What is self-service analytics?
Self-service analytics, or self-serve analytics, is a type of analytics software that can be used by any plant employee. It can include predictive analytics, predictive monitoring, supply chain analytics, market analytics, and more.
Traditionally, analytics required a lot of data science knowledge to clean, process, and analyze data, and to understand the predictions and insights that are produced. Although it could produce powerful and valuable information, it meant that the analytics process itself was slow and laborious; only a few skilled employees could interpret the results, and the number of questions and issues that could be addressed was restricted by the time and energy of the data science team.
Self-service analytics changes all that by using machine learning (ML) and artificial intelligence (AI) pattern recognition to make sense of the big data generated in a process plant. Self-service analytics are often also referred to as “plug and play,” because they can be integrated easily into any system without a lot of expertise for setup and configuration.
The main disadvantage of many self-service analytics systems is that the user must know what they are seeking, That is, what to ask of the data. Self-service analytics will not reveal any problem that the user does not know to look for.
Why is self-service analytics important for process manufacturing plants?
Self-service analytics opens up access to process manufacturing insights for all your stakeholders across the plant. The software can work far faster and more accurately than human data scientists, so there’s no longer a need to prioritize projects. Employees can turn to deep analytics for every business scenario, process question, and root cause investigation.
Self-service analytics also has built-in data visualizations and support wizards to help users understand the results of their analysis and view them from different angles. This eases the burden on data science teams who won’t have to spend time answering questions from colleagues in other departments, so they can focus on more complex use cases that drive business value.
Self-service analytics platforms can be used remotely to view and understand on-site issues. With the help of self-service analytics, process manufacturing employees can:
- Monitor and analyze plant performance
- Identify emerging problems and decide how to address them before they become critical
- Detect inefficiencies and examine the best ways to correct them
- Optimize equipment use and operations allocations
- View and optimize the supply chain
- Predict changes and trends in market demand
- Remotely investigate and diagnose plant performance issues
How can process plants make the most of self-service analytics?
Connect your equipment
Self-service analytics relies on accurate, real-time data from different parts of the plant. If only part of the operations is covered, you’ll end up with only a partial understanding of what is going on and thus the insights produced by the software won’t be fully reliable.
Widen access to all
The true value of self-service analytics is that all your stakeholders can access the insights they need to make better data-driven decisions, so make sure that you aren’t holding anyone back from accessing your tools.