Big data analytics is the process of analyzing large amounts of collected data to draw conclusions useful for technical or business purposes. This is a transformative technology that is being broadly adopted for many applications, including electronic design automation (EDA).

The modern world is awash in big data generated by many apps and gathered from many types of processes and events. Given both the quantity and diversity of this data, it is hard for people to discern patterns and trends. However, deeper insights can help people make key decisions on business strategy and process improvements.

Big data analytics automates the process of analyzing data to provide these insights. It can handle diverse datasets from many different sources. They can be well organized/structured, partially organized, or unstructured/disorganized (data lakes) and come from myriad sources, including local machines, in a data center, or the cloud. Big data analytics can yield summary statistics, technical and business trends over time, and otherwise undiscernible correlations and patterns. This approach has proven valuable across many different domains.

How Does Big Data Analytics Work?

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.

Why is Big Data Analytics Important?

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 Tools and Technologies

Big data analytics relies on a wide span of software tools—comprising sophisticated underlying technologies—to deliver the results needed to make effective data-driven decisions. Some of the key technologies in the space include:

  • Apache Flink. Open-source framework and distributed processing engine for stateful computations over unbounded and bounded data streams.
  • MongoDB. Cross-platform, document-oriented database program supporting complex queries, multi-level indexing, data aggregation, and data replication for robustness.
  • S3-compatible Object Storage. The S3 database, originally created by Amazon Web Services (AWS), is a key-value data store and one of the major categories of NoSQL databases used for accumulating voluminous, mutating, unstructured, or semi-structured data.
  • Hadoop. Open-source framework and software utilities using networks of many computers to solve computation problems involving large amounts of distributed data.
  • BigQuery. Serverless data warehouse enabling scalable analysis over huge quantities of data, with a scalable, interactive query system and built-in machine learning capabilities.
Big Data Pipeline | Synopsys

Big Data Analytics Uses and Examples

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 Challenges

Big data analytics provides many benefits, but effective deployment in any company and its infrastructure must overcome several common challenges. Choosing the right tools and technologies to perform the analysis is not always a simple process, although the guidance provided earlier is a good start.

Even the best tools cannot do their job without the big data that drives them. Massive amounts of data must be stored efficiently and properly maintained to be accessible and accurate when needed. Data must be kept free of corruption and stored in the formats best suited for retrieval and analysis by the chosen tools. Properly maintained data also makes it easier for consumption by less experienced personnel, an important benefit since hiring is challenging in this rapidly evolving field.

Security is also a significant concern for some types of big data. Customer-specific data, from sales records to field feedback, must be protected from competitors. Many types of data also have legal requirements for security, with both the data and the results of big data analysis accessible only to authorized entities.

Design.da Analytics System

Design more efficiently and effectively with the power of Machine Learning and Big Data Analytics

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Benefits of Big Data Analytics

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 and EDA

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.

Big Data Analytics and Synopsys

Synopsys provides numerous solutions that utilize machine intelligence (MI) based technologies to improve the EDA process and to optimize the resulting chips. Specifically, in the domain of big data analytics and ML, Synopsys offers two products.

Synopsys Design.da technology leverages big data analytics to seamlessly collect and process data from the hundreds or thousands of runs that constitute the evolution of a typical SoC. The solution efficiently enables comprehensive project visibility and timely insights to deliver better, more data-driven decision-making across the complete design-realization process. The Design.da solution works hand-in-hand with the Synopsys Digital Design family of products to provide unmatched levels of productivity, help to meet or exceed PPA targets, and ultimately speed customer time-to-market.

DesignDash Diagram | Synopsys

The Synopsys SiliconDash solution provides big data analytics for high-volume semiconductor manufacturing and test. It collects data from the many providers in the diverse and geographically dispersed manufacturing and test supply chain. SiliconDash technology analyzes this data and provides actionable insights to help identify catastrophic issues during the chip manufacturing and test process as early as possible. The SiliconDash solution is part of the Synopsys Silicon Lifecycle Management (SLM) family of products.

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