Making Key Business Decisions Through Intelligence

Sam Wong is a seasoned professional in the data analytics industry who stresses the importance of data quality and its substantial influence on the outcomes of AI applications. He consistently emphasizes the significance of investing energy in mastering data, implementing quality controls, and establishing a robust governance process to ensure the data’s trustworthiness, accuracy, and completeness. He believes that organizations must embrace a deeper commitment to data quality and its management to achieve success in the realm of AI and business intelligence.

In an interview with Business Management Review Canada, Wong shares his insights on the challenges and emerging trends in the data analytics industry and the experience he has gathered in the domain.

COULD YOU EXPLAIN HOW THE CONVERGENCE OF TECHNOLOGY AND THE BUSINESS SIDE PLAYS A ROLE IN YOUR PROCESS OF IDENTIFYING THE MOST SUITABLE PARTNERSHIPS OR SOLUTION PROVIDERS IN YOUR FIELD?

When it comes to data governance and data quality, these endeavors have traditionally been the domain of large enterprise companies, particularly those within regulated industries such as banks and insurance companies. Such organizations have the financial resources and impetus to establish robust data governance practices. However, the challenge arises when organizations approach this from a top-down perspective. They impose policies, practices, and rules without considering the unique needs and complexities of individual data domains.

A more effective approach would be to adopt a bottom[1]up methodology. While a top-down framework and guiding principles can provide overarching direction, the true success lies in working from the bottom up, addressing specific data domains first. This framework ensures that the most critical data areas are prioritized and tackled individually rather than attempting to enforce data governance for all systems simultaneously, which may prove overly academic and less practical in recognizing the true value of governance and quality implementation.

In addition, a successful data governance and quality initiative relies on a partnership between IT and the business. The collaboration of both entities is crucial in ensuring that the established controls, processes, and ownership are effective and aligned with the organization’s objectives.

“A successful data governance and quality initiative relies on a partnership between IT and the business”

This strategy involves committing to stringent data governance and quality measures for core data. It is essential to assess the ROI at each stage, determining whether it justifies extensive processing and monitoring efforts for low-impact data sets or domains. By rigorously controlling and enhancing key data domains that feed business intelligence, reporting, and  AI requirements, we can effectively address data governance and quality at our organization.

WOULD YOU LIKE TO TALK ABOUT ANY SUCCESSFUL PROJECT INITIATIVE THAT YOU WERE A PART OF?

One significant endeavor involves addressing key data domains with precision, prioritizing data cleanliness, and bridging existing gaps in our data. For instance, we are focused on enabling companies to compare their performance against competitors in various sectors, such as pharmaceuticals or consumer packaged goods.

To accomplish this, we are embarking on a commitment to master the product data of both our offerings and those of our competitors. This involves linking competitor product data to our master product data, a crucial step to ensure accurate and comprehensive analysis. Leveraging predictive modeling and generative AI, we aim to identify opportunities to enhance our targeted products relative to competitors.

We diligently manage and steward the interconnected data by enabling the AI engine to generate meaningful insights to establish robust data management practices. We aim to empower our sales teams with invaluable insights by coupling AI initiatives with rigorous data management practices.

HOW DO YOU ENVISION THE FUTURE OF DATA ANALYTICS TRANSFORMATIONS A COUPLE OF YEARS DOWN THE LINE?

The perpetual question we face is how to empower end users to harness the potential of data without constant IT intervention. Over the years, we have witnessed the evolution from a centralized model, where all development was IT-driven, to the advent of self-service BI tools like PowerBI, Tableau, and Qlik, enabling end users to build their own insights. However, this newfound freedom often resulted in the creation of unscalable, poorly-architected solutions akin to Excel on steroids.

Our focus now lies in accelerating the process of empowering business users while ensuring data quality and structural efficiency. Conventionally, descriptive analytics emphasizes visualizations of data, but the real value lies in shifting towards prescriptive analytics, where insights are surfaced automatically, prompting immediate action. This transformation demands a holistic approach that combines technology, data structures, and AI to enable meaningful insights to emerge effortlessly.

We aspire to cultivate a prescriptive culture where AI assists efficiently identifies critical patterns and presents actionable insights. This helps in elevating human attention to more valuable tasks by automating data preparation and analysis.

When clients request dashboards, reports, or scorecards, we prompt them to envision the intended business actions behind these requests. Understanding what actions they would take based on the provided insights is vital to making data-driven decisions. This transition towards augmented intelligence, where humans and AI collaborate to generate actionable insights, holds the key to unlocking the true potential of data and fostering a culture of proactive decision-making.

FROM YOUR EXPERIENCE, WHAT PIECE OF ADVICE WOULD YOU LIKE TO SHARE WITH YOUR PEERS DEVELOPING DATA ANALYTICS PROJECTS?

When considering data analytics, it becomes evident that they encompass more than just individual AI and BI initiatives or data management initiatives. These projects necessitate a holistic approach, addressing various components like data acquisition, management, and definition, alongside processing and monitoring procedures. Rather than merely fulfilling specific output requests, the focus should be on empowering self-service capabilities for business users. The assets developed in such projects should always center around driving actionable outcomes, seeking opportunities for process improvement, enhanced metrics, and services.

Business users should have the ability to create and utilize assets independently, guided by appropriate training and support. The true value added lies in synergizing different technology stacks and working cohesively to identify and act upon strategic call-to-action opportunities.