How Data Scientists Can Navigate Data Oceans

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Navigating the waters of a data ocean is challenging, time-consuming, and somewhat near impossible. Thinking of a data ocean like our Earth’s oceans provides perspective — it shows how vast the world of data truly is.

To better understand what a data ocean is, it’s best to identify how they’re created and what purpose they serve.

How are data oceans formed? What can we accomplish with the data inside a data ocean? We’re hoping to answer some of these questions to make data oceans more understandable for data scientists, whether you’re just starting or a seasoned expert.

Data lakes serve as a large repository for unstructured, semi-structured, and structured data. This data is held in its respective lake before cleansing and transforming. After data scientists clean and transform data, business leaders can use it to drive their decision-making.

A common problem for data scientists and businesses alike is the misuse and mismanagement of a data lake. When enterprises leave data lakes alone for too long, it tends to expand as more data pours into it.

The value of the data in these lakes decreases over time and makes it challenging for data experts to make sense of it all.

As a result, data lakes expand into what we know as data oceans. Big data is already a complex industry, and data oceans further complicate it. And when data lakes experience exponential growth as data generates, new considerations have to be made.

What happens when data lakes become too large? A few things happen:

Luckily, there are a few ways to navigate a data ocean if your original data lake becomes too overwhelming.

Investing in high-quality data management tools can make your navigation of a data ocean easier. By incorporating these tools into your data architecture, you’re managing data coming in more efficiently, making it less likely for your data lake to grow too large.

In addition to investing in the right tools, data experts can sort through data in an ocean and search for the best data to pull.

Some examples of types of data that would be important for a business to store are:

Identifying quality data and certain types of data within a data ocean makes it that much easier to analyze.

Besides finding quality data, data experts must also identify what types of data subsets a business is looking for. Here are some other best practices for big data management:

Regardless of data usage, it’s critical to employ these steps when working with big data.

Keep Your Head Above Water

Data oceans are not all that scary — applying your data analytic skills comes in handy. You can sort through big data in data lakes before they expand. Fine-tune how you search through data oceans by only looking for quality data that’s useful for the organization’s immediate and long-term goals.