What You Need to Know about Data Modernization to Stay Competitive

Everyone has data today. But is your organization using it well? If your company culture embraces data, trusts data quality, and uses it to make most business decisions, you’re likely reaping the rewards. And if not? It might be time for a data modernization project.

Outdated systems and processes cause more problems than just annoyance and wasted time. They prevent your organization from utilizing data to its full potential. Data modernization can provide the insights you need while simultaneously cutting costs. But first you need clarity on objectives and strategy.

What is data modernization?

Data modernization means leaving legacy systems and their associated silos and security risks behind with a move to cloud-based, modern databases, data lakes, or data warehouses. It means making data more accessible and easier to work with for greater business intelligence insights, positioning organizations to take advantage of evolving technologies like visual analytics, machine learning (ML), and artificial intelligence (AI).

And it starts with updating infrastructure, because without infrastructure specifically designed to accommodate new approaches—both those we know about, and those yet to be developed—none of the rest is possible.

How legacy systems impede progress

Old infrastructure is tough to maintain and gets harder with every advance made in cloud-based, modern analytics environments. Even if your company isn’t cutting-edge with technology, everyone who consumes anything is exposed to the impact of data analytics advances—just look at the bottom of your shopping search results to see what else you may like. As these advances become part of our personal lives, it’s a natural development that we seek similar insights in our work life.

Organizations with legacy systems constantly want enhancements, but the infrastructure makes them challenging and tedious to execute. The systems tend not to be well documented, preventing an accurate data model. Their on-prem nature imposes limitations on performance speed and capacity, while demanding dedicated resources to maintain them and keep them running. Silos make sharing data difficult, and lack of data governance degrades data quality—and trust in that data.

It’s impossible to have a flourishing, data-driven culture in an organization that operates legacy systems exclusively. Team members can’t embrace data they don’t trust, and no one will see data as a solution when so many obstacles conspire to make it a problem.

How data modernization helps companies achieve their goals

When organizations clarify their objectives and strategy before moving into a data modernization project, they can expect to see some pretty great benefits.

  • Fewer staff hours needed to maintain data and data environments
  • Reduced cost of ownership
  • Improved analytics
  • Improved access to data across the team (data democratization)
  • Faster time to insight
  • Data democratization

But the real benefits come when organizational change management and the modernization project are implemented together. This is what builds a data-driven culture, where team members turn to data first to inform their decisions. When teams trust that the data is valid, know it is accessible wherever and whenever they need it, and begin to see others relying on it, then they can channel their creativity into using data to solve problems—rather than finding workarounds for legacy system limitations.

Why strategy matters

Without a clear strategy leading into your data modernization project, you’re a lot more vulnerable to failure. You wouldn’t set off on a road trip for Chicago without first knowing where you are right now and mapping out the route to get there.

Knowing where you stand now on your data journey and where you want to go requires an evaluation of these and other elements to lead to an impactful transformation:

  • People and processes
  • Data models and structures
  • Data architecture and platforms
  • Visual analytics and reporting
  • Advanced analytics

It’s important to keep a big-picture, people-first perspective at the outset of strategy development because before people trust data, they trust people. Any strategy that leaves the end user out of the equation will not be successful. But when you’re clear about how you want your modernization project to impact your teams, take the time to define your strategy and objectives, and always keep people first, great things are possible.

Download the whitepaper to learn more about data modernization.

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