The modern data stack came bearing many promises. A shift from the on-premise legacy systems, it aimed to provide a faster, scalable, and more cost-effective way 0f storing and analyzing data.

However, as many of us soon realized, that promise was short-lived.

Instead of easing data management, modern cloud data warehouses created a new set of problems. The stack turned out to be nothing more than a collection of disparate technologies, pieced together

In this article, we further explore the challenges of a modern data stack, along with solutions to help you make the most of your data.

Why is managing the modern data stack so challenging?

When you scratch the surface, it becomes apparent that the modern data stack is just the legacy data stack repackaged.

But most companies hear the buzzword and buy into the hype anyway. They think a modern data stack only requires combining multiple tools and systems to handle data processing. For them, if it’s hosted on cloud-based data warehouses instead of on-premise, it’s “modern”.

However, there’s little regard for optimizing the stack or adopting systemic changes in how to organize and use the data.

This fragmented approach overcomplicates data pipelines and leads to an overwhelming volume of data going unutilized (aka data swamps). Not to mention that there’s nothing new about it. It’s just upgraded tools leading to the same data complexities and silos.

Let’s discuss these challenges in greater detail below to see just how they make handling a modern data stack difficult.

Maintaining several tools is an operational burden

Each tool in the modern data stack is picked to address a specific process, from data collection to data analysis.

In isolation, these powerful tools might be great at what they do. However, the trouble starts when you try integrating them. This means getting multiple product interfaces and data formats to somehow work in harmony. A feat too complex for most product teams to handle with no tech assistance.

Ensuring these tools work together well ultimately comes with significant overhead and high setup time. But the troubles don’t end there. Even day-to-day management of such a modern data stack becomes a task owing to the complexities of each integration point.

Plus, each data stack component requires individual monitoring for updates and maintenance, increasing the time spent on your stack’s upkeep. And as you add more tools to your data stack, that means more maintenance efforts – ultimately overloading your data teams.

It’s difficult to ensure data quality and consistency across the system

Ensuring data quality across your modern data stack isn’t easy since different tools save data in different formats. This also introduces the risk of your modern data stack tools not saving data accurately when transferring.

Such inconsistencies can lead to faulty data analytics and potentially misguide data-driven business decisions too.

You can end up creating data silos

While the modern data stack’s integration capabilities were meant to remove data silos, the reality is anything from it. Even with MDS, silos occur when certain tools or departments keep raw data to themselves. So you can’t access or share valuable insights with others across the organization.

This results in lacking or incomplete data lakes or cloud data warehouses since not all data pipelines feed into them as they should. Plus, such silos also hinder collaboration, leading to fragmented insights and poor decision-making.

Face increased security threats

With data spread over multiple tools and so many data pipelines, it’s difficult to ensure data security. You have to continuously monitor numerous systems to identify where certain data is stored, who accessed it, and why.

Tracking all these moving parts of a modern data stack only increases the likelihood of data theft or unauthorized access.

However, you can avoid such threats by conducting regular security audits and updates. These help ensure your cloud data warehouses remain free of any vulnerabilities. Additionally, consider investing in the right tools for your data stack. Ones that prioritize data security by earning certifications such as HIPAA, SOC 2, ISO, etc.

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How Userpilot is fixing the modern data stack

Userpilot is an all-in-one product growth platform that provides a holistic view of the user experience with product analytics, session replay, user engagement and feedback, and data sync capabilities.

How can these functionalities help fix the modern data stack — let’s explore.

Gather quantitative, qualitative, and visual data in a single platform

Userpilot replaces multiple tools by offering different features for collecting the specific data types you need. This includes quantitive, qualitative, and visual data — all collected using the same tool.

You don’t have to switch contexts either, so you can easily analyze all 3 data types together within Userpilot.

For quantitative data, you can use autocapture to automatically track in-app interactions. Then create data visualizations, like funnels, paths, retention cohort tables, etc., to unearth any usage trends.

Next, dig deeper into this data by capturing session replays, which enable you to focus on individual user behavior.

You can also use feedback surveys to collect qualitative insights into user actions, their needs and wants, and pain points.

Receive data from other sources with bulk import APIs

Easily import historical user data from other platforms to Userpilot, enabling a seamless transition with no loss of valuable raw data and insights. This includes data related to user and company identification, page views, and custom events.

Userpilot APIs also help improve the data stack’s security since they require your unique API key for request authentication.

Unify business and product data for better decision-making

Lastly, save time when unifying data in your chosen BI tool by relying on Userpilot’s automation instead of manual processes that come with a poorly designed modern data stack.

For instance, filter data by date range, user, company, event type, or predefined segments, then export automatically in JSON or CSV format.

Plus, to reduce any duplicates or outdated information, set sync frequency to update data every few hours. This helps ensure data consistency across your systems.

Here’s what this process would look like in action:

• Product usage data tracked with Userpilot is transferred to cloud storage platforms, like Amazon S3, Google Cloud, Microsoft Azure, etc.
• Next, this data undergoes the Extract, Transform, Load (ETL) process where raw data is cleaned and organized.
• The now-processed data is then stored in data warehouses like Amazon Redshift, Snowflake, or Databricks.
• Finally, the data is transferred to BI tools, like Power BI, Looker, or Tableau, where your business decisions are made.

Embrace the post-modern data stack era

The modern data stack could not deliver. It’s riddled with too many problems — large overhead costs, significant set-up times, data quality and consistency concerns, and increased security threats.

So what’s the next step then?

We think it’s time to move on to the “post-modern” data stack by investing in fewer tools and choosing all-in-one solutions instead. These will help reduce the operational complexity associated with maintaining a fragmented toolset, lower costs, and avoid silos.

Looking for the right all-in-one platform to get you started? Book a free Userpilot demo and see how easily you can unify product and business data throughout your data stack.

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