Optimizing Data-driven Technologies at Enterprises

Data science, machine learning (ML), and artificial intelligence (AI) are becoming more and more vital for enterprise decision-making. Drones, robots, and other technologies are collecting more and more data, which is only valuable when that data can be analyzed and used in a timely manner. 

A new Forrester report, The Tech Executive's Primer On Data Science, Machine Learning, And AI, points out that, “All executives need to make strategic decisions about how and where to leverage these technologies, but few leaders have experience with them, so misconceptions abound, causing poor outcomes, wasted resources, and resistance to future initiatives.”

Here are ways enterprises can use data-driven tech, examples of successful implementations, and a look to the future. 

Data Best Practices

The aforementioned Forrester report gives insights for executives implementing or looking to implement data-driven solutions.

1. If it looks like AI in the movies, it's probably not. "The actual advantages and disadvantages of ML and AI technologies vary so dramatically from popular perceptions that if an idea, proposed solution, or vendor offering looks like something a layperson would expect, it will be doomed to fail, is overly hyped, or will have to rely on a person hiding behind a curtain," the report said. 

2. Balance technical feasibility and measurable business results. "Start purely with the business value and you'll choose use cases that play to AI's weaknesses and miss its strengths (think fully autonomous vehicles). Start with the data and you'll find true but worthless insights (e.g., bookings drive revenue)," the report said.

3. Take a lifecycle approach. “Increase your likelihood of success by planning your project from end to end and involving the solution's intended end users from the start and throughout the process," the report said.

4. Improve your data over time. Don't wait until you have the data just right to get started or you never will. "When it comes to AI projects, quality data is a myth. Work with the data that you can get hold of rapidly, drive the value you can quickly, and use the success to advocate for the next round of investment," the report said.

5. Improve AI capabilities over time. Just like with data, most successful data projects start small and scale based on successes. "That often means buying horizontal or vertical point solutions with embedded AI capabilities first and then going beyond the capabilities of these solutions using custom models and applications," the report said.

6. Worry about human bias first, then AI. AI contains the biases of the humans who build it. The best way to avoid bias is to carefully screen the data you use to train your AI models. "Above all, test multiple hypotheses, validate models, and monitor them over time for bias and, when applicable, fairness. If you do, your resulting models will almost certainly be less biased than human decisions. If you don't, you risk reinforcing and proliferating bias," the report said.

7. Don’t let AI projects linger. "Empower your teams to kill projects but capture the learnings and resurrect them in new, more viable incarnations," the report said.

Data In Action

AI and ML are maturing industries—there’s a long way to go. Most drone data analytics companies are still using traditional methods to process data acquired from drones. But research shows that AI is becoming increasingly essential and there are a lot of successes already deployed. 

Ardenna is utilizing image processing and artificial intelligence to automate the detection, classification, and reporting of anomalies found during railway and wind turbine inspections.

Nearthlab, a Korean software company, is currently working on a solution that will automatically detect and report damage on wind turbine blades. 

In an automated mission, Skycatch’s system can identify and track assets and material deliveries across an active construction site using deep learning models.

Optelos helps enterprises organize and analyze large amounts of drone and other visual data to help with energy asset inspections.

Next Steps for Data: Swarms

Many enterprises have a long way to go in implementing data-driven decision-making, but that doesn’t stop us from looking ahead to what’s next. 

From Forbes

An emerging trend as drone, AI, and ML tech mature is swarms. Swarm intelligence is the collective behavior of decentralized, self-organized systems (natural or artificial) that can maneuver quickly in a coordinated fashion. 

Picture cargo too heavy for a single drone to carry. By working together, a swarm of drones could transport that cargo. Or a group of robots inspecting a space too small or dangerous for humans. 

No need to worry about robots getting lonely when they’re working in groups. And also because they’re robots.