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Are You a Data-Intensive Application? [Yes]

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.  

IoT Management

Consider an IoT management system: It brings together data from an automotive plant, agricultural center, or smart-city infrastructure. There are thousands of sensors, terabytes of data, and rich AI/analytics capabilities that help make sense of this information. But the end-users of the management system need to find insights, drill into related data, identify possible new patterns, and then take action based on their investigations – be it to create a repair ticket for an automotive part defect, change watering cycles on a crop, or adjust electric/water distribution to respond to changing demands.

People Management

Consider an application to help recruiters (and their customers) sift through huge amounts of job candidates: End-users need to see trends, find outliers and distributions, drill into specific former employers or job-levels, filter and refine data by skills – create hypotheses, test them, and eventually take action on specific candidates. 

Networking

Consider a company, providing wireless and wired internet to hundreds of employees (or servers in data centers) in anywhere from one to hundreds of locations: Not only do the IT analysts need to manage the internet infrastructure (finding performance issues, identifying the root cause and updating/adjusting the network in response), but they also need to track hacking attempts (using both known and unknown access points), find security threats as they happen, and then quarantine impacted users, applications and devices.

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.

Cover Photo by Marcelo Quinan on Unsplash

Erica Lanyon