Data vs AI Strategies: Why Companies Need Both

The following post was adapted from one I wrote on behalf of Elad Data

The recent commercialization of large language models (LLMs) such as ChatGPT and Bard underscores the power of harnessing vast amounts of data. By tapping into the vast terabytes of information from the digital realm or within enterprises, these models have proven remarkably adept at offering unprecedented capabilities that are revolutionizing business practices and increasingly influencing various facets of our daily lives.

Whilst the rapid pace of development of these applications has been almost mind-boggling, it is worth noting that progress in this space is dependent on the quality of the underlying data. The integrity and caliber of data are pivotal in ensuring model precision, consistency, and impartiality.

Achieving this is possible only in an environment in which a robust data strategy is in place. Such a strategy would not only involve ensuring data quality and integrity, but would also provide a way forward for planning the curation and management of the vast datasets being utilized. ChatGPT, for example, utilizes many different models, some are better at conversational applications, while others are better within embedded environments. Each of these models typically require separate data environments and processing protocols.

Data governance and management policies are also important components of a data strategy, and provide a robust framework to regulate data access, usage and sharing - making sure that the data used with the models (LLM's or otherwise) complies with legal and ethical standards. In fact to highlight this, the possibility that applications such as Dall-E and ChatGPT have been caught using proprietary data without permission may be of concern.

Also intrinsic to a data strategy is data security and privacy. Prevention of models from inappropriately accessing and using personal or sensitive information, and protecting against unauthorized access of this data is a core data strategy undertaking.

As businesses evolve, the kinds of questions required to be answered by AI or ML models inevitably change. A dynamic data strategy ensures continuous data flow and feedback mechanisms that will support adaptation to these changing business needs.

Notably, given the substantial computational demands of certain models, a data strategy should also cover efficient data allocation methods, optimizing the training of these resource-intensive models.

Regular audits to check data and model biases, model interpretability and ethical usage is also a component of a comprehensive data strategy, as are periodic checks to ensure the models are performing in line with the organizational objectives.

An AI strategy on the other hand, is more about specifying the types of models required in the organization that would add value and assist to uplift its capabilities and improve its bottom line, and also how to go about achieving this.

An AI strategy will critically cover the deployment of the AI infrastructure required to support the delivery of these capabilities. This will likely involve the provision of higher end resources such as GPU's and advanced cloud computing services.

Model design and development, which involves selecting the correct algorithms, training and validating models and deploying pipelines, are core to delivering on an AI strategy. Critically, this involves cultivating a team that not only knows how to build models, but who are also adept at integrating them into the business side of things. All these would be contained in a comprehensive AI strategy.

So, having just a data strategy or just an AI strategy in isolation isn't enough. A holistic approach that integrates both is essential. While the data strategy lays the groundwork, making data usable and accessible, the AI strategy allows organizations to realize the potential latent within the data. It facilitates the means to innovate and provide improved capabilities that will propel them forward.

Companies are increasingly realizing that a sophisticated AI strategy is untenable without a robust data foundation. So too, for those organizations which have already embraced a data strategy, understanding how, when and where they can optimally deploy AI and machine learning to uplift their businesses is critically through the creation of a comprehensive AI strategy.

If you would like to improve your understanding of how to get started on either a data or AI strategy, or you have another machine learning challenge, please feel free to reach out to continue the conversation.