Three Data Science Elements Needed To Turn Data Into Insights And Action

Uptake is able to deliver speed-to-insight and speed-to-value because of our approach and our people.

Most companies today have more data than they can handle. IoT will generate a staggering 400 zettabytes of data a year by 2018. As the lines between physical and virtual worlds continue to blend, that data volume will double every two years into the next decade.

Specific to industry, sensors on manufacturing assembly lines measure temperature, pressure, noise, and a host of other operating parameters. A single oil rig can produce 1-2 petabytes of data in a day. Smart and autonomous vehicles monitor everything from engine performance to tire pressure to environmental and infrastructure conditions.

What happens to all this data?

In most cases, not much. It is generated in such volumes, at such speed, and in such a variety of formats that most companies are unable to sort through and process it in a way that yields useful and timely insights. In fact, only about 1 percent of the world’s data is ever analyzed, much less turned into anything you could base decisions, or take action on.

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Traditionally, companies store all their data in huge data warehouses. Data scientists query the data, build models, run the models in a batch script, and produce reports. This process may take weeks, or even months. It might help managers understand long-term trends, but is not helpful to industrial applications, where timely insights and action are critical. With the traditional model, by the time you understand that a part or machine is about to break down, it’s already broken.

The best software companies change all that. They specialize in the whole lifecycle of data—ingesting, processing, and analyzing it at large scale to produce timely insights that you can act on.

Uptake is able to deliver speed-to-insight and speed-to-value because of our approach and our people. Here are a few important elements to our approach that we’ve found crucial to turning data into insights and action.

1. Timely vetting and validating of models and swift move to production environment

Our approach compresses and streamlines two main areas of friction: First is the time it takes to go from researching and writing an academic paper on a new technique or algorithm to vetting and validating that new model. The second area of friction is the time it takes to move a validated model into a production environment, where software has to be able to process millions of transactions a day to generate usable insights.

Systems and tools that address these sticking points are in demand. At Uptake, we developed one that has repeatable processes for ingesting, cleaning, and mapping data—regardless of its source or format. To back it up, great data science engines that understand how to deploy new models and consistently improve those engines. From the time a customer’s data is received, production-ready tools should be in place in days, not months.

2. World-class, broad data science expertise

People are the second element that sets the best data science teams apart. The truly exceptional data scientists have world-class skills in programming, statistics, and machine learning. On our team, several have won international data science competitions such as the ones hosted by Kaggle, the leading platform for predictive modeling and analytics competitions.

Beyond looking for award-winning technical skills, it’s important to consider filling your data science ranks with people who have deep experience and knowledge in various industrial domains. Our data scientists have industrial training with the machines they work with. This ensures that the team understands the machines and industrial environments they are writing programs for. Additionally, the insights they produce are always relevant to the real-world needs of that industry.

3. Insights and action that fit into workflow

Finally, our data science team is focused not just on the science, but on the customer—and delivering value to that customer without overhauling anything. Great insights are translated back into the client’s system and are integrated directly into the client’s workflows. Applications designed for specific industries or uses make Uptake’s findings actionable for a variety of people within an organization. For example, a useful system alerts operators of machinery to potential disruptions and generates recommendations for ways to mitigate those risks and capture new opportunities.

Learn more about Uptake's data science expertise.

Adam McElhinney is Chief of Machine Learning and AI Strategy at Uptake.