Big Data and Procurement – The amount of data is exploding – approx. 90% of the data that exists in the world today was created in the last two years alone. Procurement systems capture a vast amount of data, including sourcing information, weather reports, manufacturing and delivery data, supplier data, purchasing data, catalogue data, and more.
How can procurement leverage the insights Big Data can provide, integrate predictive analytics in the daily work and provide prescriptive guidance to buyers in the future?
There are undoubtfully very valuable insights within that data, but the challenge for procurement is to understand and use the data in order to make better, informed decisions. Big data, predictive analytics and prescriptive guidance can further scale with cognitive computing power to provide better information to enhance situational awareness and speed-to-decision, ultimately driving superior procurement performance. The resulting business value will improve the way. procurement roles and processes are executed, making both more effective and valuable.
Most procurement organizations face three core challenges by working with Big Data:
- Digitizing processes
The first challenge for Procurement is to access unstructured data that resides in scanned documents, email inboxes and spreadsheets. A key to unlocking the data’s potential is to ensure it is stored in a digital format that can be analyzed.
- Driving insights from data
The second challenge is to analyze and utilize the digitized data. There are human limitations (skills, time), as well as technical challenges to find patterns, process the information intelligently and accurately, and derive insights. Clients are challenged to figure out how to tap into cognitive capabilities and build them into their processes.
- Enabling talent and skills
The third challenge involves a change in culture and mindset that is needed to embrace these disruptive technologies. Big data, predictive analytics and cognitive computing require new skills and new ways of working, such as self-service analytics.
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