Listening To Your Data To Solve Real, Complex, And Relevant Industrial Problems

In our world, machines are talking all the time, inputting huge amounts of data. You have to listen to what they’re saying.

Uptake Co-founder and CEO Brad Keywell delivered a keynote at the Smart Industry conference to some of the manufacturing world’s brightest innovators and technology experts last week in Chicago.

Below is a summary of his 30-minute talk.

We are in the midst of a data renaissance. Every single asset—which can be a machine or product—and person in an organization is now a data generator through sensors, computers, smartphones, and even their actions in the real world. Unfortunately, rather than harnessing the vast economic value of this new data, most enterprises are overwhelmed by the sheer amount of it.

But with so much data to contend with, how do enterprises know what information is worth acting on? Poor or incomplete data costs businesses up to 30% of revenue each year. So, how do they ensure that the most vital information comes from reliable sources?

See why the World Economic Forum recognizes Uptake for revolutionizing the construction industry

At Uptake, we’re helping organizations recognize the most vital information trapped in those reams of data through a business model we call “collaborative disruption.” This new approach allows us to partner with industry leaders to co-create solutions that work to solve some of large industry’s most meaningful and critical problems.

By working closely with machine operators with many decades of deep experience, we create solutions that reflect their knowledge. Making our software—and the machine operators—smarter and more effective than ever before.

In our world, machines are talking all the time, inputting huge amounts of data. You have to listen to what they’re saying.

Temperature, fuel pressure, weather, satellite, terrain data, you name it. And your people are creating data as well: their movements, their responses, their activities.

In effect, we predict the future—by transforming messy, unstructured data into insights. These insights are outputs and they compel quick, intelligent action, which is the input to the machine learning engine.

Streamline the process of building software solutions—with a direct connection in which innovators and customers co-create relevant solutions. And create a results-based model, in which customers pay for actual business outcomes—like uptime and increased productivity.

To do this, we harvest our partners’ data to find what’s working, and what’s not. Then, we provide real-time alerts and recommendations to the people who need them most: the individuals using the products and equipment on the job.

The impact of these improvements are substantial and numerous, including: maximizing revenue, reducing operating costs, improving operator safety, and strengthening environmental sustainability.

To make this happen there’s a secret ingredient. It’s about finding the right partner with the data expertise and empathy to solve the world’s problems that you face every day. To pair a digital disruptor’s speed and agility with your own deep industry knowledge. So you can drive your own disruption.

To drive your own disruption, challenge yourself to think about the many data sources in your enterprise. Then ask yourselves whether or not you can use that data more effectively to disrupt.

If you’re in the industrial sector, start by thinking about how you can make sure your data is put into action to make your most high-value assets operate better than ever before. To make your organization cleaner, more efficient, and more sustainable—and contribute to a better world. Just think of the possibilities.

There’s this new picture of tomorrow that we can envision and paint together with our partners. It’s a picture of a company that is using their data to maximize insights that result in meaningful, transformative actions.

Through disruption, companies can be more relevant, efficient, and ultimately do incredible things.