How can AI help you turn your data into a competitive advantage?

Much has been said about AI disruption and how it can enhance productivity. But how can AI improve marketing operations, and why is it crucial to establish strong data governance and implement the right data workflows to maximize AI's potential?

1. Data Accessibility

As businesses become more digitalized, most of their sales and advertising activities already take place on various service platforms. However, marketers still face two major challenges:

• Data is fragmented across multiple platforms.

• Significant amount of data sources, such as offline ad spend, audience metrics, or retail sales could be hosted in legacy systems that are not open to perform system integrations.

Traditional retail sales data often resides in disparate systems, sometimes lacking proper organization and accessibility for multiple internal stakeholders.

How can AI and marketing analytics platforms help? These platforms offer pre-integrated data sources that update in real-time, allowing businesses to consolidate all key data into a single source. This enables more comprehensive analysis and reporting, providing a unified view of marketing activities.

But what about offline data? This is where robotic process automation (RPA) comes into play. RPA automates repetitive tasks by configuring bots to retrieve offline data from legacy systems regularly. This ensures continuous data accessibility without relying on lengthy process to manually extract data or update reports. Automation minimizes tedious tasks, allowing marketers to focus on analyzing data as opposed to getting the data in the first place.

2. Data-Driven Decision-Making

Once data is aggregated and reported seamlessly, the next step is to establish a robust measurement framework. Different measurement methodologies can yield vastly different insights, so striking a balance between statistical accuracy and business logic is essential. Refining and improving predictive models over time requires continuous iteration and business involvement.

How can AI support this process? This is where machine learning excels. AI-driven models can analyze millions of data combinations to uncover the most accurate representation of business performance. With AI handling complex computations, marketers can focus on selecting the best-fitting insights based on their business understanding, shaping the business narrative behind their models. Additionally, AI democratizes access to advanced analytical techniques, enabling businesses to leverage sophisticated models without requiring in-house data science expertise. This makes AI-driven measurement methodologies more accessible to marketers who might otherwise lack the resources for such capabilities.

3. Speed of Decision-Making

One of the biggest transformations AI brings to advertising measurement is the acceleration of decision-making. With an established measurement framework, marketers can make data-driven decisions continuously, based on real-time insights.

Previously, marketing mix modeling (MMM) took up to three months to produce, often making its findings outdated by the time they were published. As a result, many marketers hesitated to invest in MMM despite its valuable insights. However, modern AI-powered measurement platforms have revolutionized the process, enabling seamless and continuous updates. Now, MMM reports and forecasts can incorporate the latest data in real-time, allowing marketers to make timely, informed decisions. AI-driven analytics have reduced the time required to build these models by 95%, enabling setup in just a few days.

4. Data Analysis at Scale

Another major disruption AI brings to marketing analytics is its ability to perform large-scale, granular analysis at a fraction of the historical cost and effort. In the past, marketers often had to compromise by relying on aggregated data due to the expense and time required to develop multiple models. This approach risked overlooking critical granular insights, such as specific product performance or regional variations in marketing impact.

AI eliminates these limitations, enabling businesses to take on ambitious projects that were once impossible while increasing production capacity by 100 times. This expanded capacity allows for deeper analysis of key performance indicators (KPIs), including full-funnel metrics across multiple product categories, revealing both direct and indirect relationships between advertising efforts. As a result, marketers can make more precise optimizations, enhancing effectiveness and maximizing ROI.

5. Democratization of Data

Perhaps the most exciting capability AI unlocks is the democratization of data. As data continuously flows through analytics workflows, AI-driven conversational interfaces, such as customized agents, empower stakeholders across the organization to interact with data in natural language.

These AI-powered assistants are fueled by business-specific data and models, allowing decision-makers from different departments to access sophisticated analytics without requiring technical expertise. This removes barriers to data usage and ensures that insights are easily accessible across teams, fostering a data-driven culture within the organization. As a result, businesses become more agile, competitive, and smarter in their decision-making processes.

Conclusion

In the AI age, having a strong data strategy and governance framework is no longer optional—it’s a necessity. From enhancing data accessibility to enabling real-time decision-making and democratizing analytics, AI-powered solutions empower businesses to unlock the full potential of their data. By implementing the right data workflows and governance structures, organizations can maximize AI’s impact, driving smarter marketing operations and sustained business growth.

Paula Fernandez

Founder