Intelligent CIO APAC Issue 18 - Page 76

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from your desired outcome to figure out what ingredients it requires .
For this the project goals should be articulated with crystal clarity . It is not enough to say ‘ AI will help us streamline our production plant ’. That needs to be broken down into greater detail , with a focus on specific process elements .
Establish an effective data policy
Organizations can ’ t assume that because they collect data already , that they can just pass it right over to the AI / ML developer and it ’ ll drop neatly into new applications , out the other end of which will come amazingly informative dashboards of new information – if only it was that simple .
Now it is possible to see what data is required to achieve each goal . And this can ’ t be generalised . It is important to list all the data elements required . These might not all exist before the project begins , working out what they are and how they will be collected , is vital .
This is often much trickier than it sounds , and external data scientists and data engineers , with experience of working in AI / ML development , will bring an ability to ask the right questions , look round corners at problems , keep a lid on function creep and make sure the most difficult questions are addressed rather than parked .
Bringing them in early can mean an organization doesn ’ t find itself having to do this work at a later stage when it can add expense and time to a project or worse – contribute to its failure .
By the end of the ‘ working backwards ’ process , an organization should know what it needs to progress with confidence having worked through what they need to know , what knowledge of these needs that it already has and what it needs to obtain this know-how .
The way data is collected has changed in recent years in part due to the European Union ’ s General Data Protection Regulation ( GDPR ), and the extensive updates of the Australian Privacy Act .
Therefore , this year ’ s data set isn ’ t compatible or comparable with that from four years ago from a GDPR perspective , and compliance with the Australian Privacy Act may require more from your organization than what has previously been in place .
Further , with the draft of the Online Privacy Bill set to be tabled in Federal Parliament in the coming months , data privacy requirements are only set to increase . Perhaps significant amounts of historical data – even recent historical data – are missing .
Perhaps the organization needs to put in place entirely new data collection policies to start from a designated ‘ Day One ’.
Working out a ‘ Day One ’ data policy is one thing , however , to hit the ground running with an AI / ML project as soon as it kicks in , some historical data will
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