The excitement around generative AI (gen AI) and its massive potential value has energized organizations to rethink their approaches to business itself. Organizations are looking to seize a range of opportunities, from creating new medicines to enabling intelligent agents that run entire processes to increasing productivity for all workers. A raft of new risks and considerations, of course, go hand in hand with these developments. At the center of it all is data. Without access to good and relevant data, this new world of possibilities and value will remain out of reach.
Building on our interactive “The data-driven enterprise of 2025,” this article is intended to help executives think through seven essential priorities that reflect the most important shifts that are occurring, what the main complexities are, and where leaders can focus their energy to realize the data-driven enterprise of 2030.
Spotlight
MakerVerse, a marketplace for industrial parts, has integrated advanced data models into its value chain. When customers submit computer-aided-design (CAD) drawings of and requirements for parts, for example, algorithms analyze historical data models to automatically provide estimated supplier costs, contractual pricing, and delivery timing. After a customer has completed the purchase, models analyze data about suppliers (including costs, performance history, and capabilities in fulfilling specific kinds of orders) to select the best options, then automatically send and confirm proposals to make and deliver the part. Systems that are tied to the data sources at the supplier allow MakerVerse to automatically track supplier progress (and populate their databases with new data) and escalate any issues to account managers.
Spotlight
An automotive company wanted to create capabilities to offer a range of personalized services and communications with its customers. To meet this need, it decided to develop two capability pathways.
The first one was an AI and machine learning capability pathway to perform deep analysis and segmentation of the company’s customers. To build this pathway, the company pulled together a number of elements, including a PySpark machine learning library (for clustering and propensity analysis), Databricks for file storage, and Futurescope for model management using MLflow. The other capability pathway was for personalized communication made up of LLMs, a sales data warehouse, marketing technologies to send and track email performance, and a customer-360 data set and external data from Experian for customer interests and demographics, among other technical elements.
With these capability pathways, the company was able to segment customers into highly refined archetypes, send them personal offers, provide personalized prompts to service operations to follow up with customers, and deliver personalized behavioral information for sales staff.
Spotlight
Skyflow offers a platform called the Skyflow Data Privacy Vault, designed to help companies manage, protect, and use sensitive data while ensuring compliance and privacy. It acts as a secure central hub for sensitive data, isolating it from other systems and encrypting it with advanced techniques. Despite strong security, Skyflow’s secure APIs still allow users to use this data for workflows, sharing, or analysis—all without ever decrypting the original information.
(This spotlight comes from “McKinsey Technology Trends Outlook 2024.”)
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As technology permeates businesses and society, the importance of data will continue to increase—as will the accompanying challenges. The levels of uncertainty and the rapidly changing dynamics of technology mean that there are few clear answers today. But by sticking to the most important priorities and understanding the essence of the issues facing them, data leaders can navigate a path to a data-driven enterprise.
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