3 Key Stages for Effective Data Modernization

San Jose’s Winchester Mystery House is a spectacle. Stairs going nowhere. Doors opening into walls. Windows overlooking other rooms. There’s no blueprint for how the house was supposed to look --merely a set of ideas.

That kind of chaos is an all-too-common sight for organizations that haven’t realized the power of their data. Silos form and teams aren’t sure how their data impacts the whole business. You’ll see multiple tools, different processes, and inconsistencies across the company, which can lead to decreased revenue and customer trust.

While the Winchester is fun to visit, you don’t want to model your organizational structure after it. Luckily, your data modernization initiative can thrive by following three key stages: plan, build, and execute.

1. Plan

Think of your stack like a home. Add more rooms onto a house, and you need more electricity to power them. Everything must work together, or the entire system could shut down. 

Similarly, when you’re modernizing your stack, look at how to bring everything together in one environment. By leveraging the power of the cloud, you can create scalable analytics, effectively Data-as-a-Service. You’ve got this information at your fingertips and can begin planning how to use it across the organization.

The first part of that planning stage? Identify your problem. Any data modernization initiative will fail if you can’t clearly define the problem you’re trying to solve.

The next step: Establish a clear goal or objective. This is your team’s North Star. If you’ve ever gotten lost outdoors, find the North Star and it will guide you home. Your objective should be the same --something clear and tangible to work towards. You’ll likely need a centralized data analytics lake here, so everything can run through the same system and processes, creating consistent data to measure against.

The third step is defining success. You know you may want to reduce churn or improve a net promoter score. But what does achieving that goal look like? By putting metrics or KPIs in place, you’ll have a clearer understanding of your data modernization initiative.

Finally, before you can begin building, you need to get buy-in across your teams. It’s sometimes difficult to clearly articulate the value of data modernization. It’s a disruptive process and can impact the entire organization when things start shifting around. Your first stop should be the company’s C-suite or board of directors. Getting that top-level buy-in is critical.

2. Build

Once the planning phase is complete, move onto the build stage. It’s easy to say you want to move to the cloud and be data driven. It’s a lot harder to do it. How can you ensure your data modernization structure has a solid foundation?

Your first thought might be to hire outside experts. After all, an unbiased, third-party opinion can be useful in many situations. But for a data modernization transformation, you need the experience and knowledge that comes from being within the organization.

Part of that knowledge includes selecting the proper technology stack. Look at the goals you’re trying to support. If you’re choosing, say, ease of implementation, you’ll want a modern stack like Google Cloud Platform. A modern stack will still allow you to hit other goals, like analytics or scalability, but do thorough research for the best fit.

Oftentimes, modernizing your data also means updating your business structure, too. Changing your strategy to a more agile approach -- instead of a one-task-at-a-time mindset -- improves efficiency, since you’re not waiting on one task to get done before moving onto the next.

3. Execute

You’ve planned and built your data modernization journey. Now it’s time to execute. You’re likely dealing with dozens of data stores that all need to be combined into one lake -- a complex undertaking.

In my experience, it’s most efficient to create a template. Get the first couple pieces of your project correct, then build a template to automate the remaining steps. This provides two benefits: a smooth, efficient process for moving forward, and a quick way to show value to stakeholders.

You’ll also learn things and make mistakes along the way. If you’re regularly assessing your work and getting the incremental pieces right, you’ll continue remaining agile and flexible as you press onward.

I also recommend creating documentation for everything you do. Chronicle the history of your process, including emails, blueprints, and errors. That’ll both prepare you for your next big data project and is something you can share with colleagues and customers.

Data modernization is more than a one-off initiative or project. It’s a business transformation. Your end users are going to regularly consume this data. If you follow the Plan-Build-Execute model, you’ll help them achieve their goals more quickly and efficiently.

That’s a lot more fun than being stuck in a room going nowhere.

As Chief Data Officer at Rackspace, Juan Riojas is responsible for enterprise-wide data strategy, management, and analytics to meet the need of the business to answer critical questions through time to insight. He has more than 20 years of industry experience successfully migrating data ecosystem across all public clouds, leading to significant business transformation outcomes. Prior to Rackspace, Riojas worked for Informatica building their inaugural Data Office and has held various executive leadership roles at Gogo, Dell, Accenture, and Expeditors. A native of Texas, Riojas attended Texas A&M International University, where he studied business administration and holds a post graduate degree from Said Business School, Oxford University.