Why conventional BI fails manufacturing enterprises but curation succeeds

Back here I was describing what the terms democratisation, syndication and curation mean in the Enterprise 2.0 environment.

Of these, curation is particularly important to the process industries and perhaps to manufacturing as a whole. And here's why.

The data generated in a manufacturing environment can be thought of as broadly falling into the following categories; documents, ERP data and manufacturing data.

Whilst tempting to exclude documents from any BI discussion it's false to do so; whether in Lotus Notes, SQL or elsewhere, this is where day-to-day manufacturing decisions, events and instructions are stored. They represent a key data source for understanding trends yet are often ignored by BI solutions.

ERP data is typically proprietary and stored deep in an inaccessible database designed with system and process integrity rather than data reuse in mind, to be accessed only by a vendor specific MIS client.

Manufacturing systems data is generally generated with very little metadata by proprietary systems that are designed around a single purpose, e.g., to log data in real-time.

As any business intelligence vendor will tell you, the value of collecting such data is in the analysis of trends; identifying series of points that demand action. Yet the value of such analysis is exponentially increased by deriving relationships between trends, e.g., an interesting manufacturing trend may become a critical decision point when placed against an ERP trend. Causal relationships are what drive effective decisions - decisions which may require considering ERP data alongside manufacturing data alongside operational documentation.

This is precisely where conventional BI fails in the manufacturing environment; it's usually vendor aligned and incapable of dealing with proprietary data from multiple sources.

It's also difficult for end users to get to grips with, which means the enterprise can't leverage the expertise within the wider user base; conventional BI relies on users to be experts in the construction of queries, when their expertise is the construction of manufacturing processes.

It's end users, expert on the business process but inexpert on BI tools, who will spot these relationships and must be empowered to act.

This is curation; without meaningful metadata to make connections algorithmically, expert human filtering and nomination is the only way a community of users can be notified of a relevant trend. This is the real data that needs user collaboration, selected by a user that appreciates the nuances of the community's shared interests.

These users must have easy access to data from multiple proprietary sources; a level playing field that promotes mash-ups and comparisons. End users must be able to identify their own causal relationships and share their findings immediately with the wider community, driving quick decisions and developing knowledge that is in turn utilised in the future. There can be no reliance on IT to enable this process - it has to be in the hands of the end-users so they can act quickly.

In this way, data can be socialised; business intelligence can become social business intelligence; communities can benefit from shared expertise, expertly applied to their data.