In a world where every company is now a technology company, all enterprises must become well-versed in managing their digital products to remain competitive. In other words, they need a robust digital product lifecycle management (PLM) strategy. PLM delivers value by standardizing product-related processes, from ideation to product development to go-to-market to enhancements and maintenance. This ensures a modern customer experience. The key foundation of a strong PLM strategy is healthy and orderly product data, but data management is where enterprises struggle the most. To take advantage of new technologies such as AI for product innovation, it is crucial that enterprises have well-organized and managed data assets.
Gartner has estimated that 80{aa99edd08dd66036bd4af6c3dbe8af9b4ab31fcc55a521c2956d094a6dcd3a48} of organizations fail to scale digital businesses because of outdated governance processes. Data is an asset, but to provide value, it must be organized, standardized and governed. Enterprises must invest in data governance upfront, as it is challenging, time-consuming and computationally expensive to remedy vast amounts of unorganized and disparate data assets. In addition to providing data security, governance programs must focus on organizing data, identifying non-compliance and preventing data leaks or losses.
In product-centric organizations, a lack of governance can lead to exacerbated downstream effects in two key scenarios:
1. Acquisitions and mergers
Consider this fictional example: A company that sells three-wheeled cars has created a robust data model where it is easy to get to any piece of data and the format is understood across the business. This company is so successful that it acquired another company that also makes three-wheeled cars. The new company’s data model is completely different from the original company. Companies commonly ignore this issue and allow the two models to operate separately. Eventually, the enterprise will have weaved a web of misaligned data requiring manual remediation.
2. Siloed business units
Now, imagine a company where the order management team owns order data and the sales team owns sales data. In addition, there is a downstream team that owns product transactional data. When each business unit or product team manages their own data, product data can overlap with the other unit’s data causing several issues, such as duplication, manual remediation, inconsistent pricing, unnecessary data storage and an inability to use data insights. It becomes increasingly difficult to get information in a timely fashion and inaccuracies are bound to occur. Siloed business units hamper the leadership’s ability to make data-driven decisions. In a well-run enterprise, each team would connect their data across systems to enable unified product management and data-informed business strategy.
How to thrive in today’s digital landscape
In order to thrive in today’s data-driven landscape, organizations must proactively implement PLM processes, embrace a unified data approach and fortify their data governance structures. These strategic initiatives not only mitigate risks but also serve as catalysts for unleashing the full potential of AI technologies. By prioritizing these solutions, organizations can equip themselves to harness data as the fuel for innovation and competitive advantage. In essence, PLM processes, a unified data approach and robust data governance emerge as the cornerstone of a forward-thinking strategy, empowering organizations to navigate the complexities of the AI-driven world with confidence and success.
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