Why Workforce Plans Breakdown: The Real Cost of Bad Data

Key insights from Cinareo’s recent conversation with industry experts Mark Alpern and Daniel Piper

This article is the first in a four-part series exploring key themes from Cinareo’s recent conversation with workforce planning experts Mark Alpern and Daniel Piper.

Even the best workforce plans can fail if they start with unreliable data. That was one of the first realities acknowledged in a recent webinar, Why Workforce Plans Breakdown, where industry experts Mark Alpern and Daniel Piper unpacked why operational chaos often hides behind confident spreadsheets.

Both emphasized that poor data quality sits at the center of most performance breakdowns. Bad data can make forecasts look precise but feel unpredictable in practice. It leads to missed service levels, wasted budgets, and a slow erosion of trust between planners and operations.

“Bad data leads to poor predictions,” said Daniel Piper. “Too few people, and customers are left waiting; too many, and you’re paying for idle capacity.”

Mark Alpern agreed, adding that backward-looking information makes the problem worse. “Historical data shows what happened,” he explained. “But workforce plans need to reflect what’s about to change: new lines of business, attrition, or even learning curves.”

Both experts reinforced a simple truth: when the foundation is unstable, no amount of analysis or technology can save the plan.

The Hidden Price of Unreliable Inputs

The cost of poor data extends far beyond forecasting errors. It creates uncertainty, slows decision-making, and erodes trust across every level of the organization. When reports conflict or metrics shift weekly, teams spend more time debating the numbers than improving performance. Leaders lose confidence in what they see, and planners lose the time they need to make proactive adjustments.

Daniel noted that many contact centers still work with partial or inconsistent inputs. Call volumes are merged across channels, definitions vary from one report to another, and key metrics such as shrinkage or handle time are updated irregularly. 

The result is not only inaccurate plans but also inefficient staffing, inflated costs, and reactive management.

He compared most contact center data environments to a household drawer that everyone uses but no one organizes. Over time, different teams add their own reports, metrics, and definitions until it becomes nearly impossible to find what’s current or accurate. “That’s what bad data looks like in a contact center,” he said. Cluttered, inconsistent, and slow to untangle when decisions need to be made fast.

Those small inconsistencies come with a real price. Service levels dip, overtime increases, and employee frustration grows. Without clarity, every decision becomes guesswork, and every missed assumption adds to the clutter.

Why It Matters

Mark pointed out that unreliable data doesn’t just skew performance models; it affects every conversation that follows. When leaders see service levels fluctuate without a clear cause, the planning function loses credibility. Over time, that credibility gap becomes as damaging as the errors themselves.

“Forecasting is built on trust,” Mark said. “If your data isn’t right, you can’t explain what’s happening, and no one will believe the plan.”

That trust is difficult to rebuild once lost. Teams end up spending more time defending the numbers than improving them, creating a cycle where doubt replaces discipline.

Cleaning the Drawer

Both speakers agreed that solving the problem starts with standardizing the inputs. That means defining what “good data” looks like: how frequently it should be updated, how it is categorized, and how it is validated before use.

Daniel explained that before any new planning initiative, organizations must isolate and stabilize their data sources. “If you’re not in a stable place, don’t add more lines of business,” he cautioned. “You’re just adding more cables to the drawer.”

It is not about having perfect data, but consistent data. With that consistency, trends become visible, comparisons become meaningful, and planning becomes something that leaders can rely on again.

Building on Solid Ground

Bad data is rarely intentional. It creeps in through disconnected systems, rushed reporting, or legacy habits that never got cleaned up. The challenge is not identifying the issue. It is having the rigor to fix it before layering more complexity on top.

As Mark and Daniel emphasized, data quality is not a technical chore but a leadership priority. A clean foundation allows every stage of workforce planning – from forecasting to capacity modeling – to operate with accuracy and confidence.

Cinareo helps organizations build that foundation by connecting inputs into one consistent source of truth. The platform gives planners and leaders the visibility to trust their numbers and act before small inconsistencies turn into large-scale chaos.

Getting data right does more than prevent mistakes; it restores confidence in every decision that follows. That confidence is what turns workforce planning from a cycle of reaction into one of predictability and control.

Watch the full discussion

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About Speakers

Mark Alpern

Co Founder/COO

Cinareo

Mark Alpern brings over 30 years of experience in contact center consulting and management. As the co-founder of Cinareo, he developed a transformative SaaS platform for strategic capacity planning and decision-making. Mark has helped organizations optimize workforce management, financial planning, and customer experience through his innovative approaches to digital transformation.

Daniel Piper, CMgr, FCMI

Founder and Independent Consultant 

Setekh Solutions

With 15+ years of experience in the BPO, contact center sectors, Dan specializes in transforming operational complexity into clarity and driving measurable business impact. Dan’s expertise spans global operational strategy, continuous improvement, and performance management at the highest levels of corporate leadership.