Honeywell saves 10,000+ hours, scaling to 9,000 users across 14 business units

Honeywell’s Global Finance Center is the global analytics hub for the entire $40 billion company. Since March of 2016, Honeywell scaled to 9,000 Tableau users companywide. With Tableau and Alteryx, the team analyzes data in hours instead of months—helping the business make crucial decisions around liquidations and sales. Since adopting Tableau, Preetham Shanbhag, Senior Director, FP&A Operations and Transformation, estimates that Honeywell saved 10,000-20,000 man hours. When it comes to business value, Preetham said, “You cannot really put a price on it.”

Honeywell’s Global Finance Center supports analytics for 14 strategic business units and product lines across 100+ countries. Their internal customer group spans across general management, sales, marketing, procurement, IT, human resources, and more.

Powered by Tableau and Alteryx, the team analyzes data from over 150 ERP systems, including SAP, Oracle, Navision, and JD Edwards, to name a few. Since March of 2016, Honeywell scaled from zero to 9,000 Tableau users across the company.

Preetham Shanbhag, Senior Director, FP&A Operations and Transformation, explained, “In the finance domain, you analyze tons and tons of data. I mean, you have your P&Ls (profit and losses), your sales, orders backlog, working capital, cash flow. There's a ton of transactional data to analyze.”

The Global Finance Center previously analyzed data with Excel and a combination of other tools, which could take months of extraction, manipulation, and transformation. For example, standard sales reporting used to take up to three or four months to produce. When the team adopted Tableau, they created the same reports in hours or less.

“I can tell you that easily it runs into 10,000-20,000 man hours in terms of productivity generated because of the automation, standardization you get out of Tableau.”

“But more than that, it’s the business value, and you cannot really put a price on it,” said Preetham. “Earlier we spent 70% of the time on data extraction and all the drudge-work, 30% on analytics. Now that's just the opposite, which means it’s a direct P&L impact in terms of the business decisions that you can drive.”

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