Data Exploration

Data analytics vs data discovery vs data exploration

Data discovery and data exploration are closely related. They use similar techniques to understand datasets but differ in focus.

Data discovery is goal-oriented. When business leaders need insights to support their decision-making, a data analytics project will assemble datasets that can provide those insights. Data teams use discovery to evaluate each dataset’s suitability and create data preparation processes.

Data exploration is open-ended. When analysts and data scientists want to see what there is to learn from data, they use data exploration techniques to “scan the landscape” and the types of questions they can ask.

In both cases, researchers use data analytics techniques, such as machine learning models, to generate insights that will support the business.

What are data exploration tools?

Data exploration tools help analysts evaluate thousands of data points quickly using Microsoft Excel spreadsheets or data visualization tools like Tableau to generate visual representations, including:

• Scatter plots
• Time series graphs
• Box plots
• Bar charts
• Histograms

Regression analysis, bivariate analysis, univariate analysis, and other statistical techniques can further reveal trends and relationships.

Given the scale of modern datasets, automation accelerates the exploration phase, so data scientists will often write code in Python or other programming languages to better understand the data.

What does data exploration produce?

This initial phase in a data analysis project makes data scientists aware of the key characteristics of their datasets. For example, comparing the data types, formats, and metadata in each dataset makes scientists aware of the variation between sources they must address before analyzing the data.

Data exploration also maps the quality of different datasets by identifying inaccurate or missing data.

Most importantly, data exploration raises questions by revealing trends, outliers, and patterns within the data to inform subsequent analysis.

Why is data exploration important?

Data has become the most valuable enterprise asset, yet modern businesses generate data in such immense volumes that tapping into this resource is challenging. Less than 10% of enterprise data, half of which is never used, even reaches a data lake. Data exploration brings this dark data into the light and lets companies answer questions they didn’t know they had.

Data exploration also improves the quality of data science projects and machine learning algorithms. Thoroughly assessing data sources prevents poor data quality, biases, and assumptions from corrupting analyses and generating false insights.

Industry use cases for data exploration

Every industry has use cases for data exploration, from predictive modeling to business intelligence analysis. Here are ways the retail, healthcare, and financial sectors can apply these techniques to move their business forward.

Retail

In the past, retailers could generate forecasts by extending historical sales trends into the future. That approach creates inventory imbalances and missed sales opportunities. Retail data exploration uncovers complex relationships between demographics, product features, seasonality, and other variables. With these insights, retailers generate granular demand forecasts that maximize revenues and optimize inventory spends.

Retailers also use data exploration to create 360-degree perspectives of their customers that blend data from e-commerce clickstreams, social media activity, psychographics, and other sources. A complete understanding of their customers lets retailers create personalized campaigns that increase sales and customer retention.

Healthcare and life sciences

Scientific exploration is the cornerstone of the healthcare and life sciences industries. Data exploration lets these organizations harness medical and pharmaceutical data sources.

Pharmaceutical companies often search for insights in their clinical trial databases. Although each trial is a directed research project, data exploration lets scientists find patterns across multiple projects that may improve drug efficacy or open new avenues for research.

Hospital systems can unlock insights within their patient electronic health records. For example, data exploration may identify previously unknown patterns in re-admittance rates and other healthcare quality metrics. Further analysis may find ways to improve patient outcomes or hospital efficiency.

Finance and banking

Banks and other financial institutions generate enormous volumes of transactional data every day. Traditional analysis cannot keep up with such real-time data flows. Data exploration lets these organizations uncover patterns that help mitigate risk and improve customer service in a highly regulated and competitive industry.

Data exploration can reinforce Know-Your-Customer processes by helping financial institutions identify unusual customer behavior. Flagging potential risks faster improves compliance and reduces regulatory risk.

As with the retail industry, consumer financial services depend on 360-degree perspectives of customer behavior. Data exploration can produce more effective personalized product recommendations, increasing conversion rates and revenue.

What are the steps of data exploration?

Data exploration may be open-ended, but it is not unstructured. Scientists don’t explore data for its own sake. Although not as narrowly focused as in a data discovery project, there is always an objective that supports enterprise business goals.

These four steps will help develop an effective, strategically-aligned data exploration initiative.

1. Align data strategy with business strategy

Data scientists should begin by placing exploration in the business context. What kinds of problems does the business need to solve? What types of datasets could contribute to the investigation?

A business-centered exploration plan keeps data scientists from getting accurate, insightful answers to the wrong questions.

2. Exploring data in a data warehouse (or data lakehouse)

Data that is already modeled in a data warehouse or lakehouse are the low-hanging fruit in exploration. Datasets and their associated metadata are well-documented and ready for analysis.

Even well-covered territory can hold surprises, especially if you connect centralized data to other sources. For example, warehouse data can easily identify the company’s most profitable customers. However, combining that data with a demographic dataset can help you explore the characteristics of profitable customers that could make marketing campaigns more effective.

3. Data that hasn’t been centralized and modeled

Taking a step further out, you begin to explore data that hasn’t been centralized or modeled. As mentioned earlier, most data lakes contain structured and unstructured data that have never been analyzed. Data from other sources, such as high-volume clickstreams, may never get stored. These untapped resources present opportunities to find useful patterns and relationships.

4. Real data exploration

A mature data strategy uses exploration techniques to generate unique business value. At this point, you are exploring unknown data in search of business-related questions nobody has thought to ask.

Scientists rapidly iterate through a cycle of hypothesis, experimentation, and analysis to bring insights to the surface and turn them into data products that drive business success.

Data virtualization and federation technologies are helpful with this type of data exploration.

Starburst for data exploration

Making the most of your data exploration efforts requires a flexible data architecture that lets you access any type of data stored anywhere in the company. Centralized repositories, from data centers to data warehouses to data lakes, have never quite lived up to their promise of being that single source of truth.

Starburst Galaxy accelerates data exploration by enabling data virtualization and federation technologies. All data remains at the source, reducing infrastructure costs and pipeline development efforts. Instead, Starburst’s data lake analytics platform creates a virtual access layer that unifies every data source in a single point of access.

Data analysts and scientists can use the tools they already use to run SQL queries simultaneously on multiple datasets in multiple domains and locations. Data sources that have not been centralized or modeled become as accessible as any data warehouse.

Giving data users direct access to sources eliminates the pipeline development that slows exploration. Scientists can experiment with unknown data, running and modifying queries quickly to chase down insights.