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Qin Li
Qin Li
Head of Finance and Admin
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Updated
July 2, 2024
| Published

Bridging the Gap: How Data Teams Can Communicate Their Value to the Business

Qin Li
Qin Li
Head of Finance and Admin
Bridging the Gap: How Data Teams Can Communicate Their Value to the Business

Summary:

  • Understand Value Drivers: Data teams must grasp the company's business model and data challenges to quantify their team's impact. Translating data improvements into business outcomes like cost savings, revenue growth, and better risk management helps secure additional investment.
  • Use a Business Value Framework: Employing a structured approach to categorize value into operational efficiency and growth opportunities, supported by ROI analysis, makes the case for data initiatives more credible and understandable to business leaders.
  • Share Success Stories: Anecdotal evidence from peer companies or industry leaders who benefited from improved data quality can make the benefits tangible and relatable, helping build a compelling case for investment in data initiatives.

Data teams play an important role in modern business. From revealing insights that drive exceptional customer experiences to uncovering previously-hidden relationships that expose new opportunities to grow the business, the work of the data team has the power to transform organizations. But too often, the true value of the data team's efforts goes unnoticed, overshadowed by the complexity of data itself and the team’s inability to effectively communicate the impact of their work.

That’s why it’s critical for data teams to effectively communicate the value of their work to stakeholders across the business. Doing so ensures that the business doesn’t just recognize the benefits of improved data quality, but also its role in improving decision-making, boosting operational efficiency, and driving overall growth. 

In addition, when the data team clearly communicates the value of their work, and contributions to the company as a whole, they can more easily justify the investment in new technologies or incremental staff to help improve data quality. As a CFO, I’m usually on the receiving end of these communications, which is why I’m sharing my thoughts on how to articulate the business value of data initiatives. Hopefully my insights will help you and your team to tap into more resources such as additional budget, new technology, or incremental headcount. And who knows, it might even help to advance your career!

3 Ways to Effectively Communicate the Value of the Data Team

1. Understand and Articulate Business Value

Understanding and articulating the business value of data initiatives is a fundamental piece of any effective communication. And to do so effectively, you must grasp your company’s business model, identify the corresponding data challenges, and quantify the value of your team. For example, show how improvements in data quality lead to better insights which, in turn, translate into cost savings, revenue growth, and better risk management. By translating your work into outcomes that resonate with the business, you will be more likely to grab their attention and secure their buy-in for your initiatives. 

2. Use a Business Value Framework

Using a structured approach to demonstrate value can make a significant impact. I recommend employing a framework that categorizes value into operational efficiency and growth opportunities. Operational efficiency might include the cost savings that result from improved processes, while growth opportunities could encompass upsell and cross-sell opportunities that result from having better insights about your customers. In fact, in my experience, cost savings alone can often justify the investment in better data quality. Providing a theoretical framework lends credibility to your arguments, and when you pair it with a detailed ROI analysis, it enables you to quantify the benefits in terms the business understands. While the exact numbers may vary, having a structured approach helps drive a more productive conversation.

3. Share Success Stories

Anecdotal evidence is powerful. Sharing stories of how peer companies or leaders in your industry have benefited from improved data quality can help build a compelling case for investment. For instance, one company might have saved millions by reducing data processing errors, while another increased their market share through more accurate customer segmentation. These stories make the benefits tangible and relatable, helping your business counterparts imagine the benefits better data quality will have within your organization.

Steps for Getting to Business Value

To sum it up, effectively communicating the business value of data initiatives requires you to follow these simple steps:

  1. Understand the business model & strategic goals: Know how the company makes money and be clear on the business’s short- and long-term goals. Identify the KPIs that matter the most to your business counterparts, such as dollar savings or percentage improvements, and demonstrate how better data quality will help them achieve their goals.
  2. Apply assumptions and references: You may need to make some informed assumptions based on your research and reference outside research for similar projects.
  3. Identify pain points: Articulate key pain points for your business counterparts, what they are (or are not!) doing about them, and why existing mechanisms are not working. Then, explain why it is critical or beneficial to address these pain points to achieve the company’s collective strategic goals. 
  4. Measure outcomes: Define the desired outcomes or improvements as well as how you define success. Show how these outcomes align with the company’s strategic goals as well as how they directly impact your business counterparts.

Effective communication between data teams and business counterparts is essential for realizing the full value of data initiatives. By understanding the business context, using structured frameworks, and sharing success stories, data teams can bridge the gap and demonstrate the substantial benefits of improved data quality.

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