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The Hidden Cost of Miscommunication Between Business and Analytic Teams

Jun 03, 2022

Companies are doubling down on big data. From manufacturing to agriculture, healthcare to entertainment, leading companies are putting analytics at the forefront of growth plans. Over 90% of strategic plans in 2022 included desired advancements in data capabilities.

And that growth will continue.

The number of data science jobs will increase 33% between 2020 and 2030.

The global market for big data analytic services will more than double from 2020 to 2030. 

While these investments will certainly advance corporate efforts to be more data driven, they come with a less-recognized consequence: a chasm growing between business professionals and data professionals. A miscommunication cost in business

Part of this reflects how analytic methods are becoming increasingly more complex. More and more companies are applying AI and Machine learning, which require ever-more specialization. It’s not the same statistics as it was a generation ago.

We are getting more and more specialized in our analytics and less and less able to explain what the results mean.

In fact, methods and open-source tools are changing so rapidly, academic journals worry that few researchers are even qualified to peer-review their colleagues’ findings.  

What about everyday business?

Caught in this perfect storm of analytic evolution, corporate leaders face the daunting task of keeping up: hiring analytic talent, building a data-driven strategy, and applying new insights to day-to-day decisions. In this It’s not surprising that over 80% of businesses don’t yet have a coherent data strategy. 

It’s also not surprising to learn that only 20% of analytic projects produce measurable business value. Converting key business challenges into defined analytic projects, or complex results into operational procedure isn’t easy. The communication costs in business are rising as quickly as the increase in the use of Big Data.

In reviews I mentioned before, industry experts estimate that only one-in-five analytic projects produce business value. And, about half of those failures are due to lack of collaboration, miscommunication, and departmental silos. 

While Big Data has made big promises, often those possibilities get lost in the realities of everyday business. 

What is the effect of miscommunication in business?

What are the implications of such frequent failures in business analytics? Let’s say conservatively that 50% of analytic projects fail and half of those are due to miscommunication; a failure of one in every four projects. We can categorize the cost in three categories.

1. Wasted time and effort

When communication is inadequate, it results in incorrect analytic design, rework, misunderstood results, or missed opportunities to apply the insights. With our assumption of 25% failure due to communication, this means the time, pay, equipment, and benefits costs of one in every four analysts is wasted. 

At the low end, we might estimate a total FTE cost (in pay, benefits, equipment, and benefits**) of about $150,000. Applying this example to a team of ten analysts, this equates to $375,000 (2.5 FTEs of cost and lost effort). Already, this is a significant financial loss rooted in the miscommunication costs in business.

2. Frustration and discouragement (i.e., turnover and low productivity)

Talented employees need to feel appreciated and successful to bring their best effort to work. Faced with leaders who do not understand or value their abilities, a large portion of data analysts are unhappy in their jobs. 

Those who stay while unhappy are absent more and producing less, costing about 34% of their salary annually. Others simply leave. On average, data scientists quit their jobs in 20 months. They leave because their skills are not used, they don’t feel connected to the business, or don’t see a pathway to contribute meaningfully.

Once they do quit, it will cost the organization six to nine months of salary to recruit, hire and onboard a replacement. 

Altogether, disengaged analysts will produce a third less value and quit in less than two years.  For ten disengaged workers, five will quit this year, and the remainder will be under-producing by 34%. In total, a cost of $375,000 for replacement and $165,000 in lost productivity. These employees are representative of miscommunication costs in business.

3. Lost opportunity

Wasted time and hiring are fixed costs that feel tangible. But I can’t help but wonder if the greater potential cost is the inability to capitalize on hidden insights that can be unlocked in company data. 

What opportunities are we missing?
What value have we yet to uncover with a more insightful use of our data?

Imagine what might be possible if business leaders better understood what is possible with their data.  Or if data managers grasped the nuances of important business challenges. 

