This is a data-intensive world. Almost every product’s success depends on data. Data that end-users need to understand, to drill-in into, to make sense of, and to take action on.
All of these examples (and more) are products or services that are predicated on data. Data empowers AI and machine learning to find patterns and make connections, but humans still need to understand that data, those patterns, and the insights provided by AI. Humans need to take action on that data and those insights, to remediate problems and impact the top and bottom line for their organizations. Humans still need to configure, update, and maintain the data and infrastructure providing that data.
What is perhaps most important is that while there is a clear structure to the abstract data and concepts of the product, it is ultimately humans that must determine if the specific relationships of that data on screen are useful. Are there valid relationships between the insights, information and analytics? A complex network can overwhelm a user, but in the end, a human must traverse it. Only through an exploration and investigation – from the problem, to root cause, to taking a remediation action – can this data provide real value to the end user and their organization.
But how do we build these Data-Intensive applications? Continue reading in our next article.