A woman works at a computer terminal reviewing multiple screens of data, charts, and graphs

What Is Data Analytics?

Data analytics is the process of turning data into insights to inform business decisions and drive success across the enterprise.

Data Analytics Key Takeaways

  • Data analytics is the methodology of collecting, processing, and analyzing data for insights to drive decision-making.

  • Artificial intelligence (AI) is accelerating data analytics by automating key steps in the data pipeline and taking on higher volumes of data.

  • To be successful, data analytics requires high-performance infrastructure either on-premises or in the cloud.

  • Intel® technology offers built-in accelerators and Intel® Select Solutions to speed time to results and value.



Overview of Data Analytics

Knowledge is power, but the value of information is limited by what you can do with it. The field of data analytics encompasses the methods of taking raw data and turning it into business intelligence (BI), ultimately to make the right decisions to meet your business goals.

Data Analytics Has Evolved in Recent Years

The exponential growth of data and data sources continues to challenge businesses, even those that have a robust analytics infrastructure in place. The growing number and types of data sources also lead to more-disparate systems—called data silos—for collecting and processing data. To keep up, businesses need to analyze data at faster rates, and data analysts need to continually evolve their skill set, or they risk leaving insights on the table.

AI Is Giving Way to New Paradigms

AI helps automate key steps in the traditional workflow of gathering, preparing, analyzing, and visualizing data, also known as the data pipeline. Because AI can work efficiently at scale, it can also help analysts unlock deeper insights and discern more-complex patterns within data than human operators alone. The potential for AI analytics holds tremendous value, but the trade-off involves more development time to build and train AI models, as well as finding AI builders who have the requisite skill set to ensure success.

Understanding the Data Pipeline

The data pipeline describes the life cycle of data as it goes from collection in a raw state, through preparation into analysis, and finally to actionable insights. While the process can vary from business to business, the core steps remain roughly the same:

  • Ingest
  • Prepare
  • Store
  • Analyze
  • Visualize

Ingest: Data Collection

Different types of data can be collected from many different sources, from interactions with customers to social media posts and multimedia such as audio and video. Structured data is narrowly defined information that fits into a predefined schema, such as numerical data in a spreadsheet. Unstructured data can include anything, from scribbles on a sticky note to an audio file.

Prepare: Data Processing

Processing data means getting it ready for analysis and modeling, either by AI or human analysts. This can take place through extract, load, transform (ELT) processes, wherein the owner of the data will structure it as needed, or through extract, transform, load (ETL) processes, which involve cleaning up data prior to use.

Store: Databases, Warehouses, and Lakes

Regardless of whether data is prepared before or after storage, businesses often need to store a tremendous amount of data either in the cloud or on-premises. Generally speaking, databases store data to support online transaction processing (OLTP) and applications, data warehouses store structured data to support online analytical processing (OLAP), and data lakes store unstructured data, also for OLAP. There are benefits and drawbacks to both data warehouses and data lakes that depend on how a business will use its data and the level of resources it can put toward its data pipeline.

Analyze: Data Modeling

The purpose of data analysis is to both make sense of data and decide what to do with it. Businesses achieve this through data modeling, which is the process of organizing and communicating the connections between data points and structures. Ultimately, data models are representations of reality.

The connections between data points fall under three categories:

  • One-to-one, as in one customer can have one account, and one account can have one customer.
  • One-to-many, as in one customer can have many purchases, but one purchase cannot have many customers.
  • Many-to-many, as in many customers can visit many venues, and many venues can have many customers.

The many types of data models in use today explore the relationships between multiple entities and their attributes to derive an understanding of how they relate. This understanding forms the basis of BI, the purpose of which can be further refined by the type of data analytics in play.

Visualize: Graphs, Charts, and Dashboards

The final step in the data pipeline is to take the synthesis of data models and convert them into easy-to-understand visualizations, charts, and graphs, which business leaders use to inform their strategy and decision-making.

How Does AI Change the Data Pipeline?

AI doesn’t change the data pipeline, but it does change the use cases that the pipeline prepares data for. Data models feed into machine and deep learning algorithms that automate much of a data analyst’s work. AI offers many benefits, including speed, consistency, and the ability to work at extreme scales of data complexity and volume beyond that of human experts.

The Four Types of Data Analytics

The four types of data analytics start with traditional methods that focus on understanding current and historical reality through data. These are known as descriptive and diagnostic analytics. The discipline of analytics continues to build on itself with advanced analytics, including predictive and prescriptive analytics, which seek to move beyond documented reality to forecast future events and trends and align possible courses of action to business goals.

  • Descriptive analytics: What happened in the past
  • Diagnostic analytics: Why the past happened the way it did
  • Predictive analytics: What will happen in the future
  • Prescriptive analytics: What is the best path forward

The Benefits of Advanced Analytics

Compared to basic analytics, advanced analytics accelerates the processing of larger volumes of unstructured data from more-diverse sources, including edge IoT devices and sensors. Businesses deploy advanced analytics to tackle more-challenging workloads such as fraud detection, sentiment analysis, and predictive maintenance for industrial equipment.

Data Analytics Use Cases

Data analytics can be applied horizontally to nearly every industry, anywhere in the world. The practice of using data to understand situations and events on a micro or macro scale means there’s an opportunity for every business to find value in the data they create. Here are just a few examples:

Intel® Technologies for Data Analytics

Successful data analytics deployments depend on hardware and software innovations that accelerate data processing while making data storage more efficient. Faster query times and faster data backup will lead to faster results and support more online transactions.

Intel® Xeon® Scalable processors deliver powerful performance for AI and data analytics workloads in the cloud and on-premises. The latest-generation processors feature built-in accelerators that offload key workloads, such as encryption and compression, to help free up CPU cycles for priority workloads such as AI.

To learn more, read the business brief, “Advance Insights with Artificial Intelligence.” Find more in-depth information on AI accelerators in the product brief, “Intel® AI Engines for Intel® Xeon® CPUs boost performance for the entire AI pipeline.”

Boost the Intelligence of Decision-Making with Data Analytics

Data analytics is a requirement to ensure competitiveness and fuel innovation. Businesses that are more proactive with using their data will be more successful than businesses that lag behind. Fortunately, Intel® technologies make it easier to deploy powerful analytics solutions with high-performance hardware built for AI and optimized software solutions. Get started with Intel-enabled advanced analytics today.

Frequently Asked Questions

Data analytics is the discipline of turning data into insights for the purposes of business intelligence and strategic decision-making. Data science explores novel ways of gathering data and developing new tools and models to make sense of data. Often, data analysts will end up using the tools that data scientists create.

Data analytics helps businesses mine their data for insights to help inform decision-making. Because data is essentially a representation of reality, data analytics gives decision-makers a stronger grasp of historical, current, and future conditions and context. Without data analytics, many businesses would rely on direct observation or intuition alone to make decisions.