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Category Archives: Operational Intelligence
Weak signals are a crucial in the effective analysis of complex data, but due to the very nature of weak signals, they are not an easy thing to interpret. What are Weak Signals? Weak signals are changes that have ambiguous … Continue reading
We’ve married two of our best analytics tools which means you can manipulate, analyse and configure your data faster than ever. Once you have created a widget in the Pipeline Directory you can reconfigure the pipeline directly within Widget Working View. … Continue reading
Sabisu Analytics have been a big success with our customers this year. They’re built so as to apply to any customer with any process, so we think they’ll be really useful for you too. If you’d like a demo to … Continue reading
Data quality is a way of life – it’s something that needs monitoring, that needs reporting on…but you need the right tools. Obviously Sabisu can handle the reporting side, but we’re often asked, “How do you ensure data quality?” Often … Continue reading
Our Numerical Analysis team has been busy behind the scenes working with the developers to bring you Statistical Process Control and Anomaly Detection – it’s right there, at your finger tips in the Widget Working View. When you next load … Continue reading
Do you have data that needs to be analysed and interrogated quickly? Do you spend half your working day compiling tags from complex historians in an effort to get the information you need. Perhaps you’re running a large portfolio of … Continue reading
We are pleased to present some exciting new changes to Jidoka in the form of Chita, Sabisu’s analysis ‘sandbox’. Several new charts have been added making it easier to analyse your data and share that analysis with others. Chita can … Continue reading
This is the age of data, and there are few places where this can be seen more clearly than the process industry. The classic problem has always been extracting the useful information from this vast sea of numbers. Sabisu encapsulates … Continue reading
Anomaly detection of time series data is a complex problem, especially when the data is subject to measurement error and noise. First you must decide what constitutes an “anomaly”, usually through the construction of a statistical representation of past behaviour. … Continue reading