Digital Energy: 2010-11 Distinguished Lecturer Jim Crompton - Putting the Focus on Data
Data is a critical business asset which drives decisions on where to invest, when to divest and how to operate more efficiently. The business wants to focus on using data and expects IT to manage data. However, IT often focuses on the technology to capture, store data and even to visualize data but the ownership of the lifecycle of data is often ill defined.
This is not a new problem. Many studies point out the productivity loss felt when a lot of time is spent looking for data and making sure what is found is correct, complete and in the right format. While some of the issues are mitigated by our current experienced workforce, what will happen when the experience leaves? The new workforce is digitally literate with high expectations but can they perform at a high level with a poor understanding of data? Can the new engineer recognize when critical data is missing or wrong? Will they be able to recognize when a logical conclusion is not the right one?
The importance of data should be a business priority. Managing data in silos (structured data, documents, transactions, models) increases the resistance to efficient data flow. As many different people are involved in new workflows, a solution design for one specific discipline becomes a barrier for others. In some applications, the need to see the bigger picture becomes more important than reaching specific answers.
Data silos continue to survive waves of new technology development. The data explosion and the availability of powerful desktop tools create hundreds more data silos for the organization to manage. There is not a simple answer or technology to fix the current situation but there are practical recommendations to help us get back on the road to “’trusted data, easily found.”
The approach starts through a better understanding of business process and how information flow leads to decisions. Other practical steps include: data governance, common reference and master data, data quality management, and looking at the data lifecycle by managing data from its capture/creation, storage, access, use, archive and disposal.
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