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Organizations must pull together data — regardless of where it resides — to obtain value from it. That means connecting data sources, shining a light on dark data, processing and cleaning data in real time, and automating analytics environments.

So, where should you start? The answer lies in building a data integration roadmap, according to members of IDG’s Influencer community of IT professionals, industry analysts and technology experts. Here is their advice.

Step 1: Putting context around data

Every business, regardless of size, has a wealth of data — much of it dark and sitting in disparate silos or repositories like spreadsheets, data warehouses, non-relational databases, and more. The first step in the data integration roadmap is understanding what you have.

“Organizations should start with a data discovery exercise,” says Isaac Sacolick (@nyike), President of StarCIO and author of the book Driving Digital. That includes looking at the volume, breadth, velocity and integration requirements of different data sources. Doing so, he says, “aids in identifying data owners, selecting data integration tools, and learning data processing skills.”

In the process, reign in dark or unstructured data. “Try to limit the number of locations where unstructured data are stored,” says Kayne McGladrey (@kaynemcgladrey), Security Architect at Ascent Solutions. “Your organization will face less legal and regulatory risks by being intentional about your data storage and prohibiting storing or processing non-public data on high-risk or unsanctioned services.”

In addition, by shining a light on dark data, companies can begin to understand where value lies, says Scott Nelson (@Scott_Nelson19), CEO and CTO at Reuleaux Technology. “Every real-world system is complex. Start learning what data matters as soon as possible.”

Discovering and cataloging data puts context around it, says Jason James (@itlinchpin), CIO for Net Health: “Before embarking on a data integration roadmap, you need to understand the current state of data and the desired outcome. Map out where you are and where you want to go.”

Step 2: Define objectives and roles

As with any technology roadmap, determine your goals.

“Define your objectives early on,” says Tristan Pollock (@pollock), Head of Community at CTO.ai. “Identify the business problem you are solving. Are you taking a large amount of data and making it understandable? Does it tell your customers how to take advantage of it? Factor in these essential things into every decision.”

The customer plays a critical role in shaping your data value story, say the IDG Influencers.

“Start from the outside in,” says Frank Cutitta (@fcutitta), CEO and Founder of HealthTech Decisions Lab. “Too often roadmaps are developed without the input of the customer and do not reflect data-driven market forces, especially in a world of consumerization.”

Peter B. Nichol (@PeterBNichol), Chief Technology Officer, Oroca Innovations, agrees: “Quite often, we’re left with a lot of data, confusing insights, and no story.” He suggests asking questions like:

  • Who is managing the data stakeholder?
  • What story with the data are we trying to tell?
  • Who is responsible for developing and elaborating the business case?

Next, start dropping your data story into a framework that addresses risks and the data life cycle.

“A risk management framework utilizes best practices for your specific industry to protect what data you value most,” says Chuck Brooks (@ChuckDBrooks), President Brooks Consulting International. “A plan for data security combined with fundamental cyber risk management are an integral part of the overall enterprise risk management (ERM) framework to stay ahead of the threats.”

Also, think about data as a complete life cycle — from acquisition to insightful analysis, says Jack Gold (@jckgld), Principal Analyst and Founder at J. Gold Associates. “Don’t ‘orphan’ data; make sure it’s integrated into the total data picture. If you’re only analyzing 10–15% of your data, 85–90% of your business insights are not being realized.”

Step 3: Find the Right Data Solutions

Once your organization understands its data value story, then it’s time to put integration into practice. Explore strategies and solutions that best fit your business case.

“I encourage teams to think about how they build their ‘change’ muscle,” says Ben Schein (@benfromminn), VP of Data Curiosity at Domo. “The most important thing you can do is build a system that can anticipate and respond to both a changing environment and evolving technology.”

“Even if you are using the current best-in-class tech stack, if you design it in a rigid manner, it will not be long before you get stuck and cannot continue your data journey,” he notes. “So make sure you have a culture that builds the change muscle, and you will always have a way to stay ahead of the evolving data landscape.”

In terms of solutions, Gene De Libero (@GeneDeLibero), Chief Strategy Officer at GeekHive, recommends developing a master data management (MDM) strategy. “MDM is where the rubber meets the road for maintaining organizational data as an asset. That said, MDM comes in various flavors, shapes and sizes; what’s right for one organization might not be suitable for another.”

Furthermore, “a data integration roadmap should take emerging technologies into consideration,” says Steve Tout (@stevetout), Senior Advisor at Optiv Security. “Next-generation data integration platforms will enable organizations to automatically discover data types and dynamically apply granular access controls for downstream data consumers.”

Cutitta agrees: “Ensure the roadmap is developed separate from the existing underlying technology. Instead, it should be driven from a fresh ‘art of the possible’ data infrastructure roadmap, as opposed to the ‘infrastructure we’re stuck with.’”

Finally, get help where you most need it.

“Creating a data integration roadmap can be tricky and a lot of time can be wasted if you do not set it up correctly,” says Debra Ruh (@debraruh), CEO of Ruh Global IMPACT. “The benefit of using outside help for these projects is that it will allow your internal teams to brainstorm how others have created data integration roadmaps: What worked? What causes heartburn? What should we avoid?”

The ultimate goal is data value, Pollock says. “The key words are actionable data. If you don’t know what the data means, if it isn’t benchmarked, or it’s just confusing, then both you and your customers will be lost.”

Learn more about how Domo Integration Cloud helps address the data integration issue.