Data Swamp

Data swamps occur when there is mismanagement or lack of management of the big data an organization gathers. These swamps usually begin as data lakes, but without intelligent processes, they turn into a swamp of data with little to no use. Lack of organizational planning or strategy regarding data collection and storage processes turns detrimental for the company.

Despite more organizations pushing to become more data-driven, wrangling big data into actionable delivery requires strategic help to avoid gathering a data swamp. Without a strategy, organizations struggle to make data processes valuable and profitable due to a lack of knowledge and necessary time, talent, and capital budget.

Components of Strategic Data

Data Collection

Gathering data and collecting insights using the best and cleanest methodology.

Data Aggregation

Compiling data points to then analyze and summarize in the form of actionable insights around a specific goal.

Data Correlation

Statistical analysis that looks at how strongly two points relate to each other. Strong correlations show a strong relationship between the two areas allowing for low-risk decision-making.

Statistical Significance

A reliable method for decision-making is measuring risk tolerance associated and confidence levels in datasets.

Data Visualization

Identifying patterns and visually displaying insights, which companies can gain buy-in from teams and stakeholders.

Advanced Analytics

Developing complex models as a means of simplifying big data and deepening insights to help avoid analysis paralysis.

Brand Alignment via Intelligent Data Looping

Data and analytics are at the heart of every modern business. The difference between organizations that benefit from those data points and those that let data turn into a data swamp is the managed data system.

A managed data system relies on data looping to gather, mine, and use the insights effectively. Ongoing organization and analysis allow data lakes to remain clean and provide the insights needed to enable businesses to grow profit margins.

From Data Swamp to Data Insights

Although a laborious task, data swamps can be cleaned up and used to bring data into an organization to gather insights that will drive cash flow. This task, although difficult, is worth it because it allows those major investments into robust systems to pay off by giving your team and customers better experiences with your brand. These better experiences are the ultimate point of arrival because they become your competitive differentiator.

There’s no one-size-fits-all solution for collecting and activating insights. Using frameworks, you can break through the overwhelm and get your organization out of the murky waters of a data swamp.

Frameworks to Guide Big Data Initiatives

As you strategize what intelligent operations could look like for your business, there are key frameworks to help define your processes and move away from operating from a data swamp.

Empathy Mapping

Quantitative data can deepen your perspective of what’s happening along your buyer’s path to purchase. Pairing the quantitative data housed in your data warehouse with the qualitative feedback you gather from customers, you’ll gain even more important detail about the buyer’s journey and how to connect with your customers at a cognitive and emotional level.

Customer Insights Map

Drilling down into the small nuances along your buyer’s journey can help you better understand what’s happening at the ground level for your customers and your employees. Using a comprehensive framework helps you stay on top of the wave as you surf the ocean of big data housed inside your data warehouse. With this framework, you can tell a stronger story and discover new opportunities along your buyer’s journey.

Bow Tie Funnel

Brands must focus on more than the pre-purchase funnel when calculating the lifetime value (LTV) of your buyer’s journey. By tapping into the reservoir of your data warehouse, you can look closer at the emotional and logical process your buyer takes as they engage with your brand, lending guidance on what metrics you should be looking at and what questions you should be asking — not just for the sake of a sale, but for the sake of loyalty.


Looking at your brand and customer experiences side by side allows you to create alignment between your core offerings. This alignment is validated by the core offerings within your business and is the ultimate goal for any big data initiative. To maximize the data inside your data warehouse, you must accurately and effectively break down the many complex layers of each experience and strategize solutions to provide direction when navigating a new initiative.

Customer Experience (CX) Terms