In today’s digital economy, innovation is fueled by the insights company leaders have concerning their own organizations. To effectively run a business, you need good information and data. To innovate, you need a way to play out scenarios and strategies. The success of these scenarios relies on a high degree of trust in the underlying information.
In many cases, your opportunity for innovation doesn’t arise until you analyze and interrogate your data and the patterns that emerge; these will point the way to innovation.
The organizations should first understand that data is a critical asset and that its governance is a foundational element of transformation objectives like predicative analytics, process optimization, and digitization. Then, they should begin the journey of organizing their data governance programs.
Some initial questions to ask might include:
Many of those questions focus on the “people” part of the equation. And people are naturally the most important aspect. Creating the following three levels of ownership will not only answer those people-related questions, but it will also set up your business to use data successfully and optimally:
1. Business ownership: This level requires a management-level business data owner and an operational data owner beneath the process leadership. Further, it needs strategic support for data management initiatives to ensure alignment with the vision, goals, and objectives. It also must have the ability to make organizational, portfolio, and funding decisions while influencing projects.
2. Operational data governance: This level should actively own and manage the data management program and its people. It needs a data subject-matter expert and a data governance program leader.
3. Data management support: The third level should manage the tactical activities and support requirements for data and related processes. It should include information technology support, business support, and a “center of excellence.”
With ownership clearly defined, a data governance team works to optimize both data processes and business processes. That effort helps executives better assess the effectiveness of business operations to improve the quality of a product, reduce the complexity of a system, ensure compliance, decrease cycle times, and deliver key metrics.;
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