Build Your Process Expert & Data Science Dream Team with TrendMiner 2021.R1

Having people with varying skill sets within a team works best when everyone is able to really work together. Our latest release, TrendMiner 2021.R1, includes many user-driven product enhancements and expanded features, but perhaps the biggest highlight of the update is the addition of a completely new functionality, embedded notebooks.

This new feature puts data science into the hands of process experts, allowing for more robust collaboration – a dream team of process experts and data scientists. Let’s take a look.

The Next Step in the Progression of Industrial Analytics 

Self-service industrial analytics is all about empowering the people who know about industrial processes: the process experts. The aim is to put the data and the analytics in the hands of these experts, so they can have data-driven insight and thus make data-driven decisions. In this short video, our own Customer Success Manager, Nick Petrosyan, explains what we mean when we talk about our platform: 

But for you scripting wizards who have been asking for more traditional data science tooling, TrendMiner 2021.R1 takes industrial analytics to the next level with embedded notebooks. This feature makes it easy to jump from looking at data in a TrendMiner view to working with it in a code-based data science environment. Taking it one step further, visualizations from the notebook can be displayed as DashHub tiles. And as always, those DashHub views can be shared with your coworkers so that everyone can benefit from the fruits of your data science wizardry.

More technically speaking, with embedded notebooks, users have greater flexibility, allowing them to have a more advanced analytics experience. As process experts, you can prepare all operational data yourselves with TrendMiner’s advanced trend analysis capabilities, then load the data views into the notebook. Using your preferred data science library (e.g. Pandas, NumPy, SciPy, SciKit-Learn), you can create and run custom scripts yourselves for advanced statistical analyses, and use AutoML capabilities to build machine learning models for anomaly detection.

On top of that, you can make the results available for the entire organization in the form of new (predictive) tags and related monitors. And as mentioned above, you can also opt to operationalize the resulting notebook visualizations (also created with libraries of your choice such as Matplotlib, Plotly, Seaborn) as dashboard tiles in TrendMiner’s DashHub.

With embedded notebooks, process experts can become more analytics mature and get closer to the level of the analytics expertise of data scientists. And the potential of use-cases increases greatly because this notebook-technology gives its users the flexibility to play around with various algorithms and dive into data in ways they couldn’t before.

For example, now process experts can:

  • Extend process visualization with extra visuals for dashboarding and reporting purposes such as heatmaps, treemaps or joint plots.
  • Perform statistical analysis to compare sets of good and bad production data (t-test / ANOVA).
  • Do low-pass filtering or exponential smoothing to remove seasonal effects from data.
  • Create a non-linear predictive model to predict quality (using Neural Networks).
  • Do advanced Mass Balance calculations using the self-service analytics search results.

A Platform that Allows Process Experts & Data Scientists to Easily Work Together 

“Without the need for a data scientist” is a feature of self-service analytics that is commonly used – and it’s true. You don’t need a data scientist to perform advanced analytics within TrendMiner. This new embedded notebook functionality, however, allows users to bring data science knowledge and process knowledge together for an even more robust level of productivity and collaboration.

Process experts tend to think in terms of their production process and trends in the data: Is there an anomaly in the data? Is a part of the production stagnating? Data scientists think more in terms of data cleaning, models, and algorithms. But now they can more easily work with process experts on joint projects  since they can directly continue their work in the same tool where the process experts prepared the data beforehand.

As a result, the embedded notebooks allow for a collaborative, cohesive, and interactive dream team of process experts and data scientists that can more effectively solve process problems. An added bonus is that data scientists will have more free time to support projects for process experts if complex use cases arise. 

Secure a Competitive Edge with the New Functionalities and Expanded Capabilities in 2021.R1

TrendMiner 2021.R1 opens up a self-service industrial analytics tool for data scientists by providing the full flexibility of a notebook environment, embedded in the software with a one-click access to prepared contextualized data. What you get is a highly collaborative dream team of process experts and data scientists. That said, TrendMiner 2021.R1 also includes expanded capabilities to support multiple asset frameworks and many new user-driven product enhancements to help end users improve operational performance and overall profitability.

When you put that all together, what you get is an increasing level analytics maturity, securing a more competitive edge now and in the future. 

Learn more about TrendMiner 2021.R1
Read > the official release announcement for a more detailed overview of the additional functionalities.
Watch >  the release webinar to see it in action.