Anu Oladele, Chief Data Officer at AfterData.ai, and Daniel Zhang, the company’s Chief Executive Officer, join Robert Lutton, Vice President of Sales and Marketing at Sandhill Consultants, in a video interview to discuss AfterData.ai’s core philosophy, the four key metrics that anchor its framework, challenges in optimizing data management costs, obstacles preventing data leaders from demonstrating ROI, and how AfterData.ai helps bridge these critical gaps.

Oladele begins by shedding light on the core philosophy of AfterData.ai, which is to arm CEOs and data leaders with a structure. This structure should enable leaders to facilitate the flow of data, anticipate bottlenecks, and have the right resources at the right time to manage the data.

“They need to also be able to support the creation of a clear roadmap and flexibility needed to course correct when things don’t go as planned,” he adds.

Next, Oladele emphasizes the need for a shared language within organizations, allowing leaders to collaborate seamlessly and communicate effectively. He reveals that AfterData.ai has identified four key metrics to anchor the framework: Cost, Volume, Quality, and Usage.

The cost metric enables understanding the financial footprint of data. Regarding quality, he says, “Quality metrics are less about avoiding mistakes, truthfully. It’s also about building a culture of trust. This is key in managing data. When data is hard to trust, it’s very hard to use.” The quality metric helps early detection of issues, allowing leaders to respond before they turn into costly mistakes.

Speaking of usage, Oladele states that measuring how business consumes data can unlock deeper strategic value. He elaborates that understanding which applications, AI models, and BI tools benefit the most from data is critical. Identifying underutilized data sets could lead to redirecting resources for a better impact on the company’s objectives.

“Ultimately, with these, we aim to help CDOs avoid chaos within their data estates and turn them into well-organized, high-performing assets,” Oladele affirms.

Speaking of the pressing pain points of cost efficiency of data management and balancing innovation with cost control, Zhang shares how data leaders are stretched thin in their roles.  Drawing from numerous conversations with CDOs and heads of data, he notes that they are the busiest executives.

Elaborating, Zhang says many data teams are operating with limited resources and data and analytics functions, and the leaders are running non-stop. “Data leaders are struggling to secure the necessary investments to run their data functions properly, and running data costs; running data and AI functions is costly,” he adds.

Based on his conversations with data leaders over the years, Zhang outlines specific pain points. For instance, many data leaders have been on the path of building foundational unified data platforms and are facing challenges in gaining sponsorship.

Business units work in silos and hire their own data scientists and engineers without aligning with the head of data, says Zhang, and this pain point stems from the difficulty in demonstrating the cost efficiency and value of the platform.

The other pain points include ineffective data governance frameworks, high communication costs with business executives, and unclear role boundaries with IT.

To achieve the right balance, Zhang points out that leaders must focus on two things:

1. Demonstrating the ability to build data products efficiently

2. Building data products that drive business value

He maintains that data leaders need to craft strong value propositions to drive data projects.

Building on that, Oladele states that while data leaders face a range of obstacles, much of it boils down to the two key elements of support and trust. Taking up the example of the CMO and marketing funnel, he highlights that the funnel keeps those leaders aware of the journey of a potential customer.

Explaining further, Oladele says that it is a living, dynamic system that adapts to customer behavior, market trends, and external forces. Similarly, Chief Sales Officers leverage the sales pipeline framework to track every stage.

“When we look at these different leaders, what we find is that they’re ultimately more dynamic because they operate with a common framework — one that produces metrics everyone understands. This is really what we want to do for data leaders. A similar framework should be in place for CDOs so that they can enable their business,” Oladele adds.

Having key insights into the cost of maintaining data estates, the volume of data generated, the quality of data, and the direct impact on application performance, AI models, and BI consumption is critical, says Oladele. With this, the data leaders are better positioned to optimize their data before it becomes a liability.

Moreover, they can also determine whether the data is genuinely benefiting the business. “Truthfully, they should be armed with a framework and be able to speak the language of impact, cost, and value with their peers, and ultimately, be able to demonstrate ROI,” Oladele says.

Commenting on how AfterData.ai empowers data leaders to bridge the gap, Zhang states that the keyword is “demonstrate.” He adds that the focus is on framework, and according to him, CDOs and data leaders need to balance both the reality and perception of data.

“Most of them already realize they need to invest the effort to keep reality and perception in sync,” says Zhang. He further mentions that while the core metrics remain the same, the challenge lies in implementation.

While most data leaders measure and monitor them to some extent, AfterData.ai accelerates the process of building out what he calls a “monitoring engine,” which is a structured, holistic system where these four metrics are integrated and streamlined.

As an advantage, data leaders do not have to overstretch their resources to capture and communicate the value the team delivers. Pointing out a common scenario, Zhang says, “When data leaders are struggling to gain support and resources, the solution in front of them would actually require significant resources.”

Referring to the CMO example, he mentions the efforts and dollars it takes to build a customer data platform. In contrast, AfterData.ai helps data leaders by reducing the time and effort it takes to implement those metric measurements and monitoring systems, concludes Zhang.

CDO Magazine appreciates Anu Oladele and Daniel Zhang for sharing their insights with our global community.