What Is Data Analytics?
Knowledge is power, but the value of information is limited by what you can do with it. Today, the field of data analytics uses AI techniques such as machine learning (ML) and deep learning (DL) AI to transform structured, semistructured, and unstructured data into business intelligence (BI).
Ultimately, the desired result of using AI-enhanced data analytics is to help business leaders make the right decisions to meet their organizational goals.
Data Analytics Has Evolved in Recent Years
The exponential growth of data—from gigabytes to petabytes and beyond—continues to challenge businesses, even those with a robust analytics infrastructure. 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. Enter ML, AI, and powerful compute to collect, analyze, and extract insights from these big datasets.
The Impact of AI on Data Analytics
AI helps automate key steps in the traditional data analytics workflow, facilitating faster progress and better results at every step.
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-offs involve more development time to build and train AI models used to automate analysis and find AI builders with the requisite skill set to ensure success.
The Data Pipeline
AI-enabled data analytics are built through an approach called the data pipeline. While the process can vary from business to business, a data analytics solution will work through roughly the same core data pipeline stages:
- Data ingress, exploration, and preprocessing
- Model selection and training
- Production deployment
Data Ingress, Preprocessing, and Exploration
To begin, different types of data are collected from many different sources, such as interactions with customers, social media posts, and multimedia including audio and video. This data may be structured or unstructured. 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.
After all data is collected, the critical step of preprocessing occurs. This step encompasses preparing data for analysis and modeling, either by AI or human data scientists. 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.
Once the data is organized into a consistent format, data exploration begins. This is where data scientists try to understand the data and develop a comprehensive view of it by using statistics, probability calculations, and data visualizations such as charts and plots. To reveal patterns and points of interest, various analytics tools—including AI—help data scientists identify relationships between different characteristics, such as the dataset’s structure, the presence of outliers, and the distribution of data values.
Model Selection and Training
During this stage, data scientists rely on an AI model or algorithm to either make sense of the data via descriptive analytics or calculate a future outcome via predictive modeling. Predictive modeling is a mathematical approach used to create a statistical model to forecast future behavior based on input test data.
A data scientist could use one or more mathematical approaches—called algorithms—to get as accurate a model as necessary to answer the question at hand. Examples of algorithms include regression, clustering, decision trees/rules, time series/sequence, k-nearest neighbors, and random forests. Ultimately, the data scientist will select the models and algorithms they think will produce the best outcomes using the compute capacity available to them.
Once an algorithm is selected, data scientists will move on to training. Training essentially automates the tuning of various parameters in the algorithm that are then later used on test data to make a prediction. This tuning seeks to be as accurate as possible for data with known outcomes in what’s called supervised machine learning. A different technique called unsupervised machine learning instead relies on the algorithm to group and understand the data independently.
To expedite model selection and tuning, data scientists can use off-the-shelf models, also known as foundational models, as a starting point. These models can be customized and fine-tuned to fit specific use case needs. Overall, the process of fine-tuning a foundational model is simpler and faster than building from scratch, making it an effective way to streamline and accelerate the path to deployment.
Production Deployment
In the final stage of the data pipeline, the production deployment phase, the data scientist unleashes the trained algorithm on new data to get new results. Here, the trained model can make its classifications and predictions available to users or to other systems. Once the model is processing new data, the data scientist may still choose to optimize the model to ensure the output is as accurate and insight generating as possible.
How Does AI Change the Data Pipeline?
The introduction of AI doesn’t change the traditional data analytics pipeline, but it does impact preparation requirements. Namely, data needs to be prepared for the ML and DL algorithms that automate the complex and lengthy process of working with large quantities of data. AI brings a wide range of benefits to data analytics, 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. Advanced analytics methods, which include predictive and prescriptive analytics, 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 the best path forward is
The field of data analytics is ever evolving, as AI’s impact and adoption continues to grow. AI is enabling new types of advanced data analytics, such as:
- Cognitive analytics: Leverages semantic technologies and ML, DL, and AI algorithms to apply human-like intelligence to data analysis.
- AI-enabled analytics: Combines ML algorithms, natural language processing (NLP), and other AI applications with analytics tools to extract greater insights and understanding from complex data. AI-enabled analytics can also be used to automate analytics tasks for faster workflows and to expand data access to more people in an organization.
- Real-time analytics: Analyzes incoming data as soon as it arrives, so insights are ready for instantaneous decision-making. Many use cases like fraud detection, cross-selling opportunities, variable pricing, anomaly detection, and managing sensor data use real-time analytics.
- In-memory analytics: Uses data in memory rather than on disk to reduce latencies for faster analysis of much larger datasets. Having data in memory is also important in real-time analytics.
Advanced Analytics Solutions and Big Data
The term “big data” is used to describe very large datasets that generally include more than a terabyte of information. Big data is unstructured; high volume; high velocity, meaning it arrives in real time at high volume; and high variety, meaning it’s made up of many data formats and types. Because of its size and characteristics, big data requires ML, AI, and powerful compute to move it through the data pipeline.
Advanced analytics solutions accelerate the processing of larger volumes of unstructured data from more-diverse sources, including edge IoT devices and sensors. Businesses deploy advanced analytics solutions to tackle these more challenging big data workloads for use cases such as fraud detection, sentiment analysis, and predictive maintenance for industrial equipment.
Data Analytics Use Cases
Data analytics can be applied 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. Common ways data analytics is used include:
- Customer analysis: Data from customer behavior is used to help make key business decisions via market segmentation and predictive analytics.
- Demand forecasting: The use of predictive analysis of historical data to estimate and predict customers’ future demand for a product or service. Ultimately, this helps businesses make better-informed supply decisions.
- Anomaly detection: Identification of rare items, events, or observations that deviate significantly from the majority of the data and do not conform to a well-defined notion of typical behavior.
- People-flow analysis: Shows the movement of people as data and helps reveal hidden patterns behind behaviors.
- Time-series analysis: Provides an understanding of observed data so businesses can create a model for forecasting, monitoring, or even feedback and feedforward control.
- Social media analysis: Finds meaning in data gathered from social channels to support business decisions and measure the performance of actions based on those decisions through social media.
- Customer recommendations: Delivers personalized recommendations that suit each customer’s tastes and preferences across all their touchpoints with a business.
Organizations apply these data analytics use cases in a wide variety of industries, such as:
- Retail: Retailers can use data analytics for demand forecasting, movement-line analysis in brick-and-mortar stores, and personalized customer recommendations via email, in-store advertising, and social media.
- Manufacturing: Manufacturers can use data analytics for customer analysis and anomaly detection via computer vision inspections on a manufacturing line.
- Telecommunications: Communications service providers can use data analytics to detect network traffic anomalies and time-series analysis to forecast network congestion.
- Medical research: Researchers can use anomaly detection to improve the accuracy of medical imaging or patient data analysis to identify health risk factors that otherwise could have gone unnoticed.
Make More-Strategic Decisions with Data Analytics and Intel
AI-enabled data analytics is a requirement for organizations that want to ensure competitiveness and fuel innovation. Businesses that are more proactive in using their data will be more successful than those that lag behind.
Intel can help make it easier for businesses to deploy powerful analytics solutions with high-performance hardware built for AI and optimized software solutions.
Learn more about Intel® technologies for AI-enhanced advanced analytics today.