As artificial intelligence (AI) technology advances at a rattling pace, business leaders are anxious to identify practical use cases where they could enhance customer experience, improve efficiency and decision making.
At the core of successful AI deployment is data readiness. There are four critical areas to focus on.
- Data quality
Data is the foundation for mobilizing AI. When data are incomplete, inconsistent, and inaccurate, AI would not function well. Subsequently, it is difficult to convince users to adopt AI when they don’t trust the output.
For businesses that have multiple sources of data, they need to evaluate and identify the best source of truth. Moving forward, it is beneficial to standardize data entries to improve consistency.
For generative AI solutions, unbiased data are needed to train the models so skewed probabilities aren’t incorporated into the output. This means it is essential to be objective in selecting quality, diverse data sources.
- Data governance
With AI, clear policies on data ownership, security, privacy and compliance are paramount.
When deploying an application, user access rights specify who can view, input, change and manipulate data. These rights mainly address activities related to the lifecycle of data. Sensitive data are vulnerable if the business doesn’t have proper safeguards in place.
Adoption of generative AI applications adds another level of complexity to data security. The business needs to clearly define the boundaries for using public generative AI solutions. Everyone needs to understand his accountability and what he must avoid to comply.
- Data accessibility
Ease of data access is important for AI. Data residing in legacy systems are difficult to extract. Often, it is a challenge to get real-time data.
In addition, data stored in disconnected applications require additional steps to make them available. Timeliness affects performance.
To improve data accessibility, migrate all data to a centralized data warehouse or data lake. The easier it is for the AI technology to access comprehensive data, it enhances the capability to deliver more meaningful insights.
- Data management ecosystem
Vendor data management practices become part of the business’s data management ecosystem. Transparency regarding how they store, use, secure and observe privacy are essential to the business and its customers.
As the vendor is an important partner to AI deployment, the business needs to ensure there is alignment on values and ethics. Keep in mind that the business is not just purchasing a technology solution. Vendor compliance to data security and privacy will become the business’s standard.
Pose questions upfront and interview vendor’s customers to understand its practice and verify its track record.
In order to prepare data for AI deployment, there needs to be alignment across the organization on the scope of deployment and the approach. With alignment, the buy-in and support would turn a realistic plan into meaningful results.
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