.png)
.svg)
.svg)
.svg)
.svg)
Anjali Bansal, as the host of How I Met Your Data, engages with data and analytics leaders to examine the underlying issues in data governance and artificial intelligence (AI) adoption. Similar to our Data Leaders Uncovered blog series, her podcast focuses on candid discussions about how organizations navigate complex data landscapes and avoid common pitfalls.
Our conversation with Anjali centered on the recurring challenges organizations face in AI programs, the necessity of robust governance frameworks, and the importance of aligning AI implementations with organizational priorities.
According to Anjali, organizations rarely question whether their data is accurate, relevant, or legally compliant before using it in AI models. She observes a tendency to prioritize acquiring AI tools over investing in data quality and governance.
She described governance not as bureaucratic overhead but as a structural necessity for ensuring sustainable, effective AI outcomes. Without governance, initiatives often fail to deliver meaningful value and introduce regulatory and operational risks.
Anjali outlined three common challenges she observes across industries:
She emphasized that these challenges are human and organizational in nature, not purely technological.
Anjali noted that most enterprises she advises focus their initial AI efforts on internal operational efficiencies rather than customer-facing applications. This focus includes optimizing internal processes, improving reporting visibility, and reducing operational costs.
She highlighted that many organizations lack a comprehensive roadmap, resulting in fragmented projects with limited strategic impact.
When asked about the increasing prevalence of natural language processing (NLP) interfaces for querying data, Anjali expressed strong support. She stated that such interfaces reduce cognitive barriers for non-technical users, enabling broader organizational access to insights without requiring specialized skills.
To help organizations prioritize AI initiatives effectively, Anjali recommends a phased framework:
She cautioned against deploying high-profile AI solutions prematurely, which can expose organizations to reputational and compliance risks.
For Anjali, governance is fundamentally about establishing clear ownership, accountability, and context across technical and business teams. She expressed concern that many data engineering efforts prioritize data movement efficiency without verifying the appropriateness or compliance of the data involved.
She advocates for governance practices that serve as a connective framework between technical teams and business stakeholders, enabling responsible AI implementation.
Anjali reflected on the increasing use of agentic artificial intelligence systems, which consist of multiple autonomous agents performing discrete tasks in a sequential manner. These agents are designed to operate independently yet rely on transferring intermediate outputs to subsequent agents.
She noted that while individual agents may execute their tasks with high accuracy, the cumulative workflow is often vulnerable to failure during handoff points. These failures can arise due to inconsistencies in data representation, loss of contextual information, or the absence of shared state across agents. Such issues can propagate downstream, resulting in compounding errors that are difficult to detect without external intervention.
Anjali emphasized that human oversight remains a critical safeguard in these systems. Human operators provide contextual awareness, ethical judgment, and operational integrity checks that autonomous agents are currently incapable of replicating. She further highlighted that regulatory frameworks increasingly mandate human-in-the-loop models for high-stakes AI applications, particularly in sectors such as healthcare, finance, and public safety.
Anjali Bansal underscores that responsible AI requires more than technical capability. It demands rigorous governance frameworks, alignment with organizational priorities, and a sustained focus on human factors.
She advises organizations to prioritize foundational investments, adopt iterative implementation approaches, and treat governance as a strategic enabler rather than a compliance hurdle.
In a rapidly evolving AI landscape, such disciplined approaches distinguish successful, sustainable initiatives from those that fail to deliver business value.
📢 Follow the #DataLeadersUncovered series for more in-depth discussions with leaders shaping the data and AI landscape.
In this installment of Data Leaders Uncovered, we meet Angelika Kenward, Analytics Manager at a global events and media company.
Read Post
In today’s customer-obsessed world, it’s easy to talk about personalization, omnichannel journeys, and “data-driven CRM.”
Read Post
You don’t usually associate spreadsheets and dashboards with justice. But you should.
Read Post
On March 1, 2025, one of our clients received an unwelcome surprise in their inbox.
Read Post
AI is reshaping industries, but the fundamentals of data remain unchanged. In this conversation with Ashish Mohan, we explore why problem definition and statistical thinking still drive meaningful outcomes.
Read Post.png)
The streaming tv platform Tubi was launched in 2014. Its revenue had reached $150 million by 2019, and by 2023 the revenue was $900 million.
Read Post
In our latest installment of the "Data Leaders Uncovered" series, we sat down with Maxwell Dylla, a data scientist with a background in materials science engineering.
Read Post.png)
We’re excited to continue our "Data Leaders Uncovered" series by sharing insights from Prem K. Narasimhan, a seasoned executive in the global life sciences industry. Prem has been at the forefront of addressing some of the most complex challenges in pharmaceutical research, clinical trials, and data management. This conversation shines a spotlight on how innovation and strategic collaboration can drive meaningful change in the industry.
Read Post
Welcome to the second installment of our blog series, "Data Leaders Uncovered." In this series, we delve into insightful conversations with data leaders across industries, exploring their challenges, innovations, and lessons learned in navigating the ever-evolving data landscape. In this post, we continue our discussions by spotlighting Jared, A Director at W.E. Aubuchon Company; a hardware retailer based in New England.. We discussed the intricacies of managing retail data, the transition to modern tools like Snowflake and Power BI, and the opportunities presented by AI-driven solutions.
Read Post
We’re excited to start our new blog series dedicated to conversations with data leaders. Each post will explore their challenges, innovations, and lessons learned in navigating the ever-evolving data landscape. We will be highlighting challenges they have overcome, pain points and the achievements they take the most pride in.
Read Post
CRM systems have long served as the primary hub for customer data. Platforms like Salesforce and HubSpot excel at tracking customer interactions, managing sales pipelines, and providing basic reporting. These functions have made CRMs indispensable for sales and marketing teams seeking a centralized view of customer relationships.
Read Post
In recent years, the concept of fractional professionals has gained significant traction, especially in tech companies. Fractional executives, such as fractional CFOs, CMOs, and RevOps, provide their expertise to organizations on a part-time or project basis, offering a cost-effective and flexible solution for companies that may not require or cannot afford full-time executives in these roles.
Read Post.png)
A Go-To-Market (GTM) organization may find it beneficial to invest in a Data Warehouse (DWH) alongside their Salesforce CRM when they encounter specific indications that their data and analytics requirements have surpassed the analytics and reporting capabilities of Salesforce.
Read Post.png)
Data quality is a critical aspect of any data warehouse project. With an ever increasing number of data sources and technologies, scoping and prioritization is daunting. In this document, we share our data quality framework for planning data quality actions, current state assessments, and prioritizing data quality initiatives.
Read Post.jpeg)
In modern data pipelines, continuous data integration and data frequent updates may cause unintended data errors. Blue-green Deployment ensures a final checkpoint for accurate results. In this blog, details are provided on how to implement blue green deployment in a data warehouse project.
Read Post
Companies targeting mobile app publishers can use mobile app data to identify potential customers. This blog discusses using bulk mobile app data for sales outreach.
Read Post.png)
Discover how applied AI can automate tasks and enhance productivity in data analysis. Explore a real life example of Applied AI in report generation.
Read PostStay in the loop with everything you need to know.
%203-svg%20(2).png)
