Cleaning Your Data Swamp with a Multidimensional Data Observability Platform

The Data Observer
5 min readJan 27, 2022

The term, “data swamp,” refers to a data environment that’s not managed efficiently, and subsequently results in higher cost, delays, and missed opportunities. It typically starts with the goal of creating an environment that supports crowd-sourcing the development of data and analytics solutions across the enterprise and beyond.

The idea is to enable so-called “data democracy,” which is intended to accelerate digital transformation by allowing a variety of stakeholders and users to be more autonomous in how they manage their operations. When done well, organizations can quickly reap the benefits of digital transformation and outcompete in their market. When done poorly, a data swamp emerges, which often has the following characteristics:

  • Excess Cost: Unneeded and redundant data costs money, but delivers no value
  • Delay: Friction in finding, exploring, and validating data wastes time
  • Missed Opportunity: Data that goes unused due to poor visibility, access, or trust could be delivering value to the business

In short, data swamps are expensive, difficult to use, and full of untapped potential — all of which erode data ROI. But, it doesn’t have to be this way. Let’s take a deeper dive into data swamps and explore how a multidimensional Data Observability platfom can help you clean things up.

Why Do Data Swamps Exist?

No organization sets out to create a data swamp, so why do they exist? It’s usually caused by a lack of visibility and control, which results from the following:

Data Democratization Pitfalls

An unbridled push for data democratization can lead to a “data free-for-all” with each user or team performing operational tasks and projects in their own way. Without proper governance, things get disorganized, data gets duplicated, unneeded data gets left behind, and security and compliance requirements may not be met.

If data governance is too restrictive or inefficient, transformation is held back by bottlenecks in process and procedure. Efficient data governance is the key to maximizing the reward and minimizing the risks of data democratization.

The Nature of the Cloud

The cloud’s unprecedented agility and seemingly infinite capacity has allowed innovation to accelerate at a rapid pace. However, without the traditional procurement process, technology and capacity limitations of on-premise environments, a data free-for-all in the cloud can lead to tech sprawl, runaway costs … or, basically, a data swamp in the cloud.

M&A Challenges

Merger and acquisition activity is one of the fastest paths to revenue growth. It’s also one of the fastest ways to get a data swamp.

Acquiring a company with a data swamp can easily pollute your data environment — even if yours had been crystal clear. Moreover, merging two well-run data environments and organizations can still result in a mess.

External Data Risk

Third-party data sources can yield tremendous value but can come with additional risks. External data has even less visibility than internal data, can change unexpectedly, and may not have adequate governance in place.

Turnover and Rapid Change

Competition for technical talent is fierce. Data engineers, data analysts, and data scientists come and go at an alarming rate, leaving behind a complex web of abandoned projects and knowledge gaps.

Real-Time Use Cases

Digital transformation often involves leveraging data and analytics in near real-time to improve decisions in the context of the task at hand. Each of these use cases has its own requirements, data, processes, analytics, and other attributes. This leads to an explosion of complexity, much of which is unavoidable. If not managed well, a swamp will undoubtedly emerge.

Advanced Analytics

Machine learning and AI often involve a lot of data processing with many intermediate stages to turn raw data into super-human insight. This can create a data footprint much larger than the original data sources, creating additional complexity to manage and navigate.

Trust Issues

When data lacks visibility and context as to what it is and where it comes from, the tendency is to not trust it and not use it. Rather than reuse data that’s already been processed or prepared the user will start from scratch with raw data from the source. This leads to redundancy of resources, development, and maintenance.

A Vicious Cycle

If not addressed, a data swamp will get worse over time following the law of entropy. As the environment grows, costs increase, innovation slows, and maintenance gets harder.

Clean Up Your Data Swamp with Multidimensional Data Observability

With so many pitfalls, it’s no wonder that so many large organizations are struggling with a data swamp. Unfortunately, the challenges of data democratization, cloud computing, M&A, external data, turnover, AI … well, the list keeps getting bigger, and none of it is going away.

Just as the problem is multidimensional, the solution is too. While there are many point solutions that can answer certain questions about your data environment, without a 360-degree view of your data you cannot effectively clean the swamp.

A data observability platform needs to address all facets of the data swamp problem, including:

  • Catalog: Inventory data assets to improve data management and data discovery
  • Size and Cost: Identify data that’s consuming the most storage and processing resources. Prioritize optimization efforts there for the biggest cost savings.
  • Temperature: Identify infrequently used data (“cold data”) to optimize storage for cost savings.
  • Context: Automate the classification and tagging of data assets, create business glossaries, enrich, and crowd-source to make data discovery and governance easier
  • Utilization: Identify which people and processes are creating and accessing data
  • Redundancy: Identify similar data assets for potential consolidation
  • Interdependencies: Identify relationships and data lineage to understand dependencies and impact to change
  • Data Trust: Build trust in data by monitoring and analyzing a comprehensive set of data reliability concerns. Automate the creation of data quality rules, reconcile data from source to target, monitor schema drift and data drift, identify anomalies and much more.

The Importance (and Value) of Cleaning Up Your Data Swamp

With a multidimensional data observability platform, cleaning your data swamp becomes much more feasible — and worth the investment of time and effort. And, after you’ve cleaned things up, you’re bound to enjoy a number of tangible benefits, including:

  • Accelerated Transformation: Better organization, visibility, and reliability makes it easier for users to find data, trust and innovate with data.
  • Higher Data ROI: Eliminating cold data and redundant data leads to lower costs for infrastructure and maintenance.
  • Better Outcomes: Analytics and automation for improved data reliability ensures the processes and decisions driven by data are of the highest quality.

Photo by Nils Leonhardt on Unsplash