Data modernization in 2024: What you need to know

As we are over halfway through 2024, data modernization is crucial for organizations looking to thrive in our data-driven world. The ability to harness and use data can mean the difference between leading the pack and being left behind. 

However, achieving data modernization isn’t without challenges. Many organizations struggle with legacy systems, data silos, and a lack of cohesive strategy. The sheer volume and complexity of data can be overwhelming, and the rapid pace of technological change adds another layer of complexity. 

That said, data modernization is not only possible but essential for any organization looking to succeed today. By adopting the right strategies, leveraging emerging technologies, and building toward a digital-driven culture, you can transform data from a burden into an asset. 

In this post, I’ll share my thoughts on what data modernization really means and how you can approach it effectively to turn the promise of data into tangible business value. 

What is data modernization?

At its core, data modernization is how an organization works with data. Your ultimate goal is to make your organization's data as useful and valuable as possible. 

Think about it this way. Having no data is like flying blind. With sketchy data, it’s better but suboptimal. Now, imagine if your executives have data, full of valuable insights, at their fingertips—that is the ideal scenario. 

It’s also worth noting that in 2024, I’m not just talking about human decision-makers. Artificial intelligence is increasingly making and augmenting decisions at various levels of any organization. 

Challenges of data modernization

One thing that hasn’t changed is the fact that data is spread around in different silos and formats. This presents our first major challenge: how do we bring this data together in a useful way?

Some organizations opt for a centralized approach, essentially copying all their siloed data into one place in a single format. This can minimize the number of different technologies involved and make the data easier to work with. That said, it can become messy and difficult to govern if you're not careful. 

In contrast, larger organizations often distribute this work. Different teams or departments are responsible for their own data—cleaning it, governing it, and making it available to other departments or for centralized management information. This is more akin to a data mesh approach, which means higher organizational complexities. 

Key components of a modern data strategy

Regardless of the specific approach you choose, I believe there are several essential components for any modern data strategy. 

Flexible data governance

You need solid but flexible data governance. Some data sources are highly sensitive and need to be carefully governed. But you don’t want to lock data away to the point where you can’t work with it, or it becomes a bureaucratic nightmare.  

Instead, it’s all about striking the right balance. This balance is becoming even more critical as we move towards open data formats and increased data federation. 

Scalability

Some datasets in an organization might be incredibly large, especially when dealing with IoT data streams or massive amounts of unstructured data. 

You need systems that can handle this volume without creating technical barriers for business users who understand the problem but might not have great technical skills. 

User engagement

With incredible computing power, affordable storage, and modern techniques for handling large volumes of data, your days of worrying about how data is stored, processed, and transformed are behind you.

Instead, the most critical component of any data strategy is the people involved in the strategy. You need to bring all the stakeholders on the data modernization journey with you. They need to understand why the data is valuable and see it as an asset, not a threat. 

Beyond that, you need to cultivate a mindset of creating and cataloging data in a high-quality way and sharing the data across silos.

Emerging trends in data modernization

In 2024, we’re seeing some exciting trends that are significantly shaping the future of data modernization. One notable trend I love is the rise of open data formats like Apache Iceberg and Delta Lake. These formats allow an organization to truly own its data rather than locking it into another proprietary system. 

Combining these open formats with inexpensive cloud storage makes large datasets accessible. This enables easier data federation across the organization, providing a holistic picture of the organization in dashboards and reports. 

However, to get the most out of open data formats, you need to hydrate the data, transform it, and prepare it in a way that accurately reflects the business model or organizational structure to which it belongs. For this, we need powerful, easy-to-use tools that help us work on both data movement and transformation.

Historically, data integration tools have struggled to handle the volume and speed required by modern businesses. But modern solutions like Matillion make a significant difference.

Matillion is designed not only to handle the volume of data that large organizations deal with but also to transform and migrate this data at the speed the business demands.

Roadmap to data modernization

So, how do you actually modernize the data infrastructure? Here’s a roadmap I’ve found effective:

1. Start with engagement: Involve all the people on the consumption and production sides. Understand their goals and pain points. Resistance can kill your efforts before they even start, so this step is crucial. 
2. Create tactical models: Don’t try to model the whole business. Instead, work with teams to model the data their area creates. Set standards, but keep it manageable. 
3. Define your data architecture: Consider processing, storage, compute, governance, and cataloging. This is where you’ll make key strategic decisions. 
4. Prioritize and iterate: Don’t tackle everything at once. Choose high-impact projects and approach modernization iteratively. 
5. Implement powerful tools: Provide tools for data customers and producers to drive engagement and adoption. 
6. Build, test, and migrate: Construct new systems, test thoroughly, and migrate data. Once satisfied, decommission the outdated tools. 

The future of data modernization

As time passes, I see data modernization becoming increasingly critical. Organizations that can effectively leverage their data will have a significant advantage. They’ll be able to make better decisions, respond more quickly to changing market conditions, and drive innovation in ways we can’t even imagine yet. 

But remember, data modernization isn’t just about technology. It’s about people, processes, and culture, too. It’s about creating an organization that truly values data and uses it effectively at the entry-level. That’s the real challenge—and the real opportunity—of data modernization in 2024 and beyond.