Leveraging AI requires collaboration between humans and machines via data
One way to think of artificial intelligence is as a tool to have a conversation with your data. But structure is a prerequisite for all tools, including AI. In order to use AI to mine insights and action in real time, organizations must first organize their data into a consumable data layer.
However, according to a McKinsey report, 72% of organizations cite data management as one of the top challenges preventing them from scaling AI solutions across the enterprise. While many of those organizations have already begun the process of implementing AI, they're missing a consumable data layer. As a result, they aren’t seeing AI contribute to improved analytics or business outcomes.
One increasingly popular response to this challenge is to leverage AI and machine learning to structure data by automating and refining the QA process. These solutions are good, but they only get you so far. For all its power, AI can’t touch humans when it comes to understanding context, nuance and ambiguity. There are many things that AI just can’t do and likely will never be able to do. One of those tasks is making the leap from raw, unstructured data to a consumable data layer.
Consider media, which has been an early and enthusiastic adopter of AI. According to a Forrester report, 91% of U.S. advertising agencies are either currently using AI or exploring use cases, outpacing other groups including marketing organizations and the general business community. With thousands of digital advertising properties, each providing data in distinct formats, the task of consolidating and structuring this information is complex. Without rigorous processes for harmonizing and normalizing this data, discrepancies and inaccuracies will arise. This issue is further compounded when dealing with both paid and organic media, where data sets may overlap. Failure to employ effective deduplication methods can result in significant errors, costs and unreliable insights. Only a data-mature organization can push through this much complexity to create a consumable data layer.
Data-mature organizations exhibit several key traits:
Data-driven culture: The organization has a strong emphasis on making data-driven decisions and utilizes data across all departments and functions.
Clear data strategy: The organization has well-defined goals and missions related to data usage, which are communicated effectively throughout the entire ecosystem.
Strategic investments in data: There are specific data initiatives in place, with strategic investments in technology and processes to support these initiatives.
Robust data governance, security and privacy: A dedicated data governance team ensures strict adherence to training, processes and audits to comply with regulations and maintain data integrity.
Data quality measurements: Automated systems for continuous monitoring are in place to ensure ongoing data quality, with a focus on making sure all components work together seamlessly.
Pervasive use of analytics: Analytics are utilized throughout the entire organization to drive insights and inform decision-making.
The road to data maturity is long and it’s lined with human input, oversight, intervention and leadership. Too often, the AI conversation is framed as a contest between humans and machines. But in practice, leveraging AI requires collaboration between humans and machines. A consumable data layer is the venue for that collaboration. Without that layer, organizations will struggle to harness the power of AI. But with a consumable data layer, people can use AI tools to activate data and achieve business outcomes.
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