Data Discovery vs Data Exploration: An All-New Look

Hey, it’s 2024 already and data remains more important than ever, with businesses looking to their huge amount of data, to answer complex questions. 

Data analysis also, as a field has to keep evolving to meet the demands created by this ceiling-high amount of data. Data analysts now leverage interactive visual exploration at a massive scale,  enabling faster responses to business questions.  

However, data discovery at the desktop and departmental level has limitations. Specifically, the need to know what data to use to find the answer you were looking for.

This limitation often left analysts waiting for the availability of datasets. Not knowing what data to use could lead them downhill, down the wrong path, as a lot of times the data they used needed to reveal the optimal answer. 

The terms “data exploration” and “data discovery’ have remained very relevant, let’s see what they mean.

Data Discovery vs Data Exploration: Answering Questions on Big Data

As the big data market continues to expand in 2024, the term “data discovery” has been aptly applied to the process of trying to discover answers to questions buried in big data. And, since big data is indeed big, you need to know where to look to discover answers.

Big data is also used to gain a better understanding of answers to questions in areas you’ve previously never tapped. This illustrates a scenario where you know what specific questions you’re asking and the area to look for the answers. Big data is where you are trying to “explore” new areas (things you don’t know) and questions you haven’t even asked yet.

Data Discovery vs. Data Exploration : A Historical View

Historically, there were many voyages of exploration and discovery. Some you could characterize as exploration and some as discovery. Let’s look at two examples that show the difference.

Christopher Columbus was on a voyage of discovery. He knew exactly what question he wanted to answer — I want to get to the East Indies — and knew what direction or area to look — sailing directly west. Now, he did find a different answer, discovering the Americas, but his mission was one of discovery.

Captain James Cook set out on a different mission — to explore the Pacific. He was trying to explore new areas to find answers to a broad suite of questions. As he explored, he would identify specific areas that showed promise. Then, he would transition into discovery mode to answer specific questions relevant to that area.

The Complexities of Big Data

In big data analysis, the goal is always to find answers, but it’s not always straightforward. 

Here are five key challenges:

• Familiarity: Analysts often deal with unfamiliar data and new analysis areas.
• Data Volume: There’s so much data that it’s hard to know where to begin.
• Where to Look: Analysts might not be sure which data will reveal the best answers.
• Beyond Past Trends: Big data aims to predict future patterns, not just analyze past trends.

Given the challenges, a practical approach involves dividing the analysis into two clear steps:

• Data Exploration: After preparing the data, you delve into it, identifying useful parts and testing hypotheses. This step is like refining and narrowing down the data.
• Data Discovery: Once you pinpoint the crucial data for answers, you dive deep to uncover specific items. The goal is to find ways to present these insights effectively to business teams.

Exploration in big data is immensely significance for these key reasons:

• Broad Goals, Not Specific Questions: In big data endeavors, your objective may be broad, like understanding why customers churn, without specific questions yet. Exploration helps pinpoint the areas of relevant data, making it easier to find specific answers within the vast dataset.
• Vast and Complex Datasets: Big data sets are extensive, containing many rows, attributes, and distinct values. Searching for answers without exploration is akin to finding a needle in a haystack. Exploration allows you to narrow down the relevant data, transforming the search into finding a needle in a handful of hay.

After thoroughly exploring and refining the data, the next step is the data discovery phase. 

Here, the focus shifts to uncovering patterns that address highly specific questions. This involves examining particular trends, and sequences of events, conducting time-series analysis, identifying clusters, and more. 

Once you have successfully “answered” the question, the final step is to visualize the findings and present them to the business.

But what is required for exploring big data?

Essential Requirements for Big Data Exploration

Now that you understand the significance and nuances of big data exploration, it’s important to identify the key capabilities you need to look for. What are they?

• Look at All the Data: Analysts need the freedom to explore without limitations on dataset size, enabling them to uncover new and unknown insights.
• Look Across, Not Just Down: Exploration demands the ability to move sideways through data, not just drill down. This broad perspective enhances the understanding of relationships within the dataset.
• Explore Anything: Successful exploration requires the flexibility to explore any set of rows, attributes, metrics, and values, as well as their relationships.
• Fail Fast: Given the trial-and-error nature of exploration, tools should facilitate quick exploration, allowing analysts to fail fast and move on until they find the necessary insights.
• Interactive: To keep pace with exploratory thinking, the exploration platform must swiftly process billions of records, ensuring sub-second response times.
• Tight Link with Preparation and Blending: Exploration often reveals the need for data cleaning or enrichment. A seamless integration with data preparation capabilities ensures a cohesive workflow.
• Finish the Curation Process: Once exploration uncovers valuable insights, the next step is producing a usable dataset effortlessly. This transition to the discovery step should be achieved with a single click, streamlining the entire process.

In summary, data exploration plays a pivotal role in the big data analysis cycle, given the vast dimensions of datasets and the need to make sense of unknown data, domains, and questions. 

Without direct exploration of big data within the analytical process, data analysts could potentially use incorrect data, resulting in flawed conclusions.

Make data exploration a central part of a cooperative data curation process that brings together your two domain experts — data analysts and business analysts. Let them work together to tame and shape your big data to find answers to new questions and the optimal answers.

In this dynamic landscape, Datameer stands out as a leading platform, providing robust capabilities for seamless data exploration and discovery. With its efficiency, it empowers analysts to navigate through big data, facilitating the uncovering of valuable insights and promoting well-informed decision-making.

Would you like to learn more about the evolving landscape of data discovery and data exploration in 2024 and how Datameer can support your data analysis needs?  Please visit this page.

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