Analytic and business teams don’t trust each other.  The results are devastating.

Analytic and business teams don’t trust each other. The results are devastating.

We have a problem. And it is the rule, not the exception. Business teams and analytic teams often misunderstand each other, leading to distrust, frustration and millions in lost productivity and rework.

In a recent series of interviews of twenty senior-level analytic leaders, we asked about their experiences working with business teams and how miscommunication limits companies’ ability to capitalize on the power of data and analytics.

In my previous newsletter, I focused specifically on how this dynamic diminishes effectiveness. The executives I interviewed admitted that their teams are at least 50% less effective than they could be if communication and collaboration were better.

In this brief, we will talk about how it impacts analysts and data scientists.

Imagine failing. All the time.

Requests from business teams to analysts are often cryptic, rushed, and urgent, while lacking context or purpose. Further, business professionals often cannot articulate exactly what they need and analysts have no training to help them figure it out. As a result, an estimated 80% of analytic results provide no value, and almost all projects require rework.

This dynamic is demoralizing to data professionals. The are repeatedly sent back, told their work isn’t what was needed, asked to revise, or find that the results won’t be used. One executive admitted that he saw one of his main jobs as “shielding their staff” from unreasonable requests and perpetual disappointment.

Even when shielded, data scientists and analysts become aware that their work isn’t appreciated and will likely need to be redone. Related to this, I polled analytic professionals about how often they answered the business question right the first time. As we see here, the success rate is dismal. Only 5% of respondents reported getting it right all the time, while almost 40% say almost never.


So why bother?

There is a psychological phenomenon (learned helplessness) that occurs when no matter what you do, you are unable to change a negative outcome. So, you stop trying. Analytic executives report that the dysfunctional dynamic between business and analytic teams is demotivating to their teams.

The impact of miscommunication and distrust between business and analytic teams builds over time. As analysts continue to feel misunderstood and underappreciated, their morale declines. This impacts performance and the overall quality of work.

Across my interviews, here are the personnel consequences they listed:

Low morale and burnout:

  • Morale is adversely affected, leading to burnout.
  • Workers feel unappreciated and undervalued.
  • Analysts retreat, feeling demotivated and worried about being blamed.

Wasted time and energy:

  • Rework and repeated project modifications because of poor project definition.

Turnover:

  • Thinking that the situation will be different elsewhere, they look for another job.
  • There are estimates that analysts quit every 12 -18 months on average.

Low team performance:

  • Negativity develops. Even a single team member's dissatisfaction can diminish the overall performance.
  • Work quality suffers because analysts aren’t motivated to “go the extra mile.”

Cumulative mistrust:

  • Analysts begin to worry more about getting it wrong than coming up with innovative ideas.


The human cost.

In the previous newsletter, we highlighted economic and performance consequences. But the human cost may be even more disturbing. Entire teams of trained professionals working in circumstances where appreciation is rare and contributions are ignored (or worse, rejected). Analytic leaders devoting time to protecting their people. Professionals focused on avoiding mistakes, rather than imagining solutions.

When I work with analytic teams, we focus on systems that fix the communication problem. Of course this results in better efficiency, less rework, and improved business value.

But the most compelling indicator of change is when someone tells me (a direct quote): “Everyone’s happier. I’m happier when I know I am delivering value. The people I work with are happier because I am meeting their needs.”

Maybe we can’t put an exact financial number on it, but to me, creating a rewarding work environment may be the most important outcome of all.

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To see if we can help you improve your work environment, reach out and schedule a call here:? www.analytic-translator.com.

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(You can read the full report here: LINK)

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Absolutely, the cost of miscommunication in the realm of analytics and business can be profound. As George Bernard Shaw once said, "The single biggest problem in communication is the illusion that it has taken place." Enhancing this bridge through skills training is invaluable. ?? By the way, for organizations passionate about making data-driven decisions for a greener planet, there’s an exciting sponsorship opportunity for the Guinness World Record of Tree Planting. Thought you might be interested! Explore here: https://bit.ly/TreeGuinnessWorldRecord ???

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Absolutely, the gap in understanding between analytics and business teams can lead to significant lost opportunities. As Stephen Covey once eloquently put it, "Seek first to understand, then to be understood." ?? It's essential for fostering a culture of effective communication and shared goals. #BridgeTheGap #StephenCoveyWisdom #CollaborativeSuccess

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Nicole Luke

Uncover the power of building a data mindset

9 个月

Wendy Lynch, Ph.D. While I agree that communication is a real challenge, I also wonder if lack of process is also a contributing factor? Oftentimes analytics teams feel like order takers and both business leaders and analysts don’t have a good requirements gathering process, which can result in getting it wrong the first time.

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