Thought leadership
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November 15, 2023

Transforming Your Data Team: From Cost Center to Value Center

Data teams have often been perceived as cost centers, but does it have to be that way? We don't think so.

Kyle Kirwan

In the world of enterprise companies, data teams have often been perceived as cost centers, primarily responsible for collecting, storing, and managing data without directly contributing to the bottom line. This traditional perspective has led many organizations to view data initiatives as necessary expenses rather than strategic investments. 

However, there is untapped potential within data teams. These teams can evolve from being mere cost centers to becoming strategic assets that drive substantial business value. In this blog, we’ll explore how and provide tips for data leaders looking to create more value-driving data teams.

What Is a Cost Center? 

A cost center is a department or unit within an organization that incurs operating costs without generating direct revenue. HR and customer support organizations are other examples of traditional cost centers. Cost centers are typically focused on minimizing incurred costs rather than maximizing the bottom line. 

Historically, technical organizations including data and IT have often been classified as cost centers. This is, in part, because they are responsible for managing the underlying technology stacks that businesses rely on and that are recognized as operating expenses on the balance sheet. But another important reason is the fact that results of data teams are usually intangible or difficult to tie to business outcomes.

When data initiatives do not produce immediate and measurable results or when the impact on revenue is not evident, stakeholders may view data teams as cost centers.

Data teams often face difficulty in attributing their contributions to specific outcomes because their work involves guiding and assisting cross-functional teams (e.g., engineering, product, design) in making data-informed decisions. The collaborative nature of their role can make it challenging to definitively quantify their impact on specific project components.

Recognizing Potential Value

Data teams, when empowered and positioned as value centers, can deliver substantial benefits. 

Data teams can provide accurate, timely, and actionable insights to support decision makers across the organization. These insights enable data-driven decision making, leading to more effective strategies and improved outcomes.

By analyzing data, data teams should proactively identify trends, opportunities, and threats that may not be apparent through traditional methods. This strategic perspective can help organizations pivot quickly, adapt to market changes, and gain a competitive edge.

Several organizations have successfully made the transition from viewing their data teams as cost centers to recognizing them as value centers. For instance:

Netflix: Netflix, once known primarily as a DVD rental service, transformed into a global streaming powerhouse. Central to their success was their data team's ability to analyze user viewing habits and preferences and make fast, informed business decisions based on them. This data-driven approach led to personalized content recommendations, which significantly contributed to subscriber growth and retention.

Amazon: Amazon's data team has played a pivotal role in the company's evolution. They use data to optimize supply chain operations, personalize the shopping experience, and enhance customer satisfaction. This has propelled Amazon to become one of the world's largest e-commerce and cloud computing companies.

These examples showcase how organizations that shifted their mindset from cost-centric to value-centric data teams not only survived but thrived in today's data-driven business environment.

To achieve success like these organizations, it's necessary to align your data team's work with your business goals. When data teams are seen as value centers, they offer timely and accurate insights that help with decision-making. By analyzing data effectively, they discover trends and opportunities that traditional methods might miss, giving you a competitive edge. When your data team's efforts match your business objectives, you set the stage for positive changes that lead to better outcomes and long-term success in a data-driven world.

Demonstrating Value

To demonstrate how a value-focused data team can positively impact a business's objectives, let's explore some practical approaches for data leaders:

Set Key Performance Indicators (KPIs): Start by setting clear KPIs that directly connect your data efforts to the desired business results. For instance, if your company aims to improve customer loyalty, the data team can gauge the effectiveness of customer engagement strategies by tracking KPIs such as reducing churn rates.

Manage Costs: Evaluate whether your current infrastructure is still necessary. If your operations are cloud-based, consider optimizing your expenses in that area. It's essential to ensure that you're getting the most value for your money.

Facilitate Collaboration: The primary purpose of having a centralized data team is to gather all business-related information in one place. This allows for better collaboration between different departments, both internally and externally. For example, if the sales team requires new reporting tools, the data team can work on reducing the time it takes to deliver these reports. This way, you can provide the sales team with the necessary information more quickly.

Accelerate Data Integration: Businesses often need to blend data from various sources, such as Salesforce and product data. The data team can focus on finding efficient ways to merge this data seamlessly to meet the company's needs. The goal is to make data integration smoother and more efficient.

Create Case Studies: Create real-life examples or success stories that demonstrate how data-driven insights have positively impacted the business. Share these stories within the company to illustrate the value your data team brings.

Stay Visible: Regularly provide updates to senior leadership and business stakeholders, showcasing the progress and influence of data projects on business goals. Use visuals and clear, non-technical explanations to make this information easy to understand.

Set Up Feedback Loops: Set up systems for gathering feedback from different departments. This feedback will help to refine each project the data team works on and understand how it affects other departments. 

Deliver Products: Data products are a new approach to driving data value that allow the data team to create and deliver a complete “product” instead of simply completing projects. Data leaders can define a challenge or opportunity in the business, and then use their existing tech stack to build and deliver a product to help business users engage with data and solve this project. Data products can be customer-facing or internal-facing and help ensure that data initiatives are focused on value rather than just utility. 

The transformation of data teams from cost centers to value centers is not just a theoretical concept but a practical reality demonstrated by organizations like Netflix and Amazon. These companies leveraged the power of data to help them achieve the success they have today. 

To follow in their footsteps, aligning your data team's efforts with your business goals is crucial. 

Setting clear KPIs, optimizing costs, fostering cross-functional collaboration, and streamlining data integration are all actionable steps to ensure your data team becomes a driving force behind your business's success. Additionally, using case studies, maintaining high visibility, and establishing feedback loops will help solidify the value your data team brings, making it a truly indispensable asset.

Looking for an even deeper dive into this topic? Listen to our podcast episode here.

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Resource
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
$15,000
3
12
$540,000
Data analyst
$12,000
2
6
$144,000
Business analyst
$10,000
1
3
$30,000
Data/product manager
$20,000
2
6
$240,000
Total cost
$954,000
Role
Goals
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Monitoring
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights

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