The ever-increasing volume of information in the workplace comes from both the creation, receipt and aggregation of information in the organisation. Knowing what it is and where to find it is a crucial step in managing information. Users can easily access the information through a federated search, which can be enhanced with the use of filters to achieve better search results.

The benefit of automating the capture of information for users by using artificial intelligence (AI) is proven in many businesses that are applying these advanced tools. AI combined with machine learning is able to recognise content types and user patterns to add appropriate metadata tags which enables the delivery of the relevant information to users, Easy to Find.

Some of the biggest challenges in achieving metadata-driven information management are:

• developing with users the right type and number of metadata descriptors and deciding on standard terminology for the metadata values, basically developing the ontology and business taxonomy for the business. There are some standards and models available BUT the metadata must make sense for the business specific context.
• Helping users move away from (sub)folder structure is the challenge. Relying on auto-populating metadata tags when they upload documents can make the capture of information in SharePoint, MS Teams and OneDrive less onerous, it will make saving information easy, saving them time.
• Educating users in how to find information in any of MS document management applications and how to use filters when searching for information from the document management system is pivotal: Change Management is a must do investment. Demonstrating before and after auto-populating metadata tags with the information they save is an effective way to illustrate to the user of the effort and time saved by the implementation of such intelligent tool.

Automating the retention and disposal process: life-cycle-management

Another vital tool to improve compliance and efficient management of information and data storage is a smart records management tool. The use of auto-classification to add metadata tags to information about their retention will assist and support an automated life-cycle-management.

The automation focusses on the creation of consignments of information for retention and disposal (RnD): archiving or purging. The human aspect in such an automated RnD process will remain in the electronic approval process, whereby the business is able to advise longer retention of the information than the legal requirement, the business purpose.

Conclusion

Implementing AI with Machine Learning tool to improve the capture of information created, received and aggregated will give the user that positive experience. This is the outcome you want to achieve as one of the Returns on Investment. Other outcomes are improved compliance and management of storage by implementing a smart records management tool.

By making the information findable and demonstrating what is possible, users will begin gathering confidence. Saving time and taking away frustration are big gains.

Arriving at such result requires the support from effective Change Management. The emphasis is on training staff and making them not only aware but also competent users of information. That is pivotal for an overall satisfactory success.

Easy to save, easy to find, easy to manage and complying with the NZ Public Records Act 2005 is the ultimate goal for every organisation in local and central government.

Gerard Rooijakkers is Corporate Information Manager at Auckland Transport. Originally published HERE