Quantifying The Return On Investment For Your Data Team
Aug 6, 2023
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Exploring how to calculate the ROI for data teams, the podcast covers methods of measuring ROI, collecting and analyzing data for efficiency, optimizing queries, generative AI, innovative approaches to ROI, and the biggest gaps in data management tooling.
Measuring and tracking the ROI of data teams helps align their work with organizational goals and ensure business growth.
Quantifying ROI involves considering people costs (such as payroll) as well as qualitative and quantitative measures of impact and cost savings.
Challenges in measuring ROI include identifying the right metrics, aligning data team activities with broader goals, and prioritizing technical debt for better productivity.
Deep dives
Motivations for Measuring Data Team ROI
The motivations for organizations to measure and track the ROI of their data teams often arise when data teams are hired or created with the promise of accelerating the business. Organizations invest significant resources, including people, management, infrastructure, and time, in data teams. As a result, they want to ensure that the data teams are aligned with the goals of the organization and are driving business growth. Measuring ROI helps answer the question, 'What did the data team deliver?' and provides insights into the alignment and impact of the data team's work.
Calculating Investment and Return for Data Teams
When it comes to tracking the investment and return of data teams, people costs, such as payroll, tend to be the biggest driver of the cost aspect. For instance, building and maintaining a team has inherent costs in addition to the costs associated with platform and tooling. Data teams can use qualitative and quantitative information to calculate return. This could include gathering feedback from stakeholders and customers to understand the value and impact of the data team's work. Additionally, cost savings can be another measure of return, such as identifying gaps in billing systems or reducing time spent on troubleshooting and data anomalies.
Challenges and Considerations for Measuring ROI
Measuring the ROI of data teams presents various challenges and considerations. One challenge is identifying the right metrics to measure. For example, some organizations measure ROI through the impact of data products on the business, how data teams improve uptime and resolution time, or the Net Promoter Score (NPS) of their data consumers. Another consideration is the need to align data team activities with the broader goals of the organization. Data teams should focus on working on data products that matter, addressing customer problems, and collaborating with stakeholders. Overall, flexibility, continuous evaluation, and adapting to changing metrics and goals are key to measuring and improving ROI for data teams.
Importance of Prioritizing Technical Debt and System Visibility
As organizations grow, data teams need to make trade-offs between spending time on technical debt and ensuring that systems work effectively. Technical debt hinders data team productivity and holds them back from achieving their goals. It is crucial for data teams to prioritize their activities based on the needs of the business. Understanding the current state of the organization helps data teams allocate their time effectively between technical debt and other important tasks.
Framework for Categorizing Data Team Activities
A robust framework has been developed to categorize the different activities of data teams. These activities can range from driving growth initiatives to optimizing operations, scaling initiatives, and developing new capabilities across various teams such as marketing, customer success, HR, product development, and engineering. By categorizing these activities and mapping them against the different teams, data teams gain valuable insights into where their expertise can bring the most value in supporting the business. Having a clear view of the organization's needs allows data teams to prioritize their efforts and communicate their impact effectively.
As businesses increasingly invest in technology and talent focused on data engineering and analytics, they want to know whether they are benefiting. So how do you calculate the return on investment for data? In this episode Barr Moses and Anna Filippova explore that question and provide useful exercises to start answering that in your company.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing Barr Moses and Anna Filippova about how and whether to measure the ROI of your data team
Interview
Introduction
How did you get involved in the area of data management?
What are the typical motivations for measuring and tracking the ROI for a data team?
Who is responsible for collecting that information?
How is that information used and by whom?
What are some of the downsides/risks of tracking this metric? (law of unintended consequences)
What are the inputs to the number that constitutes the "investment"? infrastructure, payroll of employees on team, time spent working with other teams?
What are the aspects of data work and its impact on the business that complicate a calculation of the "return" that is generated?
How should teams think about measuring data team ROI?
What are some concrete ROI metrics data teams can use?
What level of detail is useful? What dimensions should be used for segmenting the calculations?
How can visibility into this ROI metric be best used to inform the priorities and project scopes of the team?
With so many tools in the modern data stack today, what is the role of technology in helping drive or measure this impact?
How do your respective solutions, Monte Carlo and dbt, help teams measure and scale data value?
With generative AI on the upswing of the hype cycle, what are the impacts that you see it having on data teams?
What are the unrealistic expectations that it will produce?
How can it speed up time to delivery?
What are the most interesting, innovative, or unexpected ways that you have seen data team ROI calculated and/or used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on measuring the ROI of data teams?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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