Enterprise industrial data science is critical if you're to realise the full potential of IoT.
The challenge for most companies is not that they don’t have data, it’s that the data is in different systems, uneven, and not where they need it when they need it. As a result, it’s not very useful for real-time decision making. We solve the first problem by bringing all that data into context in one place and solve the second one with data science.
Data science uses scientific methods, processes, algorithms and systems to extract knowledge and insights from any data, structured or not, and is critical to delivering business outcomes from IoT. Without it, your IoT solution is nothing more than another dashboard – it’s where the magic happens!
End-to-end industrial data science services
Rayven can support you with all the skills and expertise that you need to deliver a data science-driven solution that will deliver real, measurable outcomes for your organization.
Business intelligence analysis
Measure and model factors that influence performance. How does store location and passing foot traffic relate to store sales?
We bring data together and into context – whether it is time series, relational, geographic – context gives data meaning.
Although our platform is for live-streaming data, we maintain historical records, so you can go back and look for relationships in data that you weren’t looking for when you started collecting it.
Instead of prospectively collecting data for every continuous improvement project, data is immediately available for analysis with everything from SPC and Pareto charts, to statistical analyses and machine learning.
While statistical analysis seeks to uncover how things happen, machine learning focuses on finding patterns and predicting what will happen. For example, forecasting solar demand and identifying anomalous power spikes.
Even with the most powerful tools, we still learn about the world around us and look for patterns through sight. Providing meaningful visual insights is a critical part of making sense of data.
Data analytics vs. machine learning
Machine learning takes large amounts of data and generates useful insights that help the organization. That could mean improving processes, cutting costs, creating a better experience for the customer, or opening up new business models. The thing is, most organizations can get many of these benefits from traditional data analytics, without the need for more complicated machine learning approaches.
Traditional data analysis is great at explaining data. You can generate reports or models of what happened in the past or of what’s happening today, drawing useful insights to apply to the organization.
Data analytics can help quantify and track goals, enable smarter decision making, and then provide the means for measuring success over time.
So when is machine learning valuable?
The data models that are typical of traditional data analytics are often static and of limited use in addressing fast-changing and unstructured data. When it comes to IoT, it’s often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of data points.
While traditional data analysis would need a model built on past data and expert opinion to establish a relationship between the variables, machine learning starts with the outcome variables (e.g. saving energy) and then automatically looks for predictor variables and their interactions.
In general, machine learning is valuable when you know what you want but you don’t know the important input variables to make that decision. So you give the machine learning algorithm the goal(s) and then it “learns” from the data which factors are important in achieving that goal.
Rayven's codeless industrial machine learning tool kit
The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Continuously evolving models produce increasingly positive results, reducing the need for human interaction. These evolved models can be used to automatically produce reliable and repeatable decisions.