One insight to be taken from recent technological advancements is this: we live in an era in which everything is data. Artificial Intelligence (AI), data analytics and the Internet of Things (IoT) have given us access to more value from data than ever before.

Organizations looking to leverage this trend should be clear about how insights from this new data could help them solve their business challenges. The right solution will lie somewhere between specific use cases and the available data sources.

How everything became data

This proliferation of data, sometimes known as ‘datafication’, has been driven by technological advancements such as IoT, networks of connected devices that communicate with each other and the cloud. This has made it possible to collect data from a staggering range of sources,[1] from wearables to advanced industrial sensors used in manufacturing.

Technology is changing not only the range of data available to organizations, but also the kinds of data that can be used for insights. AI allows us to augment our valuable structured data sources with information from semi and unstructured data from sources such as PDFs, images and meeting recordings.

AI also provides opportunities to interact with existing data in new ways. People can use natural language interfaces to quickly and efficiently find insights in cumbersome or siloed sources of data, even if they are not data literate.

How businesses are leveraging new data sources

To put this theory into practice, let’s look at a couple of use cases that we’ve seen developing in the healthcare and consumer-packaged-goods (CPG) industries.

In healthcare and life sciences, cohort identification and patient enrolment in clinical trials is onerous and challenging, particularly for rare conditions or diseases. By using AI tools to identify essential features in patient records, organizations can translate these records into usable datasets, streamlining patient enrolment and strengthening clinical trial outcomes.

Our second example comes from the CPG industry, where shelf placement in retail is a vital but complex commercial question. A product’s position on the shelf—and its placement relative to other items—can significantly impact sales. However, understanding and quantifying this impact is no easy task. Machine Learning helps make this process scalable, automating the analysis and enabling scenario modeling to help optimize shelf layouts for maximum sales impact.

Both ends into the middle

As data continues to proliferate and technology advances, organizations have unprecedented opportunities to innovate and improve decision-making. Hopefully, the use cases we’ve shared demonstrate that the best solutions come from a ‘both ends into the middle approach’. Start with what you need, then understand what’s possible. The right solution will bring these ends together.

[1] AWS – What is the Internet of Things?