Why a data strategy is key as businesses implement AI

As Peak announces findings into AI adoption, AI Magazine takes a look at the importance of data strategy and unpicks why some still fail to implement one

The recent research by Peak, a decision intelligence pioneer, has shown that artificial intelligence (AI) adoption is picking up pace, but only a third of businesses recognise that a data strategy facilitates AI.

Peak’s new findings suggest that while the success of AI is reliant on the data strategy that surrounds it, only 35% of businesses with a data strategy sat its includes provisions for AI.

“AI is inherently a data technology and must function as part of an overarching data strategy, yet the majority of respondents in this survey are thinking about the two separately,” commented Richard Potter, co-founder and CEO of Peak.

Data strategies have become increasingly important as the amount of data businesses produce is increasing at an incredible rate. To collect, store and analyse this data sufficiently, a data strategy is essential.

This report also highlights that leveraging data in an effective way is a priority for many, with 52% of organisations now employing a chief data officer (CDO), and 82% have also invested in a data lake or warehouse.

Despite this uptake in data-focused initiatives, many fail to incorporate AI provisions within them. Instead, the strategies are focused on centralising data within the business, establishing measurable goals for the use of data and managing security risks and compliance.

Ensuring successful AI implementation with a comprehensive data strategy

The lack of AI consideration within data strategies could be a hurdle for businesses as they look to improve efficiency, drive growth and save costs with AI-enabled technologies.

“Our research also reveals that within many companies there is a lack of clarity around the overall AI strategy, even at the top levels of management,” said Potter. 

“If businesses want to successfully implement AI and, critically, drive value with this technology, we need open discussion within a business to ensure everyone is aligned to and understands the vision,” he adds.

When incorporating AI, a data strategy can enable the effective application of AI by providing a timeline, structure, and support to overcome challenges. 

Within the strategy, executives and CDOs can assess the relevance of AI to the organisation’s most important business outcomes, determine which types of application to leverage and address the organisational, governance, and technological challenges associated with AI.

To maximise the return on investment from AI, businesses need to look into their data culture, data quality and data privacy to ensure they can successfully scale AI within the organisation.

Everest Group also stress the importance of a strong data strategy with AI incorporated into it, as without it, once AI has been implemented, it could do more harm than good.

Nitish Mittal, Partner in the digital transformation practice at Everest Group, commented on this, he said: “I can’t stress this enough: data or the lack of the right data strategy is the number one bottleneck to scaling or doing anything with AI. When clients come to us with what they think is an AI problem, it is almost always a data problem. AI depends on viable data to prosper. That’s why it’s important to think about the data first.”


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