The advantages of Plant-wide historians vs. Relational Databases

Industrial businesses have entered the age of “big data,” the volume, variety and complexity of data they manage is exploding at record rates. Clearly the volume of data from which to extract value is beyond the capability of a traditional data management system.

The challenge of managing big data for industry goes beyond the sheer volume of information; there is the diversity and complexity of data, which comes in various formats and from disparate sources. There are typically “islands” of process information that must be aggregated, stored, and analysed to derive context and meaningful value.

To leverage big data, industrial businesses need the ability to support different types of information, the infrastructure to store massive datasets, and the flexibility to leverage the information once it is collected and stored—enabling historical analysis of critical trends to enable real-time predictive analysis. As businesses increasingly realise that much more of their value proposition is information-based, technologies that can address big data are quickly gaining traction.

The Industrial Data Challenge
In an increasingly competitive environment, companies need to gain a sustainable advantage by achieving operational excellence, a journey that begins with data for process visibility. The vast amount of information continually increases, and it’s imperative for companies to truly understand and control their manufacturing operations by efficiently collecting critical data and maximizing its value. Optimised data enables better and faster decision-making, increased productivity and reduced costs.

For example, a manufacturing manager may want to understand the significance of temperature variation on quality as the rate of flow of materials varies through a production line; or a power plant supervisor may want to analyse five years of past data to examine anomalies and variations to understand whether they were followed by subsequent outages to enable predictive analysis. This level of operational insight requires the ability to quickly run a query against large data sets for specific time periods—a unique and powerful capability that calls for an industrial data solution.

Advanced Historians vs Relational Databases
While historian software may not yet be top of mind when it comes to industrial big data solutions, what many companies may not realise is that these advanced, out-of-the-box solutions are specifically designed to efficiently collect, store and manage large volumes of time-series process data, which is precisely the industrial big data challenge. As

data sets grow larger and more complex, advanced historians offer an effective, simple, and easy way for companies to efficiently leverage vast amounts of real-time and historical process data, a critical need for optimised decision support.

Relational databases (RDBs) have helped many manufacturers gain more information about their operations by supporting simple operator queries, answering questions such as “What customer ordered the largest shipment?” They are built to manage relationships and are ideal for storing contextual or genealogical information about manufacturing processes, but are rarely the best approach for vast amounts of process data collection and optimisation.

On the other hand, plant-wide historians are designed for manufacturing and process data acquisition and presentation. They maximise the power of time series data and excel at answering questions that manufacturing typically needs to address real-time decisions in production such as “What was today’s hourly unit production average compared to where it was a year ago or two years ago?”

There are critical capabilities that manufacturers need to consider that position plant-wide historians as a better option for leveraging raw data from sensors and other real-time systems to improve production for operational excellence.

Data collection
RDBs do not offer built-in data collection capabilities; therefore, custom code has to be written to insert and update records. This is sub-optimal because with a custom system, development costs and continual enhancements can be very expensive and time consuming.

However, plant-wide historians include built-in data collection capabilities and can capture data from multiple sensors and systems. For example, GE’s Proficy Historian can collect large volumes of real-time plant floor information from various plant-floor devices at incredibly high speeds.

Instead of having to build custom software for every type of data source as you would for an RDB, Proficy Historian does not need to know any of the details regarding the propriety data sources. It can instantly connect to any OPC-enabled solution to collect data, providing flexibility, time savings and reduced costs.

Speed
RDBs can require significant custom engineering for each defined access and have comparatively slow performance when the queries cover large data sets or associated periods of time.

In contrast, a plant-wide historian provides a much faster read/write performance over a relational database and “down to the millisecond” resolution for true real-time data.

Data compression
With an RDB, the maintenance alone can be a full-time job because you have to continually manage archives and disk space due to the lack of compression; performance can be severely undermined, even with proprietary, pre-compressed data work arounds.

However, the powerful compression algorithms of plant-wide historians enable you to store years of data easily and securely online, which enhances performance, reduces maintenance and lowers costs.

Leveraging the value of plant-wide Historians and RDBs
Plant-wide historians offer a clear value proposition for logging, storing, and retrieving high volumes of process time series data. However, RDBs place in industrial applications are valuable for drawing relationships between contextualised data collected to drive continuous improvement and promote a safe supply chain.

Plant-wide historians are like “black box recorders” for your plant, capturing all of your raw data and providing the first level of context “time” to it. This can be leveraged by additional operations management applications.

The future
Business and IT leaders need to ask themselves whether their industrial enterprise is maximising the full potential value of their process data and using that insight to drive real-time improvements. As data volumes continue to expand, information-driven strategies will only become more pervasive as a source of competitiveness—making the use of big data in the industrial space ever more imperative.

A closer look at advanced historians demonstrates how such technologies can help enterprises leverage their time-series process data by providing the ability to efficiently run real-time analytics within massive sets of historical data. These solutions have the potential to revolutionise the way enterprises do business by providing critical insights for timelier operational decisions while also enabling continuous improvements across the enterprise.

Looking into the future, as information increasingly empowers enterprises to understand their businesses better and to foresee what is possible, those that capitalise on the value of big data will gain insights to improve performance beyond their competitors. They will be positioned to better innovate, compete, and drive value to significantly accelerate business growth and drive optimised performance for long term success.