Machine Learning and Big Data: Predictive Analytics Make Big Data Meaningful
Predictive analytics use data, statistical algorithms, and Machine Learning techniques to predict the likelihood of business trends and financial performance, based on the past. They bring together several technologies and disciplines such as statistical analysis, data mining, predictive modelling and Machine Learning to predict the future of businesses. For example, it is possible to anticipate the consequences of a decision or the reactions of consumers.
Predictive analytics provide insightful insights from large datasets, allowing companies to decide where to go next and provide a better customer experience. With increased data, computing power and the development of easier-to-use AI software and analytical tools, many companies can now use predictive analytics.
Artificial intelligence and Machine Learning represent the top level of data analysis. Cognitive computer systems constantly learn about the business and intelligently predict industry trends, consumer needs and more. Few companies have already reached the level of cognitive applications, defined by four main characteristics: the understanding of unstructured data, the ability to reason and extract ideas, the ability to refine expertise at each interaction, and the ability to see, speak and hear to interact with humans in a natural way. For this, it is necessary to develop the algorithmic processing of natural languages.
Machine Learning for Data Management
Faced with the massive increase in the amount of data stored by companies, companies face new challenges. Key challenges in Big Data include understanding Dark Data, data retention, data integration for better analytics, and data accessibility. Machine Learning can be very useful for meeting these challenges.
All companies accumulate over time large amounts of data that remain unused . It’s about dark data. Thanks to Machine Learning and the various algorithms, it is possible to sort through the different types of data stored on the servers. Then, a qualified human can review the classification scheme suggested by Artificial Intelligence, make the necessary changes, and put it in place.
For data retention, this practice can also be effective. Artificial Intelligence can identify data that is not being used and suggest which data can be deleted. Even if the algorithms do not have the same capacity of discernment as the human beings, the Machine Learning makes it possible to make a first sorting in the data. This saves employee’s valuable time before permanently deleting obsolete data.
Machine Learning is also useful for data integration. In an attempt to determine the type of data that they need to aggregate for their queries, analysts typically create a directory in which they place different types of data from various sources to create a pool of analytic data. To do this, it is necessary to develop integration methods to access the different data sources from which they extract the data. This technique can facilitate the process by creating mappings between the data sources and the directory. This reduces the integration and aggregation time.
Finally, data learning makes it possible to organize the storage of data for better access. Over the past five years, vendors of data storage solutions have put their efforts into automating storage management. With the price reduction of SSD, these advances in technology enable IT organizations to use intelligent storage engines based on the learning machine to see which data types are used most often and which are hardly ever used. Automation can be used to store data according to algorithms. Thus, optimization does not need to be done manually.
A poor form of AI?
Some voices rise within companies to recall that humanity is at the beginning of the development of Artificial Intelligence. Machine Learning today is a simple form of AI. The algorithms are not yet able to perform the tasks as complex asthe fictional computer network of the movie Terminator. They process images, sounds, text, algorithms perform simple tasks. It is only by interconnecting algorithms that we can create smarter systems. This is how autonomous cars are thought. Unfortunately, Artificial Intelligence players are creating their solutions in their corner. But this does not prevent the emergence of effective solutions based on simple Machine Learning algorithms.
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