Do you have more business data than you know what to do with?

A top business priority for many companies is gathering as many insights as possible and using data analytics to make decisions. This often involves the Internet of Things (IoT), which brings in even more datapoints.

If this sounds familiar, it’s time to get serious about integration. This is where data lakes come in.  

What is a data lake? Let’s dive in.

Data Lakes Defined

Many of the ERP vendors that our ERP consulting team works with like to talk about “data lakes.” With the increased popularity of IoT technology as well as AI in ERP, we’ve heard even more talk about data lakes than usual.

So, of course, we thought we should provide a definition.

Data lakes are centralized business systems designed to house, process, and protect large amounts of data. This includes data that is:

  • Structured
  • Semi-structured
  • Unstructured

These repositories help solve a core challenge in the business world: making sense of data both in real time and into the future.

The 2024 Top 10 ERP Systems Report

What vendors are considering for your ERP implementation? This list is a helpful starting point.

The Structure of a Data Lake

Most data lakes consist of two different sides:

  • The data-in side
  • The data-out side

Let’s take a look at how each side works.

Data-In Side

The data-in side ingests and stores raw, unstructured data as it’s created. This data can come from a variety of sources, including IT and OT systems, ERP and SCM systems, and even sensor data from the warehouse or factory floor.

When it first enters the data lake, this data is still in its original format. As it’s processed, it transitions to the data-out side.

Data-Out Side

The data-out side is the actionable side. This section of the data lake stores processed data in a range of different business systems and databases, such as:

  • SQL databases: Used for smaller volumes of organized data (e.g., orders from ERP systems)
  • Time-series databases: Used for larger volumes of organized data (e.g., contextualized data from sensors)
  • In-memory databases: Used to store real-time data for quick access
  • Blob storage: Used to store binary, unstructured data (e.g., images, videos, PDF files)

All of these databases operate within a cloud architecture, which makes them inherently flexible and scalable.

While time-series databases contain data that can assist with traceability and compliance, they typically aren’t fast enough to provide real-time visibility or alerts on the plant floor. When these insights are required, companies typically use in-memory databases.

Regardless of the type of database, it can be used to store and process large volumes of data not only for immediate insights but for compliance and traceability mandates down the road.

What is a Data Lake vs. Data Warehouse?

As you might have gathered, data lakes share many of the same qualities as data warehouses. However, these entities differ on one major point: flexibility.

Unlike data warehouses, data lakes are not “schema-in.” In other words, data does not have to be structured before it can enter.

This means companies can intake raw data and store it without changing any of the attributes from the source. Then, they can analyze the data they need as soon as they need it.

In contrast, companies that only have data warehouses are required to define data before they can use it. This can be challenging and time-consuming, so organizations often use just a small portion of the valuable data across their enterprise.

Benefits of Data Lakes

1. Quick Analyses

As mentioned, data lakes allow companies to run data analyses without having to start from scratch. They can access and reprocess raw data in the data lake.

If that same information was housed in a data warehouse, it wouldn’t be available because it would already have schema applied on the data-in side.

2. Problem Identification

Quality assurance is a top priority for many industries. Without a data lake, this process would be unnecessarily complicated. Employees would have to wade through volumes of contextualized data to identify the source of a quality problem before they could even begin to take action.

With a data lake, this process is much more efficient. Employees can run a simple query to detect issues in mere minutes.

They can even take a proactive approach by creating and subscribing to traceability reports. If a quality problem arises, employees can scan the reports to pinpoint which units were affected, so they don’t have to pull the entire lot.

Looking Ahead: Data Lakehouses

We’ve established that data lakes are distinct from data warehouses, but what if there were a way to combine the best of both worlds? Enter the data lakehouse.

Like a data lake, this is a repository built to store all kinds of data (structured, semi-structured, and unstructured). Yet, it also provides the benefits that make data warehouses so valuable, including:

  • High performance
  • Robust security
  • Trusted governance

Looking ahead, data lakehouses are poised to become the only type of data architecture that can support all of a company’s advanced analytics technologies, from BI and SQL analytics to real-time data apps and AI-powered enterprise software.

Transform Your Approach to Data Management

What is a data lake? It’s a solution that allows you to intake, manage, and process data from all of your integrated systems, including your ERP system, your AI copilot, and more.

This is more than just digital storage. It’s the key to making smarter decisions and turning insights into action.

If you’ve found that your data analytics technology is creating mountains of untapped data, you already understand the benefits of data lakes. The next step is exploring your options. Contact our enterprise software consultants below to get started.

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