Today, there is a huge chasm between what is possible and what actually gets done. When business and analytic teams begin to perceive their data and analytic resources as a joint opportunity for creating business value, this produces far more potential benefit than avoiding direct costs. 

New discoveries might lead to a new product line. Research might provide evidence for a new, more convincing marketing strategy. Analysts could also identify inefficiencies in operations to help make the organization more profitable. These are just some possibilities to create revenue potential.

While not as easy to quantify exactly, the value of lost opportunity could be even larger than wasted time and effort.

I have been part of analytic teams that, working in sync with the business and sales teams, provided a powerful engine of valuable discoveries that advanced business objectives. 

In those collaborations, I feel comfortable estimating that the evidence boosted marketing effectiveness by at least 10%, and that nuanced analysis of potential buyers led to new products that increased revenue by another 10%, and that models predicting optimal operations efficiency probably improved profitability by 5% or more.  

Let’s be conservative and cut those possible revenue increases in half. Let’s also discount the profitability value (which is only a fraction of revenue) to 1% or revenue. For a company with $10M in revenue, that still could aggregate to a potential value of $1,100,000 in additional revenue. 

The total?

In all, for a team of ten data professionals in an organization with revenues of $10M, miscommunication (and under appreciation) has an estimated cost of:

  • $375,000 in wasted effort and time (even when trying hard), 
  • $540,000 in replacement costs and poor performance (when morale is low).  
  • $1,100,000 (minimally) in missed revenue from product development, marketing efficiency.

Or conservatively, about $2M in lost value—half in the hard costs of paying current workers for wasted effort and recruiting, hiring and training replacements for those who leave; and half in missed opportunities to be more effective and profitable.  

All because the company does not invest in building a culture where business and analytics teams collaborate effectively. Beyond money, the negative impact on culture will likely extend to anyone who works with analysts.

Now what?

Whether you accept the exact estimates above or not, disconnects between analysts and leaders create a communication cost in business. I see those consequences in almost every company, ranging from (at best) frequent rework to (at worst) blatant contempt. 

I use these examples to illustrate the value of analytic translators, people who operate in the nebulous chasm between complex analytics and business decisions.

Analytic translators don’t simply convert concepts from one terminology to another, they open lines of communication between the teams.

As a result, we notice positive outcomes, such as:

  • Building greater analytic understanding of the full scope of business needs.
  • Improving inter-group dynamics by working to include everyone, helping non-analysts feel informed and competent.
  • Building excitement among non-researchers about the incredible value of analytics by making it more accessible.
  • Making conclusions and implications clear to support subsequent business efforts, such as decisions or marketing.
  • Helping business leaders avoid eventual challenges and questions by explaining any serious drawbacks in understandable (but not overly dramatic) language.
  • Saving time by presenting the key findings efficiently, and only calling attention to details that have business ramifications.
  • Taking advantage of business expertise by making results tangible to their work.

By developing a more cohesive partnership between analytic and business teams, companies can avoid a large portion of unnecessary rework, improve morale in both teams, and open pathways to new sources of potential value.

As business leaders face the growing necessity for cutting-edge data capabilities—and the increasingly complex modeling and specialized talent required to achieve them—the potential for miscommunication between teams will only multiply. This means the costs of poor communication in business will multiply, too.

If you are business leader: hire or train analytic translators in your teams. If you are an analyst, expand your skills to help bridge the gap between these two critical—but disconnected—teams.

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** if we assume the analysts produces value equivalent to what they are paid.  Which is probably low.

Wendy D. Lynch's headshot photo.

Wendy D. Lynch, PhD

Wendy Lynch is an experienced sense-maker and data scientist with over 35 years of research experience, primarily in business settings. She has played the role of Analytic Translator for hundreds of companies, from start-ups to Fortune 100 corporations. Her expertise is both in data analytics and effective communication, combining the two into a framework for optimizing the value of analytics in a business setting.  Connect with her through LinkedIn or email.

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