As was noted in the previous article, it will not sustain your business to risk the future of your company on intuition and educated guesses in the long term future in the current data-driven market.
Create New Data through Existing Data
The promise—and process—of producing value through ML for an enterprise is often challenged by how much data must be utilized to train and then re-train a model. This process is magnified through the emergence of continuous delivery (CD) for ML modeling, by which a model is continuously trained and released all over again, to yield more and more data. It can become very easy to obsess over just the volume and how much it costs to store this data, but please remember the larger aims of the project. It’s important to engage in cost rationalization, however this must not come at the expense of yielding added benefits to your customers.
Give everyone working at the organization access to data
Gaining access to the correct data is about more than just APIs. Almost always, even if the right APIs for the situation are already in existence, they’re typically only accessible to programmers who develop the software that leverages those APIs. A group of tools, referred to as low-code or alternatively no-code tools, democratizes the accessibility of data through targeting users of the company (occasionally known as “citizen developers”). This method empowers employees at the company to develop, build, and deploy business automations and applications which leverage the data of the enterprise.