How Data Scientists Can Navigate Data Oceans

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.

Expansion of Data Lakes Into Data Oceans

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:

• Costs increase: Storing massive amounts of data means higher costs to maintain it.
• IT struggles: It’s difficult for IT departments to locate the root of network issues, making it harder to diagnose problems.
• Lost strategies: With an overwhelming amount of data, enterprises can lose sight of their original business strategy.

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

Navigating a Data Ocean

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.

Quality data has three characteristics:

• Comprehensive
• Accurate
• Current

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

• Data required by law.
• Business contracts and documents.
• Any data associated with customer experience.

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

Best Practices for Data Storage

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:

• Communicate with clients often: What are their business goals? Which datasets are most valuable to them? If data experts can communicate with enterprises to find answers to these questions, navigating data oceans is less complicated.
• Partner up: Bringing together representatives from all parties involved with data ensures everyone is on the same page.
• Trial and error: It may lead to some extra work, but it never hurts to test the data available to you and review the outcomes. Then, make adjustments to manipulate the results as you see fit.
• Start small: Before jumping in and creating a data lake, review all your options first. Your client’s business may only require a data mart or data warehouse, depending on their data volume.

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.