Five steps to building a data strategy for AI

Our data-centric world is driving many organizations to apply advanced analytics that use artificial intelligence (AI). AI provides intelligent answers to challenging business questions. AI also enables highly personalized user experiences, built when data scientists and analysts learn new information from data that would otherwise go undetected using traditional analytics methods.

AI-driven analytics delve more deeply into organizational data, deriving smarter insights that can give businesses a powerful competitive edge.

A well-considered data strategy is essential from the start. When organizations identify a business problem to be solved—and the decisions to be supported by the analytics—they reach the point where they need to think critically about the data required to solve that problem. Here’s a five-step process for helping ensure a successful AI analytics project.

Many enterprises struggle with data silos that can render a unified view of analytical data highly challenging. Achieve clarity on the goals of your analytics project first. Then, identify potential data sources across the enterprise. Integrating this data may require a data lake in addition to conventional enterprise data warehouses.

For example, relational databases include a wealth of structured, quantitative data. Quantitative data is useful for answering questions such as how many units were sold and when—and with what other products. However, structured data is much less useful for questions such as which product might have been sold with another or suggesting a new line of business to pursue. Augmenting structured data is necessary to answer these kinds of soft, strategic questions.

Data required to answer strategic questions is often qualitative in nature. Qualitative data generally comes from unstructured sources, such as text documents or notes, external website content, social media posts, and images. Organizations need to determine how they can such data to get additional value.

One example might involve the Internet of Things. Organizations with sensor data streaming in from a smart device, for instance, might augment the quantitative data with engineering notes or other types of softer data to enhance machine reliability and repair prediction.