A quick guide to AI for small/medium manufacturers

As stated in my previous post using advanced, sophisticated techniques to build AI or machine learning systems are expensive. Straightforward and simple statistical methods will often work just as well when building on small to medium enterprises at a fraction of the cost incurred by major enterprises.  The AI system is only sustainable in the long term if a company has high-quality data, and the right way to classify and access it.
In many cases, data flows to a company in inconsistent formats. A data warehouse may contain event-type data stored on a large distributed system or individual applications - each having unique data storage formats where only the experts of each system know how to retrieve the data.

How to Manage Data Well
The first step to doing it right is acknowledging that the data that drives a business is fundamentally one cohesive set of information instead of disconnected pieces or systems, then representing that data in an accessible format.
Enterprises rarely look at data management issues with the intent to solve them until the issues become too difficult to manage. In truth, defining data formats and organization at the onset is a strategic move because the costs of doing it wrong are huge.

The Role of the Chief Data Officer
Each arm of the organization can define the data well, but the data may not flow smoothly across the organization. Having a chief data officer (CDO) whose single most important mandate is to understand and manage the data ensures that that data has value and accomplishes its role in the business. This should also spare the company from having to implement expensive fixes to an improperly set up data architecture.

Dealing with inconsistent systems is a necessary evil. A CDO may be bound to some of the choices that were made in the past. It won’t be realistic to replace the master data management systems that govern fixed data points that drive the core of an enterprise. But there are still flexible elements within the data strategy that the CDO can focus on, such as the data warehouse, the way that applications acquire, consume and produce data without being reformatted, repurposed and re-understood.
Companies looking to hire CDOs should look for data scientists with experience from an engineering organization as these professionals shall have gained data management skills as they build product test solutions. They are also accustomed to solving increasingly large problems through proven agile methodologies. Statistical, mathematical, analytical skills are definitely important, but data management is fundamentally an engineering problem, and engineering is where many critical CDO skills will come from.  The CDO should be the highest cost of any AI project. The chief information officer (CIO) will be responsible for the systems. There will be other stakeholders, perhaps subject matter experts, marketing or finance. But in the real world these people don’t come together until the problem has grown out of proportion and that most companies are likely to only hire a CDO when the pressure of working with poorly organized data makes it necessary;  Never the time to hire or a good idea.  The CDO needs to be a permanent, long term position.  
 

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