Machine Learning has become the most sought-after technology now to help innovate and transform businesses for efficient data analysis.
Organizations have realized the importance of data analytics in their businesses and are looking deeper into data to gain a competitive advantage. Organizations are implementing machine learning and artificial intelligence along with analytics in their daily tasks to achieve new business objectives.
Deep learning techniques can be used on a set of data to classify images, text, and speech with greater precision. This has led to the development of applications in text and speech recognition, imaging analytics, Natural Language Processing (NLP), and a few.
Organizations are starting to engage in machine-learning-based predictive analytics to move ahead of competitors in the industry. Neural networks and deep learning algorithms can be deployed to discover and utilize hidden patterns in unstructured data sets successfully. Businesses can collect data about their customers quickly, and it is essential to process and extracts real-time insights from the data promptly. Organizations need a strategy to harness large volumes of unstructured big data in near real-time and re-wire several of their business processes.
Three types of analytics:
The basic form of data analytics which collects big data and provide useful insights from historical records
Predictive analytics uses historical data, artificial intelligence, and machine learning to predict future scenarios.
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This type of analytics is a combination of business rules, machine learning, and computational modeling to suggest the best course of action for any pre-specified outcome.
Even though companies apply all the types of analytics on a data set, but predictive analytics drives more value for businesses as it helps them to anticipate future outcomes. Organizations will have to set a balance within their data, technology, and employees to transform their business into an AI-driven predictive analytics model which helps them in faster decision making. Data-driven culture coupled with leveraging advanced technologies is required to implement AI at an enterprise level. This additionally requires capital investment, infrastructure changes and employee training for flawless integration of technologies in the business.
The capabilities that drive AI and predictive analytics can be applied to almost any business domain in any industry one can think of, like securing the IT work environments, detecting cybersecurity and data security frauds, and thefts.
Organizations are currently focusing on Proof of Concepts (PoCs) and experimenting with Artificial Intelligence. These have been simple initiatives leveraging a limited set of data with well-defined outcomes. For Organizations to get real-value, it is crucial to build on a solid foundation just like how traditional software is being developed.
Data Governance processes and Data Quality is required to harness insights from the available data. Big Data involves cataloging and quality metrics before it can be used for any type of Analytics. Organizations will have to push the data on to Cloud storage because a lot of Machine Learning is happening on cloud-based scalable infrastructure.
Combining Big data with predictive analytics and cloud computing can provide essential insights accurately. Big Data Analytics and Machine Learning are allowing business to use artificial intelligence to detect, learn, and optimize operations.