The Clear Sign Your Enterprise Is Serious about AI
The Clear Sign Your Enterprise Is Serious about AI
Data considerations become paramount for enterprise AI
Creating order from disaggregated data is a foundational element of successful AI implementations. Image created on Midjourney.
Many enterprises talk about implementing AI, but in reality, they are just experimenting with individual accounts or their favorite app’s new LLM integration. How do you know if your enterprise is one of the pretenders or a contender?
Enterprises serious about private AI implementations that will do more than boost individual productivity all have one common element: Data.
Data is the fuel of AI, training it, feeding it, providing the answers to prompts, and resolving user queries and needs. Any significant enterprise AI implementation beyond individual productivity apps needs contextual data to fuel its algorithm and training to refine how the algorithm interprets that data.
Poorly maintained data produces less than optimal results in the best of cases. In some worst-case scenarios, sloppy data practices can derail an AI project before it ever launches, create significant security risks, or offend customers. AI problems are often data problems… Garbage in, garbage out.
That’s why enterprises that want to deploy impactful applications that will serve their larger enterprise and stakeholders have already or are actively addressing data needs to fuel their AI models. For most enterprises, looking deeply at their data practices is a painful reminder of years, even decades of neglect.
Data hygiene was never a critical issue until the recent AI boom. But AI turns this weakness into a liability, causing companies and organizations to face the music and finally address their challenges. Data is a foundational element for successful private AI implementations.
Data Considerations
The most recent No Brainer podcast featured Courtney Baker, CMO of Knownwell, a company that uses professional services companies' data to turn it into insights to strengthen client relationships and prevent churn.
When companies and organizations get serious about AI, they build a use-case focus that includes a model, the necessary algorithm(s), and details of the data needed to train and deploy the application. There are several steps required to achieve robust data governance, including:
Inventory: This is often considered a pre-step, but in reality, most teams in the enterprise have an idea of where their data is but don’t know the fine details. Inventorying where the data is, what format it exists in, existing data architectures for each data set, and the quality of those data sets is
Cleaning: Traditionally the most complex and laborious part of an implementation, cleaning data meets enterprise standards for uniformity and quality. Today, more data-cleaning AI tools are being deployed to help developers and their enterprises clean data.
Architecture: The stuff of larger enterprises, more and more small and medium enterprises are implementing data storage architectures, from lakes and houses to hubs and lakehouses. Regardless of the architecture format, the goal is to have the ability to understand, control, and facilitate the transfer of clean data on behalf of the enterprise.
Security: Controls are used to protect enterprise data; in turn, that governance policy informs how the model is built. This includes role-based access controls to protect data and AI models from unintentional employee use, errors, and bad actors.
Guidance: This section details how employees use enterprise data safely, including quality standards for new first, second, and third-party data, when and when not to use that data, and guidance for all enterprise employees on how to safely use company data and intellectual property.
While a simplified list of requirements, the above should give most business managers a healthy appreciation for the years of work required to implement a mature data organization. If you are serious about AI, you are at least beginning to weigh what it takes to prepare your data for consumption by advanced algorithms.
While the end goal may seem impossible, take heart. Addressing training data begins with one AI project and an eye toward the future. Work with your peers in the larger organization to facilitate alliances and create a larger strategy to help guide you. Data maturity requires alignment between business units, technology teams, and legal and policy operations.
