What is data exploration in artificial intelligence (AI)?

By Brynne Henn - July 28, 2021

When you open up a jigsaw puzzle, it’s a good strategy to first find the corners, then the same-colored pieces, and finally the oddball shapes, all of which make it easier to see the bigger picture.

Data exploration is a similar process, but with data analysis. Also known as exploratory data analysis (EDA), data exploration is the initial discovery of looking at datasets and determining what’s what. It’s not the stage of data analysis where all the information gets sorted and turned into actionable insights with the help of artificial intelligence and advanced analytics. Instead, it’s the critical stage before that, which allows machine learning models to work their magic.

Used throughout data science, the data exploration stage has several objectives: to streamline analyses, to identify problems or patterns within the data from the get-go, and to create data visualizations that provide an easy overview of the information on hand.

But data exploration is also a foundational step in effectively using artificial intelligence in your business. In order to take full advantage of machine learning in data-analysis engines such as prescriptive analytics, data mining, and semantic analyses, there needs to be quality data preparation—and exploration—done first.

What is the difference between AI and machine learning?

Although it’s been the subject of philosophers and mathematicians for generations, there is no universally accepted definition of AI. But generally speaking, AI refers to the study and development of computers that can replicate human tasks such as vision, speech, and decision-making.

What makes AI truly intelligent is the computational ability to process complex data in real time. The interaction between AI and datasets is fluid and iterative, allowing these systems to autonomously respond and grow more sophisticated over time. For example, AI that powers self-driving cars can analyze visual and spatial data quick enough to let the vehicle safely share roadways with human drivers; the AI also improves as it takes in more data and stimuli. AI that sniffs out financial cybercriminals is programmed to look for suspicious patterns within high volumes of bank and credit card transactions; but it’s also anticipating new threats we’ve never seen before, one step ahead of human awareness.

None of that would be possible without machine learning algorithms, the engine of practically every AI system. Machine learning is what enables smart systems to get smarter. These algorithms are designed to equip AI with the power to self-educate and improve its own accuracy over time, learning from the data it’s steadily taking in. This means the AI is always adjusting to interactions between data points, providing living, breathing data analysis as the data quality changes.

And because machine learning is an iterative process, the data quality, particularly early on, is crucial to performance. AI that gets trained on datasets with anomalies or incorrectly tagged information will lead to false positives and less effective machine learning.

Data exploration techniques and best practices

Identifying outlier data points and errors in datasets is one outcome of data exploration tools. But it also gives you an overview of what is contained within the raw data—its values, trends, and clusters included.

The point of data exploration is to break down this raw data quickly, before seeking out deeper insights. In this way, data exploration can be an essential step in deciding which AI models should be applied to your data in the first place.

Data exploration typically involves a mix of manual techniques and automated methods that enable fast categorization of data. The primary goal is to gain more familiarity with the data at hand. And because humans process visual information more swiftly than numerical or textual data points, histograms—such as scatter plots, box plots, line graphs, and bar charts—and other data visualization techniques are common components of data exploration. These visualizations allow for simple regression and bi-variate analyses that suggest broad relationships between the categorical variables.

Manual data exploration tools involve writing code on platforms like Python or going through Excel spreadsheets to generate basic observations and visualizations. Automated methods include business intelligence tools from software vendors, along with open source platforms that you can find free of charge online. Some of the best open source data visualization tools work in tandem with popular programming languages like Python.

The growing availability of machine-learning tools within data exploration space means the learning curve to adoption is minimal. You won’t have to hire a special data analyst to start doing data exploration better.

Discover how Sisu fosters data exploration

Earlier this year, Sisu launched a brand new data analytics experience that fast-tracks data exploration. We took input from data scientists at clients like Samsung, Mastercard, and Gusto to create a new take on cloud data exploration that enables quick diagnostics, visualizations, and leveraging of data warehouses throughout any company.

Sisu’s new experience accelerates data exploration by diagnosing key differences in customer behaviors. It keeps metric insights always up to date and it standardizes metrics and data organization across the company. See how these results and more can fit into your company by requesting a demo today.

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