The Hidden Costs of Data Silos

data science| Data Engineering| IT | | Pauline Brown

Today, being data driven means doing machine learning (ML) and artificial intelligence (AI) at a more macro level. But it also means empowering all employees at all levels of the company to use data in innovative ways for faster and better decisions in their day-to-day work. Unfortunately, businesses still operating with data silos will simply spin their wheels on both initiatives.

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It’s More Than Just Frustrating

For people working with data, of course, running into roadblocks because of siloed data (like constantly having to ask for access or not even knowing what data exists to work with) is frustrating. But that frustration has a cost:

  • Lost time: The more data teams (or other staff) are held up tracking down data, the less time they have to work on business-impacting models. The bigger the team and the more siloed the data, the more the cost of paying staff to hunt down the data they need adds up quickly.
  • Incomplete data projects: Without a way to see what data is available, teams can work on entire data projects without the information they really need, which can in turn make models and insights less valuable than they could be.
  • Incorrect models: Worse than a data project that isn’t complete is one that is incorrect. When teams do get access to siloed data, often it’s still challenging to understand what exactly that data means. Without a central system or ownership, teams can work with data that they don’t understand, ultimately misguiding the business and leading to decisions based on fundamentally flawed models or projects.

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Profiting from ML and AI

Once data silos are broken down, the path to profit via ML and AI becomes much more clear, but it still isn’t easy. Executives and team leaders can begin focusing on other key initiatives that contribute to the incorporation of ML and AI in all parts of the business to affect the bottom line, like:

  • Smooth and fast operationalization (what is operationalization?)
  • A robust process for data project selection.
  • Building the right data team with the optimal mix of skills.
  • Building a culture around agility.
  • Mastering collaboration so that projects stay properly aligned between technical and business players.

Case Studies: How to Break Down Barriers

If you want to hear from real-life companies who have overcome the burdens of data silos to ultimately profit from ML and AI throughout their business, don’t miss these to talks at Strata Data Conference:

With Tony Baer (Ovum) and Florian Douetteau (DATAIKU):

Briefing: Profit from AI and Machine Learning – The Best Practices for People & Process

With Deborah Reynolds (VP, Data & Analytic Innovation at Pfizer Inc.) and Kurt Muehmel (VP Customer at Dataiku Inc.):

From Analytic Silos to Analytic Democratization: How (and Why) Companies May Make the Shift

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