Intelligent Data Exploration in SAS: An AI/ML Perspective
Data exploration is a key part of any data science project, but it‘s especially crucial in artificial intelligence and machine learning initiatives. Before even considering AI algorithms or models, data scientists need to thoroughly understand the data they‘re working with. What are its characteristics, quirks and quality issues? How is it structured and how can it be transformed for analysis? What insights and patterns does it hold?
Thorough data exploration upfront can make or break an AI project. Issues like missing values, outliers, inconsistent formats or biased data can severely degrade model performance if not caught early. Exploration also surfaces key relationships and guides feature engineering efforts. According to a survey by Anaconda, data scientists spend nearly 40% of their time on data preparation and exploration tasks.
SAS has long been a leader in data management and analytics, and it offers a robust set of tools for data exploration. The SAS data step and proc sql are two foundational components for data transformation and analysis. By leveraging these along with other SAS procedures and some AI techniques, data scientists can efficiently explore even large and complex datasets in preparation for machine learning.
Exploring Big Data with SAS
In the era of big data and AI, datasets are growing larger and more diverse. It‘s not uncommon to work with tables containing millions of rows and hundreds of columns, often including unstructured or streaming data. Traditional methods of exploring data manually or with simple queries can be too slow and cumbersome for these big datasets.
Fortunately, SAS is well-equipped to handle large-scale data exploration. The SAS data step can process datasets far exceeding available memory by reading and writing data in chunks. Proc sql can also work with tables larger than memory using the outobs= and inobs= options to process subsets. Hash tables are another useful data step feature for optimizing queries on big data.
For truly massive datasets, SAS offers high-performance analytics options like in-memory processing with LASR, grid computing with SAS Grid Manager, and integration with Hadoop using SAS/ACCESS. These technologies enable distributed processing and faster queries on big data.
Another approach is to use intelligent sampling techniques to create representative subsets of big data for exploration. Methods like stratified sampling and oversampling of rare events can create manageable samples that still capture key patterns. SAS Viya offers automated sampling and data partitioning as part of its data preparation tools.
Exploring Unstructured Data
AI and machine learning often involve unstructured data like text, images, audio and video. Exploring these complex datatypes requires different techniques than structured tables and columns.
For text data, a common approach is to first convert the unstructured text to a structured format using natural language processing (NLP) methods. This could involve tokenization to break text into words, stemming or lemmatization to standardize word forms, part-of-speech tagging, named entity recognition, and sentiment analysis. The resulting structured representations of the text can then be analyzed with standard data exploration techniques like frequency counts, co-occurrences and topic modeling.
SAS offers a variety of tools for text mining and NLP, including SAS Visual Text Analytics, SAS Text Miner and SAS Contextual Analysis. These enable text exploration methods like:
- Word clouds and frequency counts to visualize common terms and phrases
- Concept maps and network diagrams to show relationships between terms
- Sentiment analysis to quantify emotion and opinion in text
- Topic modeling to discover latent themes across documents
- Text clustering and classification to group similar documents
Images, audio and video can similarly be transformed into structured features using deep learning techniques like convolutional neural networks (CNNs). The extracted features can be explored to understand distributions, clusters and relationships before being fed into downstream ML models. SAS Viya supports deep learning for computer vision and image analysis through its DLPY API.
The Role of Data Exploration in the AI Lifecycle
Data exploration is a key component of the larger data science and AI lifecycle. It‘s typically one of the first steps after data ingestion and cleaning, and before feature engineering and modeling.
The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which has been widely adopted in the data science community, breaks the lifecycle into six phases:
- Business Understanding
- Data Understanding
- Data Preparation
- Modeling
- Evaluation
- Deployment
Data exploration spans the Data Understanding and Data Preparation phases. The goal is to get to know the data, understand its structure and quality, and begin to identify patterns that could be useful for modeling. This informs the subsequent Data Prep steps to clean, transform and engineer features from the raw data.
Exploration doesn‘t stop after the initial Data Understanding phase though. It‘s an iterative process that continues throughout the lifecycle as new data is acquired, models are refined, and insights are generated. Advanced analytics and machine learning often uncover new relationships that invite further exploration in a virtuous cycle.
SAS vs Open Source for Data Exploration
When it comes to data exploration, SAS is often compared to open source tools like Python and R. All three offer capabilities for data manipulation, statistical analysis and visualization. However, there are some key differences.
One advantage of SAS is its ability to handle large datasets efficiently. SAS was originally designed for data management and processing large files, while Python and R were conceived as programming languages and interactive tools. SAS also offers high-performance and distributed computing options for big data that are more integrated than open source tools.
Another strength of SAS is its rich library of statistical procedures and algorithms. Procs like means, univariate, freq, corr, glm and logistic offer a wide range of exploration and analysis methods. Python and R rely more on user-contributed libraries which can be inconsistent in their interfaces and documentation.
However, Python and R generally offer more flexibility and expressiveness for data manipulation and transformation. Packages like pandas, dplyr and tidyr make it easy to perform complex operations on data frames with concise, readable code. SAS data step and proc sql are powerful but can be more verbose.
Python and R also offer better integration with modern machine learning and deep learning frameworks like scikit-learn, TensorFlow and PyTorch. While SAS now offers APIs for calling these from SAS code, it‘s often easier to do all the work in Python or R.
Ultimately, the choice of tool depends on factors like existing skills, IT environment, and project requirements. Many data scientists today are bilingual, using a combination of SAS, Python and R depending on the task at hand. The key is to leverage the strengths of each as needed in the data exploration and analysis workflow.
Automating Data Exploration with AI
As datasets grow larger and more complex, manually exploring every variable becomes infeasible. This has led to the development of automated data exploration tools that use machine learning to surface relevant insights faster.
SAS Viya offers a variety of automated exploration and insights capabilities, including:
- Automated data profiling to detect data types, missing values, cardinality, min/max and other characteristics
- Univariate and bivariate analysis to visualize distributions and detect correlations and outliers
- Variable clustering to group similar features
- Automated analysis and natural language insights generation
- AI-powered recommendation engine to suggest relevant exploration steps and visualizations
By automating rote exploration tasks and augmenting human intuition with AI and ML, data scientists can get to insights faster and have more time for value-added analysis.
The Future of Data Exploration
As AI continues to advance, the way we explore and interact with data will evolve as well. Some key trends shaping the future of data exploration include:
- Conversational interfaces and NLP to enable natural language queries and insight generation
- Augmented analytics and automated machine learning (AutoML) to guide users through the data science workflow
- Explainable AI techniques to make ML models and insights more interpretable and trustworthy
- Continuous, real-time data exploration on streaming and IoT data
- Interactive, immersive data visualization using AR/VR to better understand complex datasets
- Collaborative, cloud-based data exploration platforms for sharing insights across teams
SAS is investing heavily in these areas to reimagine how we explore data in the AI era. Capabilities like conversational AI, augmented analytics, and immersive visualization are all available in SAS Viya, with more innovation ahead.
Conclusion
Data exploration is a critical step in any data science or AI project, and SAS provides a powerful set of tools to support it. By leveraging the data step, proc sql, and other SAS procedures along with AI and ML techniques, data scientists can efficiently explore and prepare data for advanced analytics.
As datasets continue to grow in size and complexity, automated exploration and augmented analytics will become increasingly important. SAS Viya offers a range of intelligent features to surface insights faster and empower users of all skill levels.
Ultimately, effective data exploration is both an art and a science. It requires a combination of domain knowledge, statistical thinking, and technical skills. By staying abreast of the latest tools and techniques, data scientists can master the art of exploration and put their organization on the path to AI success.
