Product Lifecycle Management for Data-Driven Organizations
In a world where every company is now a technology company, every company must become well-versed in digital product management to remain competitive. That means you need a strong digital product lifecycle management (PLM) strategy. PLM delivers value by standardizing product-related processes from ideation to product development, launch to market, improvement and maintenance. This ensures a modern customer experience. The core foundation of a strong PLM strategy is healthy, organized product data, but data management is where companies struggle most. To leverage new technologies such as AI for product innovation, it is important for companies to organize and manage their data assets well.
Gartner estimates that 80% of organizations are unable to scale their digital businesses due to outdated governance processes. Data is an asset, but it must be organized, standardized, and managed to deliver value. Addressing vast amounts of disorganized and disparate data assets is challenging, time-consuming, and computationally expensive, so enterprises must invest upfront in data governance. In addition to providing data security, governance programs should focus on organizing data, identifying non-compliance, and preventing data breaches or loss.
In product-centric organizations, a lack of governance can worsen downstream effects in two key scenarios:
1. Mergers and acquisitions
Consider the following hypothetical example. A company that sells three-wheeled vehicles has created a robust data model that makes all data easily accessible and understandable in its format across the business. The company was so successful that it acquired another company that made three-wheelers. The new company’s data model is completely different from the original company. Companies usually ignore this issue and allow the two models to operate separately. Ultimately, businesses will end up weaving a web of misaligned data that requires manual correction.
2. Siled business units
Now imagine a company where the order management team owns order data and the sales team owns sales data. There are also downstream teams that own product transaction data. When each business unit or product team manages its own data, product data can overlap with data from other departments, causing a number of problems, including duplication, manual modifications, inconsistent pricing, unnecessary data storage, and unavailability of data insights. Obtaining timely information is increasingly difficult, and inaccurate information is bound to occur. Siled business units hinder executives’ ability to make data-driven decisions. In well-run companies, teams connect data across systems to enable integrated product management and data-driven business strategies.
How to Succeed in Today’s Digital Environment
To succeed in today’s data-driven environment, organizations must actively implement PLM processes, embrace an integrated data approach, and strengthen their data governance structures. These strategic initiatives not only mitigate risk but also act as a catalyst to unlock the full potential of AI technology. By prioritizing these solutions, organizations can be better equipped to leverage data as fuel for innovation and competitive advantage. In essence, PLM processes, an integrated data approach, and strong data governance emerge as the cornerstones of a forward-thinking strategy, enabling organizations to navigate the complexities of an AI-driven world with confidence and success.
Learn how IBM can help you set up an effective data management solution.
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