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:
- Retail: Meituan accelerated inference performance for vision–AI–based services for better online and in-person experiences.
- Medical research: Cochin University of Science and Technology used AI analytics to increase the accuracy of cancer screenings.
- Manufacturing: Novelis accelerated product design with AI analytics, which sped up prototyping and reduced the number of product iterations.
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.