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Combining data analytics and AI

One will look at how executives can begin to utilize data analytics and AI to come up not only with the right solutions, but also the right questions.
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Combining data analytics and AI

Data analytics is a valuable commodity in today’s day and age. It is the most important resource organizations can hope to draw upon in a bid to quickly boost sales and confer significant competitive advantages.

Bigger companies are building larger data sets by the day in order to establish dominance in their fields. This importance of scale has come to mean that success in AI propagates itself.

To explain, the effectiveness of software will win it more users. This generates more data which improves the software, hence attracting even more consumers in a never-ending cycle. For the vast majority of companies, the majority of their expertise in AI and data analytics will be applied to their pre-existing data models. It is not many companies that care to build a business on monetising data.

However, every company must try to understand the strategic and business challenges that might be posed by these methods and tools. Staying on this technical frontline is quite the technical challenge, requiring large teams to talk meaningfully with each other.

In order to enact this role, teams have begun to invest in a new function or adviser – the “data executive” or “data product manager” whose role is to bridge the lack of knowledge between the data and AI camps of an organization. While it is hard at this juncture to say what skills, these individuals need necessarily possess, but what will definitely be needed is the ability to apply AI in conjunction with data analytics responsibly. It should act as a supplement to intuition rather than as a replacement to it. This will not only allow work to be allocated more efficiently, but also allow people to manage the ethical conundrums revolving around the use of data.

Be it in managing fossil fuel compilations as part of the energy mix, or in predictive policing in enforcement institutions, the field of data analytics is till rife with moral ambiguities that demand moral management. However, it has been learnt that algorithms are only as good as the data you train them on, and retain human biases present in the information. Hence, organizations should focus on training and enabling individuals who can take charge and enable better decision making.

By thinking ahead, these executives can not only figure out what you need to know, but also execute it in a responsible fashion, serving invaluable to the current market.

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