A Guide to Advanced Analytics

Written by Sean Tropsa on May 27, 2021

In the process manufacturing world, there’s been a significant increase in the accessibility into operational and equipment data. Teams now have visibility into both historical and near real-time data from their operation, and can even monitor this as it’s happening at remote locations. But the problem with this is that teams are drowning in data—”DRIP”—data rich, information poor.

Combining these colossal pools of data (for example, some chemical processing facilities have 20,000 to 70,000 signals, oil and gas working with 100,000 or more, and enterprise-wide networks reaching millions of sensors) with a lack of accurate organization, data cleansing, and contextualising, engineers face a major standstill.  

It’s easy to be overwhelmed by these amounts of data, but implementing tactful refinement processes leads directly to transformational insights. Too many of today’s process engineers and SMEs are spending their valuable time sorting through spreadsheets in an attempt to wrangle their data, instead of analyzing patterns and models that lead to useful insights. By implementing advanced analytics, process manufacturing operations can seamlessly visualize all up-to-date data from multiple disparate sources and make data-driven decisions that will immediately improve outcomes.

Don’t Let Your Experts Become “Data Janitors”

Unfortunately, some process manufacturing organizations are reporting that over 70 percent of their SMEs’ time is spent simply cleansing data when it comes to operational analytics. Moving this data from “raw” to “ready” should not be taking up this much time.

Advanced analytics technology takes care of these tedious tasks of accessing, cleansing, and contextualizing data. This way, your team is empowered to immediately begin benefitting from the insights.

How Spreadsheets Are Limiting Process Manufacturing Engineers

Using spreadsheets has been the standard method for analyzing data within the process manufacturing realm for the past generation. But to put it simply, they don’t meet the main requirement for users to be able to define critical time periods of interest and relevant context, quickly. 

Their drawbacks include:

  • Time-series data and related information (including time zones, daylight savings time, interpolation types and logic) are only able to be addressed by the user with complex formulas
  • Specific process manufacturing knowledge is not existent within the platform
  • Manual methods for data cleansing and contextualization
  • Adjusting to data changes is slow and inefficient
  • Collaborating and sharing analyses are difficult

The process of using spreadsheets to analyze process data is cumbersome and full of barriers, preventing organizations from analyzing data in the broader business context that’s necessary to increase efficiency and profitability.

Experts using advanced analytics who are on the front lines of configuring data have a streamlined approach and interface for accelerating improvements to production yield, quality, availability, and bottom-lines.

How Advanced Analytics Work

The technology leverages innovations in big data, machine learning, and advanced web technologies to integrate and connect to disparate data sources within process manufacturing and drive business improvement. 

Users are empowered to take advantage of:

  • Diagnostic analytics: Save on unproductive downtime by solving a performance issue and determine its root cause.
  • Monitoring and alerts: Easily stay updated on all equipment performance metrics without sifting through unnecessary data.
  • Predictive analytics: Identity when maintenance is necessary on equipment before downtime occurs, based on historical data, time-series data, and pattern recognition.
  • Repeatable analysis: Easily monitor all performance levels against models of ideal conditions with scalable calculations.
  • Data security AND collaboration: Share analyses and data between an entire operation, seamlessly and securely. The technology follows specific data governance protocols to the operation and is easy to manage.

Advanced Analytics: Looking Further

To put it briefly, advanced analytics gives your team the entire picture. The application draws relationships and correlations between detailed data through automated cleansing and easy-to-implement calculations and data contextualization, empowering your engineers to improve performance based on accurate and reliable insight. 

Seeq’s leading advanced analytics solution is the only application specifically designed for process manufacturers and their data. Learn more about Seeq’s advanced analytics and how they eliminate the need for spreadsheet exhaustion here.