Business team analyzing charts and graphs.

One Conversation That Can Make Your Business Analytics More Effective

Jan 08, 2023

If you’ve worked in business analytics, the following scenario might be familiar.

Your analysis is done! Results are packaged into a neat, structured report. You deliver them, proud of your team’s hard work and relieved to complete the project on time.

You expect appreciation from the business team. Or, at the very least, positive acknowledgement for the effort: a job well done!

Not quite.

Instead, business leaders respond with disappointment, questions, or objections.

This isn’t what we asked for, they complain. Why doesn’t it tell us what we need to know?, they demand. I thought you were going to help us make a business case… what is this? 

Or the findings are misinterpreted, believed to say something they don’t. 

Internal dialog among the business team: How come we never get what we need?

Internal dialog among the analytic team: Why can’t they ever ask the question they need? Don’t they understand data? 


These project failures are common. In fact, they are the norm.

Over 80% of analytic projects produce no business value.

Let that sink in. Four out of five projects produce no business value. 

That’s a lot of wasted work. And a lot of discord.

It’s something we don’t talk about.

Perhaps we don’t realize it—or we think the miscommunication is unique to our situation, rather than a widespread phenomenon happening at organizations across the globe.

Trust me, ineffective analytics is the rule, not the exception. 

Now, don’t get me wrong. I’m not saying that companies generally lack talented data scientists.

Or that they don’t have adequate tools.

Or they don’t know how to apply sophisticated analytic techniques.

Lack of skills and resources is not the primary problem.  

The problem is that business teams and analytic teams don’t speak the same language, and aren’t equipped to bridge the communication gap effectively.

While these problems are often systemic, there is a way to begin collaborating more effectively. 

Before the request even comes. Have a conversation about priorities.

What matters most to your (internal or external) client? 

I find that analysts often don’t know. They respond to specific questions being asked. But they lack context. 

Outside of specific requests, have a conversation with your client.

Set the stage by letting them know your intention.

Something like, “I often find that it’s helpful for the analytic team to understand more about a business team’s current and upcoming priorities. Would you have a few minutes for a conversation about your goals for the next (quarter/year)?

Then, in your conversation, allow the other person to give unrushed, thoughtful answers by using open-ended questions.

Some examples:

  • Tell me about your current priorities and what you’re working on.
  • What else is your team focused on right now?
  • How is this project useful to the business?
  • How will you know you have succeeded in this objective? 
  • If there were ways that the analytic team could help you, what would those be?


Listen to their answers attentively without interrupting.

Avoid the temptation to dig into small details.

Pay attention and let them keep talking.

Listen for both what is important and why it is important. 

Don’t use this conversation to offer your own ideas.

Simply listen.

(Launching into suggestions only gives the impression that instead of listening, you really wanted to tell them how to do their job.)

Hopefully there will be future conversations where you can give input. Just not this one.

Clarify what you heard by summarizing it back to them and asking if you are correct.

Thank them for sharing and ask if they are open to future conversations in the future if you have other questions. 

What are the purposes of this conversation? 

  1. First and foremost, your goal is to establish rapport and a direct line of communication.

    Authentic listening—done with a sincere desire to understand—builds trust. Trust helps us get through confusing or difficult future conversations successfully.

  2. Second, by getting a preview of priorities, you can spend time learning about these topics.

    Are there existing examples or published studies that can illuminate new ideas or expose challenges others have faced asking similar questions? What metrics and methods might you need? Are there things others have learned that you can share with your client that might shape their thinking? This gives you time to prepare rather than react. 

  3. Third, you begin to understand their terminology.

    As you listen, pay attention to their word choices, acronyms, and buzz words. Ask for clarification, if you need to. Get a sense of how they define and express what’s important. This helps you position findings in the most understandable way in the future.

  4. Fourth, this conversation gives you an appreciation for the context of requests.

    When we understand what’s behind the analytic question, it informs not only how we do the work, but also how we present the results. Who is the ultimate audience for the results, and what do they understand about these data? Is there a huge consequence (such as a decision to discontinue a project and fire people)?  

As an example, I had a conversation with an internal client, Tim, who worked in HR.

The company was facing a significant increase in turnover.

He let me know that in the near future he had planned to ask us to document who was leaving, from which locations, and which jobs. 

Tim noted that turnover was threatening company performance and that the CEO was putting pressure on him to come back with solutions.

The more Tim talked, the clearer it became that it was turnover of new employees that seemed to be of greatest concern. 

It was clearly a stressful situation for Tim, and the senior leadership team wanted answers.

I expressed empathy and listened to his concerns.

After listening, I met with my team to think about the steps we could take to help Tim not only quantify the problem, but also begin to envision solutions.  

We gathered evidence from previous work we had done, identifying additional data sources it would be useful to add.

For example, we knew that tenure, timing of performance-based bonuses (from compensation data), time off (from PTO records), caregiving events (from family medical leave), and levels of team engagement (from departmental engagement surveys) were useful in predicting the risk of quitting.

We then laid out analytic steps we could suggest, along with data requests to add the additional metrics. 

We planned a sequence of analyses to produce results that Tim could deliver to the CEO with both insights and actionable solutions.

Tim was unaware of the potential to predict turnover, or the connections between these other factors and turnover likelihood.

He was excited about the potential to better understand the problem and offer solutions.

Plus, we planned to deliver it in a format that Tim would be comfortable presenting and that the CEO could easily grasp.

Without the original conversation, Tim would have sent a quick request: Please give me turnover rates by location, job, and demographics.

We would have produced a report, but without awareness of its underlying importance, or access to metrics that would identify potential causes. 

We were proactive and prepared to deliver what Tim needed in a format that best suited his needs.

He became an appreciative partner.

Opening a dialog helps future projects go more smoothly.

As we see in the example with Tim, a broader perspective encourages collaboration.

When business teams and analytic teams have regular interactions, data and analytics become natural tools to investigate and answer questions.

Business teams, even those who may not have a data orientation, begin to wonder how analytics might inform their decisions.

What better time to schedule a meeting about priorities than January? 

To start the year in a more collaborative way, reach out and schedule a conversation about each other’s most important challenges. It can lead to better analytic outcomes, and quite possibly a more productive partnership.

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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