In Praise of (Data) Transparency - Part #2

InPraiseOfDataTransparency2bIn my previous blog on data transparency, I posited my admittedly idealistic vision that—within reason—the more that an enterprise fosters the free flow of data through an enterprise, the better. In this follow-up, I’ll look at some of the organizational blockers to data workflows, and how to get around them.

I’ll start with the basic underlying ideal: More data is better. If I work in marketing, I need to be able to see marketing data. And sales data. And financial data. And product management data. And…I could go on, but you get the point.

The problem, the challenge, really, is that in far too many organizations, that glorious cross-functional data just doesn’t flow across the enterprise, or I should say, over or through its silos, be they functional, architectural, or process-based. Perhaps it’s naive of me to ask, but why on earth does this obstinate hindrance to progress still persist?

Data blockages—institutional or human-created—lead to data-hoarding. (Know any data hoarders in your enterprise? Am I the only one who thinks “Data Hoarders” would make for a great reality show?)Let’s look at some of the organizational contributors to data blockage. Any of these data-hoarding characteristics hit close to home?

  • Provincialism: “It’s my data. I own it. Only I get to derive value from it. Plus, I may be able to use it against those who anger me.”
  • Trust (or more specifically, the lack thereof): “This data is proprietary, and must remain confidential. I don’t know who you hire over there in [other department that's not mine], therefore, I cannot trust you with this information.”
  • Change is a threat: “We’ve always done it this way. We’ve never shared before, and we’re not about to change for your benefit.”
  • Incompatibility: “You’re the one who chose that marketing automation solution. It’s not my fault it doesn’t easily integrate with my CRM.”
  • Misplaced or missing incentives: “What benefit will I see if I share data with you? It will cost me time/money to share, and could even be a risk…one I’m not willing to take.”

The inefficient flow of information in an enterprise so often boils down to organizational dysfunction. How willing are you and your colleagues to work together to share data? Would you share your team’s data with someone in your enterprise you don’t like? Does sharing your team’s data with another group deliver tangible benefits to your team?

Defeating the data-hoarders requires a corporate commitment to the free flow of data over, under, and through the enterprise. That’s an organizational behavior and leadership challenge that should be addressed at the C-suite level.

Moving towards data transparency requires more than just progressive leadership. Effective data integration is a prerequisite. Technology helps, on both the data management and data consumption sides of the equation. For example, Informatica frames its data-management capabilities around its Enterprise Information Catalog, or EIC for short. The EIC is Informatica’s data catalog solution, a technology that leverages machine learning to catalog, classify, and map relationships between enterprise data assets. The end user (typically a data scientist or even a business user) can get at her or his data assets via a search interface. That new process delivers benefits: Discovery is convenient, access is accelerated, and perhaps most importantly, the data is trustworthy.

The data-workflow approach championed by Informatica and other data-integration and data-cataloging vendors works, and delivers all the tangible benefits the vendors’ respective marketing materials trumpet. But no technology by itself can overcome myopic, office-politics-driven data-hoarding. To reap the benefits of true enterprise data transparency, you’re going to have to come to agreement with your peers—even the ones who drive you crazy—on five simple words: “We’re all in this together.”

About Toph Whitmore

Toph Whitmore is a Blue Hill Research principal analyst covering the Big Data, analytics, marketing automation, and business operations technology spaces. His research interests include technology adoption criteria, data-driven decision-making in the enterprise, customer-journey analytics, and enterprise data-integration models. Before joining Blue Hill Research, Toph spent four years providing management consulting services to Microsoft, delivering strategic project management leadership. More recently, he served as a marketing executive with cloud infrastructure and Big Data software technology firms. A former journalist, Toph's writing has appeared in GigaOM, DevOps Angle, and The Huffington Post, among other media. Toph resides in North Vancouver, British Columbia, Canada, where he is active in the local tech startup community as an angel investor and corporate advisor.
Posted on July 18, 2017 by Toph Whitmore

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