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Advanced Data Analytics: Making Your Business Smarter

Today’s analytics are going further, thanks to hardware and software designed to help organizations make better decisions.

Overview of Advanced Data Analytics:

  • As organizations grow and develop their analytics strategy, analytics changes from examining historical data to understanding the present and future.

  • Advanced analytics techniques enable new solutions across a range of industries, from manufacturing to healthcare.

  • Some of the most exciting developments include augmenting analytics with Artificial Intelligence (AI) and forecasting future outcomes using predictive analytics.



Making fully informed, data-driven decisions in a modern organization depends on data analytics. Now, the competitive advantage belongs to organizations that can access and understand large data sets. This includes unstructured and previously “dark” forms of data and information from the intelligent edge.

Moving from traditional to advanced forms of analytics can help organizations get smarter about customers, processes, and products. Accurate business intelligence (BI) and predictive modeling make it easier to explore new approaches and strategies.

The Changing Landscape of Advanced Analytics

The most-exciting frontiers of analytics no longer depend on traditional sources of data or methods of analysis. Advanced analytics has expanded the data analytics field in two key ways:

  • Focus on the future: Older analytics typically looked to the past for a greater understanding of historical data. Advanced data analytics often take a forward-focused approach, solving problems before they emerge and predicting the likely impact of business decisions in advance.
  • Broader data approach: Advances in analytics make it possible to learn more from data sources that had previously been untapped. These include images, audio, video, and IoT sensor data, using hardware and software optimized to deliver accelerated analytics performance.

As analytics capabilities become more advanced, the number of devices and quantity of data involved can scale immensely.

Why Use Advanced Analytics?

For organizations seeking more data-driven decision-making capabilities, the promise of advanced analytics is immense: collect more information, which yields more insight and can even predict the future.

However, as many companies discover when they begin to expand their capabilities, an effective advanced analytics strategy cannot be built overnight. It requires a foundation in traditional analytics and data ingestion.

Developing an organizational analytics strategy starts with understanding the maturity level of your current analytics efforts. Once you assess your level of analytics maturity, you can break through the roadblocks to achieve the next level of insight from your information. For support, see Intel’s guide, From data to insights.

Getting the most out of big data analytics requires an approach based on optimizing hardware and software for scalability, performance, and cost-effectiveness.

Advanced Data Analysis Methods

The most-advanced use cases in analytics make it possible to understand more information from more sources. As a result, insights and decisions are generated automatically in a short period of time.

  • Real-Time Analytics
    As the number of connected devices at the intelligent edge grows, so too does the demand for understanding collected data in real time. With real-time analytics, information can be processed and modeled like never before, with insights emerging as soon as new information enters the data stream.
    From improving fleet operations to ensuring patient safety, real-time analytics holds the promise of delivering insight when it can make the most difference: right away.
  • Predictive Analytics
    Using predictive analytics, businesses gain the ability to understand likely future outcomes—and optimize those outcomes to achieve their biggest goals.
    Large-scale predictive analytics require dedicated infrastructure capable of handling a full range of analytics workloads. Flexibility is key: achieving success in predictive analytics requires a data infrastructure adaptable enough to enable platforms like SAP HANA or Oracle Analytics Cloud. It should also support streaming analytics solutions and open source analytics tools like Spark.
  • Augmented Analytics
    Some of the most-promising developments from data scientists have emerged from the convergence of analytics and AI. Neural networks and machine learning have revolutionized the ability of computers to make inferences and understand a wide range of unstructured data.
    By augmenting analytics with AI, retailers can better understand how customers move through stores, and manufacturers can more easily identify defective products. Doctors can get a better understanding of medical imaging, while warehouses can enable smart inventory tracking.
    With technology designed to accelerate the development and deployment of AI at the intelligent edge, including the Intel® Distribution of OpenVINO™ toolkit,1 Intel is committed to enabling AI analytics for the world’s most innovative businesses.

Whether your organization is just beginning to explore advanced analytics or already has made significant investments in next-generation analytics technologies, Intel offers support. Our deep ecosystem of hardware, software, and partner technologies can help your data scientists achieve a higher level of analytics maturity and data-driven growth.

Intel® Technologies for Analytics

Intel® technology is changing the way modern organizations do analytics. With use cases that span many industries—and the globe—Intel works to continuously drive analytics forward while helping businesses optimize for performance and cost-effectiveness.

Getting the most out of big data analytics requires an approach based on optimizing hardware and software for scalability, performance, and cost-effectiveness.

With a broad ecosystem of technologies and partners to help businesses create the solutions of tomorrow, Intel powers advanced analytics for enterprises worldwide. From the data center to the edge, Intel works at every point in the analytics ecosystem to deliver maximum value and performance.


Frequently Asked Questions

Advanced data analytics can include a wide range of approaches, from analytics augmented with AI to real-time and predictive analytics that provide instant, forward-looking insights.

Advanced analytics offers the opportunity to understand more information at a greater level of depth than ever before. Advanced analytics can help to achieve a wide range of business goals, from streamlining operations to delighting customers.

While traditional analytics focused on understanding what had already happened, advanced analytics emphasizes the present and future. Advanced data analytics is also capable of analyzing more types of data at a larger scale than traditional analytics, including unstructured data.

Real-time analytics keep insights as current as the data stream, while predictive analytics help businesses create statistical models of likely future outcomes. Augmented analytics uses the power of machine learning and other types of AI to make analytics smarter.

Analytics transforms raw data into insight, for use in business intelligence (BI) or other applications. Analytics can drive greater operational efficiencies and improved experience across the organization.