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Big data analytics uses a wide variety of techniques to examine and study the datasets. The most familiar method is data mining, which searches and analyzes the data to discover and extract patterns. This step is often followed by knowledge discovery in databases (KDD), which ties closely to the underlying structure of the data and data management techniques, including parallel and distributed databases.

Much of the analysis performed during big data analytics leverages advances in artificial intelligence (AI), including intelligent searches, natural language processing (NLP), neural networks, machine learning (ML), and deep learning (DL).

Big data analytics aims to produce results quickly, as close to real-time as possible, and update these results as new data is gathered. As the data collected evolves, this keeps the insights current. Therefore, decisions can be automatically re-evaluated and adjusted in response to new data and insights.

Company leaders face countless decisions about what products to develop, how to market and advertise them, the best way to maximize profits, and the type of organization and individuals needed to achieve its goals. Given an increasingly complex market landscape, these critical decisions should be data-driven and not entirely based on instinct or experience. In this increasingly digital world, there is no lack of data available. However, figuring out how to use this data effectively in decision-making is challenging.

This is precisely where big data analytics plays a crucial role. Big data analytics systems and software analyze diverse datasets to find patterns, correlations, and trends. This insight is valuable in guiding data-driven decisions for all aspects of business strategy. With the best possible decisions using properly analyzed data, companies can produce better products, create product variants for new market opportunities, compete more effectively, increase revenue, and profit, and run their business more efficiently. 

Big data analytics can help companies improve many aspects of their business and operations. With the insights provided by the analysis, company leaders can make crucial, data-driven decisions. Examples of these decisions include:

• Focused product development, including gauging product viability and lifespan, making enhancements to benefit customers, and identifying new target markets.
• More effective marketing, including targeted ads and other personalized campaigns that speak to customer needs.
• Better customer acquisition and retention through experience personalization and increased customer satisfaction.
• Financial results optimization, including optimal pricing models, effective inventory control, and proactive management of supply chain networks.

Big data analytics leverages the vast amounts of data collected through various channels to provide actionable insights. Users can recognize trends, predict future data values, recommend changes or new ways of operation, automate processes, reduce costs, and optimize processes and products. This is precisely why big data analytics is such transformative technology. Critical decisions can happen more quickly, accurately, and agilely than a manual, ad hoc analysis of limited data. For big data analytics users, this can translate into better products that draw more customers and drive more business.

Big data analytics enables chip development teams to streamline the design and verification process, optimize the power-performance-area (PPA) attributes of semiconductor devices, improve manufacturing costs and yield, and extend the lifetime of end products. Examples of technical and business benefits include:

• Co-optimizing chip architecture and software for PPA based on previous results from current or related projects.
• Accelerating design debugging by identifying patterns and correlations in design and verification results.
• Optimizing design PPA during implementation (logic synthesis, floorplanning, placement, and routing) based on previous runs.
• Accelerating time-to-market to beat the competition and maximize revenue.
• Improving the manufacturing process using feedback from chip testing and devices in the field.
• Getting early warning of reliability and aging issues from trends across the full range of chips deployed in the field.