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Veracity of Big Data PDF
Veracity of Big Data serves as an introduction to machine learning algorithms and diverse techniques such as the Kalman filter, SPRT, CUSUM, fuzzy logic, and Blockchain, showing how they can be used to solve problems in the veracity domain.
We've all heard about the three dimensions of big data: volume, variety, and speed. However, Inderpal Bhandar, director of data at Scripts at Express, noted in his presentation at the Big Data Innovation Summit in Boston that there are additional factors that IT, business and data professionals need to consider the credibility of big data first. Other Big Data V calling attention at the top: validity and volatility. Here's an overview of 6V Big Data.
In We used to store data from sources such as spreadsheets and databases. Now data comes in the form of emails, photos, videos, monitoring devices, PDF records, PDF kids. This variety of unstructured data creates problems for storage, retrieval, analysis and analysis. Jeff Weiss, vice president of solutions at HP Autonomy, discussed how HP is helping organizations address critical challenges, including data diversity.
The speed of big data determines the speed at which data comes from sources such as business processes, machines, networks, and human interaction with objects such as social networking sites, mobile devices, and so on. The flow of data is huge and continuous. This real-time data can help researchers and companies make valuable decisions that deliver strategic competitive advantage and ROI if you can keep up with that speed. Inderpal believes that sampling data can help resolve issues such as volume and speed.
Big data volatility means how long the data is valid and how the data means how long the data is valid and how long it should be. In this world of real-time data, you need to determine where data is no longer relevant to current analysis.
Of course, big data is not just about volume, variety, and speed, but other issues like validity, validity, and variability as well.
Big data fidelity refers to distortion, noise, and abnormalities in data. Whether the stored and extracted data is relevant to the problem being analyzed. Inderpal, when scaling your big data strategy, you need your team and partners to work to keep your data clean and processes to prevent dirty data from piling up on your systems.
As with big data, the problem with reliability is that the data is correct and accurate for its intended use. Perfectly reliable data is the key to making the right decisions. Phil Francisco, IBM's VP of Product Management, spoke about IBM's big data strategy and the tools it offers to ensure data integrity and credibility.
Big data means huge amounts of data. Previously, it was data generated by employees. Now that data is generated by machines, networks and human interaction in systems such as social media, the amount of data to be analyzed is enormous. However, Inderpal says that data volume is not as big an issue as data volume is not as big an issue as data fidelity.