Artificial Intelligence is shaping the transformation of our world in many ways. From travel to tourism, to fashion, the influence of AI is leading to advancements that were barely imagined a few decades ago. But what feeds AI? While deep insights into the industry, real-time information and actionable recommendations are easy wins by Artificial Intelligence in almost every industry today, data is the lifeblood of artificial intelligence.

The adoption of AI-enabled business solutions continues to grow popularly across the tech industry. In post on the GPT-3 built by Open AI, we highlighted as an example of how AI might change the customer support sector. This is just one of the examples of the sectors the innovation of artificial intelligence could potentially disrupt.

However, admittedly, the excitement around the innovation from AI runs into gridlocks as businesses seeking to execute their ideas run into data problems. In a number of cases, there seems to be a continuous gap between our brilliant ideas and execution. This article seeks to address what we consider as the missing link to executing AI into efficient business models that will profit everyone involved from the businesses to the end-consumer.

Last week we talked about the big differences between NoSQL databases and the consequent responsibility on software architects to pick out the appropriate one for each project. You can read the post here: “The ultimate NoSQL comparison (Beyond MongoDB and CrunchDB)”.

Internet of Things technology is a hot topic. For the past 30 years enterprise data has been quite predictable, but everything has changed. Nowadays Big Data is snowballing and the impact on traditional relational database management systems is being significant. In this new context databases need to adopt and meet these new requirements as greater data processing agility and multiple analytical tools.