Sometimes “Small Data” Is Enough to Create Smart Products

When thinking about practical applications for artificial intelligence in your business, it’s easy to assume that you need vast amounts of data to get started. AI is fueled by data, and so it only makes sense that the more data you have, the smarter your AI gets, right? Not exactly.

When it comes to extracting intelligence by applying AI to data, context matters. In other words, you can build the biggest data lake imaginable, but if you don’t know what you’re trying to find and you don’t have the right data to do it, then you’re not going to get where you want to go.

That’s because AI is not some magical black box that can ingest mountains of data and then just spit out results. AI is a huge set of technologies, each with a specific, fine-tuned purpose. Companies that can zero-in on the impact they want to see and focus on curating the right datasets mapping to those goals have the best opportunity for generating really impactful results from AI.

Consider how the United States Postal Service (USPS) automates mail sorting. With the help of machines and advanced optical character recognition (OCR) technology, the USPS can now read and process 98% of all hand-addressed mail and 99.5% of machine-printed mail without human assistance. By linking this technology with a relatively small and finite data set of U.S. zip codes and cities, the USPS can now process upwards of 36,000 pieces of mail per hour. With the USPS facing harsh financial challenges in recent years, the impact of this automation is immeasurable.

Another interesting example of small, high precision data being used to make big gains with AI can be found in the airline industry. In 2015, Boeing launched the Aerospace Data Analytics Lab in partnership with Carnegie Mellon University to develop AI technology for airlines. One such project aims to dramatically reduce maintenance costs with AI by standardizing maintenance logs.

Every aircraft is required to keep highly-detailed maintenance logs. However, when planes travel around the world, communication starts to breaks down. Basic language barriers are the first stumbling block. From there, it only gets worse. Some logs are captured digitally; others are hand-written. Some maintenance workers stay in the lines, others jot notes and abbreviations in the margins. For the average maintenance worker, translating these variations on the fly can be next to impossible. But with AI and a narrow data set of common aircraft maintenance terminology, it becomes possible to capture and dynamically translate these logs in real time. By leveraging AI to improve the speed and accuracy of the airline maintenance workflow, airlines stand to save billions.

These are but two examples of how AI powered by precise data can lead to outsized impact. How can you put these ideas to work for your company? There are three main steps:

Set goals that tie back to business objectives. Setting goals with a cross-functional team that tie back to business objectives is a critical step to any undertaking, and AI is no exception. AI is prescriptive by nature; the more narrowly you can define the business objective, and the more contextually precise your data set, the more likely you are to get some meaningful results.

What’s often overlooked is the importance of establishing a cross-functional team with visibility across the organization. This is essential to determine where in the organization impact is most needed. If you build a team that brings in operations, sales, finance, and the executive suite, you are more likely to figure out where the real bottlenecks and opportunities are, and you are more likely to come up with practical solutions that actually start solving them.

Tame data chaos. Every company has a data set with unique value to their business. Often, however, there’s a disconnect between the data and the value. You’ve captured data, but it’s not clean, precise or actionable. A useful framework for taming data chaos and extracting small high precision data is focusing on the lifecycles of customers, partners, and suppliers. Following the lifecycle shows you all of the steps, systems, and stakeholders involved. As you examine these lifecycles, you will find gaps where you are leaking value. These are your opportunities to make a clear and measurable impact. Focus on the key data surrounding these gaps and you will have more precise and actionable data.

Select the right technology for the job. There’s a lot of buzz right now about machine learning and AI — and it’s justified buzz. These are amazing technologies with great promise for any level of executive in any B2C or B2B company. They are also now available at a fraction of the cost compared to even five years ago. Don’t hire a team of a hundred data scientists; look to the growing ecosystem and pick the right tool for the job.

In the world of digital business, companies are always looking for big bang solutions — some breakthrough that can give them an edge. But the reality is that when you get practical, you can start racking up lots of smaller wins — and you can do it quickly. Over time, this accumulation can drive massive outcomes.

This is the right way to think about AI. It’s not a magical black box — it’s a highly-specialized set of tools. It’s not about shooting for the moon — it’s about winning the ground wars. And it’s not about mountains of data — it’s about small, high-precision data.