2. Ensuring data management and governance across the ecosystem
Data landscapes are only as good as the management and governance of the data itself, which can vary significantly from system to system. At the same time, they must take a proactive approach to ensure data and AI are used ethically and responsibly to avoid negative consequences and violating customers’ trust. It takes enormous effort and expertise to manage and govern all these different landscapes consistently to 1) ensure data quality and veracity (so users trust it) and 2) provide a simple, intuitive access experience for those who need to use the data (or insights derived from it), regardless of where it sits. Because most companies don’t have the resources or skills to do this, data quality and the access experience can vary substantially by system and data set—ultimately hindering efforts to unlock data’s value.
Data sharing is an important KPI and a business necessity. It accelerates digital business transformation and more than ever, leaders are responsible for ensuring data quality, creating security measures that anticipate threats and building ethically responsible frameworks for managing data and AI. This establishes a virtuous cycle of data creation and consumption, because quality is always improving. Data practitioners don’t rely on unverified or biased data to make decisions and instead take steps such as using AI itself to detect biased algorithms.
With data security, controls and governance processes in place, the organization can delineate who can access which data (or data-related products) and how they can use it. There is a delicate balance between protecting critical data from misuse or being exposed to those who aren’t authorized to see or use it, with making it useful to those within the organization to improve the business. Management and governance rigor is needed to ensure that all data is consistently of the highest quality across all systems and platforms, and that data is fully traceable from when it’s acquired to when and how it’s used. This is critical to building user trust in the data and the insights generated from it. A comprehensive data rights management solution enables data creators or producers to retain control of their data products and Consumers to have a seamless experience in accessing data products.
3. Focusing on the people and experience
Creating a positive user experience is critical for adoption of your data and AI strategy. Each part of the organization may be looking at the same data but through a different lens.
Producers of data products go to multiple data management systems to define and manage their products. With each function offering a potentially different perspective, you get different offshoots of data products which lack consistency and can cause confusion when reporting.
Having the right tools that enable data to be consumed and used effectively and in a way that’s meaningful and relevant to individual users is key. This could be a data marketplace, a sandbox workbench, enterprise data search functionality, business intelligence reporting, and data visualization platforms. For example, a data marketplace or data platform enables reusability of data products, increasing efficiency and value of data projects. This is key to creating a superior user experience that encourages people to access an apply data in ways that support their role. It also evangelizes the use of data and insights for decision-making across the enterprise.
Improving the user experience is also about efficiency – having simpler and more intuitive processes to build, manage, consume and use data reduces the time to perform data-related activities, reducing the time to insights.