Lessons from data leaders on fixing the process, engaging stakeholders, and building the right skills.
Scaling data analytics is no easy task. If you’re a Chief Data Officer (CDO) or a data leader, you’ve probably faced at least one of these roadblocks:
- Business stakeholders not fully engaged or unclear about their role in data initiatives.
- Analytics processes that are slow, inefficient, and fail to generate real ROI.
- Skill gaps—both technical and non-technical—that hold back execution.
You’re not alone. Many CDOs share these struggles, but some have found ways to break through and turn data into a true business asset. In this article, we’ll explore real-world stories from CDOs and what they did to overcome these challenges and scale their data analytics efforts successfully.
Challenge 1: Getting Business Leaders to Fully Buy Into Data
Case Study: When Business and Data Teams Don’t Speak the Same Language
Alex, a CDO at a large institution, was leading a data monetization project—a big investment aimed at modernizing infrastructure and selling data-driven products. Sounds exciting, right?
The problem? Business leaders and the data team weren’t aligned.
- Each group had different ideas about who was responsible for what.
- There was duplication of effort, and too much data was being created without a clear business purpose.
- Only 70 percent of the data was clean enough to migrate, leaving the rest to be manually fixed—an expensive and time-consuming mess.
What This Cost the Business
- Deals were lost to competitors with cleaner, more accessible data.
- The company fell behind on its modernization efforts because of internal inefficiencies.
How CDOs Can Fix This
Data alignment starts at the top. If leadership doesn’t understand how data fits into the bigger picture, data teams will always be playing catch-up.
What You Can Do Right Now:
- Align key business metrics to ensure every data initiative has a clear purpose.
- Create a single source of truth (SSOT) so teams aren’t duplicating effort or working with conflicting data.
- Prioritize high-value use cases where data can drive measurable ROI quickly.
Challenge 2: Slow, Inefficient Analytics Processes That Kill ROI
Case Study 1: When Agile Frameworks Slow Everything Down
Maria, a CDO in the CPG industry, was tasked with using data analytics to drive $1 billion in revenue and cost savings.
To do this, the company formed data transformation teams and required them to use Scrum and Agile methodologies to build new digital products.
Here’s where things went sideways:
- Scrum didn’t work for data science projects. Data scientists couldn’t fit their workflows into the sprint structure.
- 80 percent of teams missed deadlines, burning through $20 million with little to show for it.
Case Study 2: When Data Requests Go Nowhere
Paul, a CDO in manufacturing, was responsible for rolling out a process automation system.
The plan was simple:
- Business leaders submitted data requests for dashboards and reports.
- The data team processed the requests using the existing fulfillment system.
- Everyone got the data they needed.
Except…
- Only 20 percent of requests made it through the process.
- The other 80 percent were delayed so long that they became irrelevant.
What This Cost the Business
- A huge waste of time and money on processes that weren’t working.
- Missed opportunities to make data-driven decisions in real time.
How CDOs Can Fix This
Most data analytics processes aren’t built for speed or business impact—they’re built for data team efficiency.
What You Can Do Right Now:
- Measure time-to-value instead of productivity—how long does it take to deliver meaningful insights?
- Map out the entire data pipeline and look for bottlenecks—where is time being wasted?
- Streamline workflows so business users get faster, more relevant insights.
Challenge 3: Building Data Teams with the Right Mix of Skills
Case Study: When Dashboards Don’t Lead to Better Decisions
John, a CDO in the airline industry, led an analytics project to improve Monthly Business Reviews (MBRs).
The team built a beautiful, technically sound set of dashboards—but when executives started using them, they were frustrated.
- The data wasn’t answering the right business questions.
- Insights weren’t actionable—leaders still had to rely on gut decisions.
- The team didn’t find out about these issues until after months of work.
What This Cost the Business
- Executives lost trust in the data team.
- The next iteration of dashboards wasn’t much better because the underlying skill gaps weren’t addressed.
How CDOs Can Fix This
Technical skills alone won’t make data useful—teams also need critical thinking, collaboration, and stakeholder engagement skills.
What You Can Do Right Now:
- Embed training and coaching into real-world projects so teams learn how to translate data into business impact.
- Work with stakeholders to define high-value use cases—this creates buy-in and accountability.
Key Takeaways: How CDOs Can Scale Data Analytics Successfully
1. Align Business Leaders and Data Teams from the Start
- Don’t assume stakeholders understand their role—make expectations clear.
- Connect data initiatives directly to business goals to avoid wasted effort.
2. Fix Inefficient Analytics Processes That Slow You Down
- Stop measuring productivity—focus on time-to-value instead.
- Find and fix bottlenecks—where is your process breaking down?
3. Develop Data Teams with the Right Skills
- Train teams in problem-solving and stakeholder communication—not just technical skills.
- Make sure insights are relevant and actionable—data alone isn’t enough.
Scaling data analytics is hard, but the good news is these challenges can be fixed. By focusing on alignment, efficiency, and skill development, CDOs can turn data into a competitive advantage—not just another line item on a budget.
About the author:
Susan Stocker partners with Digital Transformation executives, Data leaders, Software Development/Agile leaders, and other functional leaders, to align and transform the workforce through the adoption and scaling of new digital and data-driven ways of working.
Susan is a passionate advocate for transforming human and process performance in the digital world. For 9 years she served as the first Global Learning Leader at GE Digital. For the past 6 years she has worked as an L&D consultant for clients like Mayo Clinic’s Center for Digital Health and Kraft Heinz Digital Transformation Division.
As Aryng’s Principal Data Literacy Consultant, she has worked with many clients to define and implement a practical, ROI driven roadmap that streamlines the cross-functional data analytics process, aligns decision-making, and scales skill building.
Susan is also a deep, hands-on practitioner – a certified Master Back in Lean and Six Sigma, a Certified Citizen Data Analyst. a Certified Scrum Master, and a Certified Change Acceleration leader. This expertise gives her the ability to share unique, data driven insights into the combined effects of human and process performance, and its impact on delivering strategic business results.
