Your organization is charged up for AI – but is your data ready?
Most business leaders are fully convinced of the value AI technology will bring to business and are all geared up to dive in head first – but when it comes to the realities of deployment beyond pilot and proof-of-concepts, that enthusiasm can quickly fizzle out.
The truth is that while many companies may consider themselves ‘AI-ready’, they are let down when it comes to the quality of data they’re hoping the AI will draw from.
On average, just 3 percent of an organization’s data meets the standards required for accurate AI analytics. Data is poorly managed – it either gets chucked into obscure places, never to be seen again or is aplenty, but inconsistent and unusable.
Building a complete an AI data set is not easy. It’s time-consuming and costly, as it can’t exactly be bought off the shelf. Each organization has unique use cases and demands that call for bespoke data inputs. Unfortunately, there is no standard, one-size-fits-all framework.
How then, can organizations go about achieving an AI-ready data set?
First, know where the data is. In many organizations, data is accumulated over many years. Because this data is not managed well, it is easy to lose track of the location, who owns it, and where they came from. This is further complicated if the organization has obsolete, siloed systems.
With such poorly aggregated data, those responsible to implement AI solutions will have limited access to relevant information. These data need to be cataloged in a data center of sorts, which will act as a ‘home’ for data, easily available to those doing AI work.
Second, gauge the data’s fit. Organizations ought to conduct case-specific assessments about the quantity and quality of available data, and determine if the data is fit for purpose.
Develop a reusable data model to identify specific fields and tables required to inform the model, and based on this, systematically assess the quality and completeness of data. Working systematically is key, as it will accurately reflect gaps and potential opportunities for improvement.
Thirdly, ensure good governance. It is important that a solid governance framework exists, especially for security. Organizations must be proactive in securing and information assets, keeping them in a complaint environment or accredited containers.
Data that is fed into AI systems is extremely sensitive, and negligence would possibly lead to contravening a law somewhere, or a fine (in some markets a jail sentence). Consumer trust has to be earned, and keeping data safe is definitely a good way of doing so.
AI will do wonders for a company, improving both top and bottom lines. If done correctly, the efforts pumped into AI initiatives will yield a high ROI. Get your data game right, and half the battle would already be won.
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