What is an example of Big Data?
A. entering and tracking a company’s daily transaction records in a spreadsheet.
B. tracking the work hours of 100 employees with a real-time dashboard.
C. providing real-time data feeds on millions of people with wearable devices.
D. sending user survey responses from various store branches to a single, central database.
Answer: option C) providing real-time data feeds on millions of people with wearable devices is an example of Big Data.
Big Data refers to extremely large data sets that cannot be processed or analyzed using traditional data processing methods. The data sets are typically characterized by their volume, velocity, and variety. In this case, the real-time data feeds from millions of wearable devices are generating massive amounts of data that must be collected, processed, and analyzed in real-time to extract valuable insights. This requires specialized tools and techniques such as distributed computing, machine learning, and data visualization.
A few more examples of big data in Accenture:
1. In the financial services industry, Accenture has used big data to help banks and other financial institutions improve their risk management strategies. By analyzing large amounts of financial data, Accenture has helped these organizations identify potential risks and make more informed decisions.
2. In the retail industry, Accenture has worked with large retailers to help them leverage big data to optimize their supply chain operations. By analyzing data from various sources such as sales, inventory, and weather forecasts, Accenture has helped these retailers improve their forecasting accuracy and reduce inventory costs.
3.In the energy industry, Accenture has used big data to help oil and gas companies optimize their exploration and production activities. By analyzing vast amounts of geological and geophysical data, Accenture has helped these companies make more informed decisions about where to drill and how to manage their assets, resulting in increased efficiency and profitability.
Overall, big data plays a critical role in many of Accenture’s client engagements, and the company has a wealth of expertise in this area.
Here are some bullets that elaborate on why providing real-time data feeds on millions of people with wearable devices is an example of Big Data:
Volume: The amount of data generated by millions of wearable devices can be massive. Wearable devices collect data on various metrics such as heart rate, steps taken, sleep quality, and more. This results in a large volume of data being generated every minute.
Velocity: Wearable devices are constantly collecting data in real-time, and this data needs to be processed and analyzed in real-time to extract meaningful insights. This requires specialized tools and techniques that can handle the high velocity of data flow.
Variety: Wearable devices can collect different types of data from different sources. For example, a smartwatch might collect heart rate data, step data, and GPS data, each with its own format and structure. This variety of data types and sources can make it challenging to process and analyze the data.
Processing requirements: Analyzing this amount of data in real-time requires specialized tools and techniques, such as distributed computing, machine learning, and data visualization. These tools allow for the processing and analysis of large datasets, enabling valuable insights to be extracted.
Value: Despite the challenges, the insights that can be gained from analyzing this data can be invaluable. For example, wearable device data can help identify patterns and trends in health and fitness, which can inform personalized healthcare and fitness plans. It can also be used for research purposes, such as identifying risk factors for certain diseases.
What is Big Data?
Big data refers to the large volume of structured, semi-structured, and unstructured data that inundates businesses on a day-to-day basis. This data comes from a variety of sources, including social media, sensors, and other connected devices, as well as traditional enterprise systems.
Big data is characterized by its volume, velocity, and variety, and traditional data processing tools and technologies are often insufficient to handle the scale and complexity of this data. As a result, organizations need specialized tools and techniques to process, store, and analyze big data in a timely and cost-effective manner.
The analysis of big data can provide valuable insights that can help organizations make better decisions, identify new business opportunities, and gain a competitive edge in the marketplace. Examples of big data analysis include predictive analytics, data mining, and machine learning, among others.