering products to customers or materials to manufacturers.
Governments and individuals may also use big data to improve transportation. A city may use data to plan roads by predicting traffic patterns based on the size of the population living in a particular area. It can be used to design public transportation systems to accommodate populations while maintaining cost efficiency. Individuals might use big data when they use applications like GPS to plan a travel route, avoid traffic and save fuel.
A classic example from our daily life is, Uber, It generally generates and uses a vast amount of data regarding drivers, vehicles, locations, etc. All this data is analyzed and used to predict supply, location, demand, fares that need to be set for every trip, etc.
The power and energy industry has worked with big data for years and regularly processes a significant amount of information. Either companies choose to set the infrastructure on-site and use Big Data with internal consultants or, they use it in the cloud and have the cloud provider take care of the entire infrastructure. Whichever way companies choose to go, the value Big Data brings is evident.
Big data is being used to resolve complex business problems to take strategic investment decisions. Energy companies use advanced analytics to filter their data collected from thousands of sensors and use it to make informed investment decisions. This data helps to evaluate market demand and provides all the necessary insights.
Power generation companies use advanced analytics and modeling to predict future prices and accordingly change their operational model to face the challenges. Big Data in the cloud platform allows data backup and prevents business data from any future data loss. Big data allows energy manufacturers to understand the risk profiles of their portfolios and possible opportunities, resulting in more strategic decisions.
Smart meter readers allow data to be collected almost every 15 minutes as opposed to once a day with the old meter readers. This granular data is being used to analyze the consumption of utilities better, which allows for improved customer feedback and better control of utilities use.
In utility companies, the use of Big Data also allows for better asset and workforce management, which is useful for recognizing errors and correcting them as soon as possible before complete failure is experienced.
With the increase in the amount of data passing through different channels of communication, it becomes essential to properly collect and manage it. Proper handling of data will lead to profit maximization and effective strategies for telecommunication companies.
The telco industry is using big data to improve in several key areas, including customer experience, fraud reduction, churn prediction, and dynamic pricing. And with the rollout of 5G, data plays a key role in network planning, monitoring, and management. With big data, telco no longer simply ‘dials it in’ when it comes to impactful analytics.
Transferred data can be visualized to make better management decisions and enhance customer satisfaction. Visualizing techniques that are defined by algorithms can detect fraud groups who illegally access the system through fake profiles.
Big data help telecommunication companies to differentiate target audiences and add policies according to customer segmentation. With valuable customer feedback data, it is smart enough to run predictive analytics to gauge customer preferences and interests. Analysis of the current network management and customer engagement rate becomes easy; this information can also identify the areas for improvement.
Big data is playing an important role in enhancing the performance of the agriculture sector. The main aim is to minimize the loss and increase the generation of necessary food grains. Big data has helped a lot to introduce the digital and futuristic methods of the existing agricultural traditions.
Farmers are probably not the first people who come to mind when you think of big data. But data analytics are now crucial for agriculture and they are poised to grow only more important as predicting the weather and squeezing maximum productivity out of the land become essential for feeding a growing world population.
One of the best uses of big data in agriculture is automating the watering system helps farmers to concentrate on other more critical factors. Data can be maintained and analyzed from previous years which can help find the appropriate pesticides that work best under certain conditions.
Big data smart technologies are collecting data directly from the fields while advanced algorithms are being used to predict the soil and weather conditions. Based on these analyses, agricultural activities are carried out effectively resulting in higher yields for farmers.