Overcoming the Top 5 Data Infrastructure Missteps

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Despite increased investment in data infrastructure platforms and applications, many companies are still making significant missteps. Therefore, they are far from achieving the goals of improving efficiency, increasing productivity and increasing profitability.

There are a few main reasons. They include a lack of agility, an inability to adapt to change, and an inability to scale results. In response to these issues, Michael Katz, CEO of MParticle, shared his thoughts on why organizations fall victim to these challenges and what they can do to overcome them.

Founded in 2013, MParticle is credited with widely creating the customer data platform space. The company provides a customer data infrastructure that helps teams unify their marketing stack and improve customer engagement. Katz says MParticle doesn't run campaigns, "we improve them by improving data quality and continuously enriching customer context across multiple channels and partners."

Here are five of the biggest missteps Katz sees in clients and what is needed for successful execution.

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Data infrastructure mistake #1: Not starting with a clear first-party data strategy.

"The key to avoiding it is to improve collaboration between data creators and data consumers and to align business goals and established workflows with data collection requirements" , said Katz.

Data Infrastructure Mistake #2: Assuming steady state in the business rather than solving data entropy.

“The key to avoiding it is to first define your risks and identify your vulnerabilities to those risks,” Katz explained. “The next step is to implement a data architecture with a built-in observability framework that can help the business become more resilient. Finally, the organization must establish workflows for escalation. »

Data Infrastructure Failure #3: Building an ELT architecture without clear business goals and requirements up front.

Katz emphasized that "the key to avoiding it is to engage in a collaborative data design process and establish well-defined workflows around data governance and consumption."< /p>

Data Infrastructure Mistake #4: Relying on Excessive Debt Transformations as a Quick Fix to Bad Governance and Data Debt.

“The key to avoiding it is to treat your data like a commodity,” he said.

Data infrastructure failure #5: Relying on generalized data pipelines to solve complex customer engagement use cases.

"The key to avoiding it is learning about go-to-market requirements, marketing nuances, and choosing a more savvy/specialized pipeline, like customer data infrastructure," says Katz.

Furthermore, Katz stresses that no single data platform or pipeline will solve anyone's challenges.

“Success requires collaboration across teams, a cultural willingness to embrace change, the right incentives, and the right workflows,” he explained. "Specifically, it starts with creating a first-party data strategy that aligns data...