Today’s manufacturing operations and maintenance teams generate vast amounts of data in all forms. As a result, finding the right information, at the right time, and making it accessible to the right people are critical to keeping these functions operating at optimum levels.
Companies trying to understand how to make better use of their data are turning to various types of analytics for answers. These include how best to manage the data, how to determine what data is truly valuable, and when and how to align technology and people to assist with making meaningful conclusions.
Finding nuggets in a mountain of data
With digital transformation initiatives increasing the amount of data created and shared within today’s industrial organisations, making use of all this data can be a challenge. It’s not that this data isn’t relevant, but often some of the more meaningful and actionable nuggets are hidden within a mountain of disparate data, both structured and unstructured.
It is becoming increasingly difficult to make meaningful use of all the data being generated. This is particularly true for end users on the shop floor looking to expand their predictive maintenance and predictive analytics capabilities.
Data that was often managed separately in silos simply cannot be managed that way today. The implication is that maintenance and operations will need to have a much more cohesive vision around shared data and analysis. This is why many industrial organisations seek analytics solutions that can be used by operations and maintenance personnel alike.
Accessing data at the edge
As industrial organisations adopt smart manufacturing methodologies, there is a growing need to acquire, access, and share equipment and sensor data, and then transform all this data into actionable information when and where it is needed.
This data is typically generated at or near the edge layer (close to the point of origin), and processed, stored, and accessed at the database and Big Data layers. Moving forward, data will need to be managed closer to the origin point and then made accessible throughout the organisation.
The emerging democratisation of analytics
With all of this data being streamed and stored in a wide variety of locations and systems, making practical use of it can be a challenge, since mining such disparate data can be difficult. Until recently, most software programs available required specialised expertise and investments in traditional and often costly analytics solutions. These solutions also have all the attendant services costs such as implementation and maintenance. In addition, the skill sets needed to use these solutions have traditionally been left to trained data scientists and statisticians assigned to organisations’ quantitative staffs.
For years, analytics solutions were deemed suitable only for large organisations with dedicated quant staffs. These teams commonly consisted of people with skills that ranged from report writing, business intelligence (BI), and SQL programming expertise, and experts skilled in various forms of predictive and quantitative analysis. Consequently, many industrial organisations have been reluctant to fund analytics projects at the operations and maintenance levels.
More recently, however, new analytics solutions have been introduced to the market that are designed for other users within the business, such as operations and maintenance staffs. These users typically have limited quantitative skills and these newer solutions can provide value for a broader range of users within an industrial enterprise. As industry undergoes a digital transformation, non-data-science users now have more powerful and accurate tools at their disposal. They can now run various operations-specific predictive models and scenarios, and in near-real time if necessary, a capability not generally available until recently.
The time is right for maintenance and operations staffs to make better use of analytic tools to improve industrial asset availability and performance. A change is under way with software, as new, intuitive, and powerful products are being introduced by established and emerging business intelligence, analytics, and data visualisation providers.
In addition to being relatively easy to use (compared to traditional solutions), some of these new solutions enable users to construct models intuitively via visual representations of the data. These solutions are both powerful and intuitive and can allow business users the ability to create queries and some models without the need to write and sequence SQL (structured query language) queries. Other solutions require text-based commands using SQL.
What makes these new solutions accessible to a broader set of users? With these solutions, the rules and sequences for data evaluation are often set by manipulating visual elements (much like setting joins and formulas in some report writer programs), with the underlying SQL code available for those experts who want, or need, to review in greater detail. The result has been a new class of data visualisation analytics products that are powerful, yet intuitive and easy to use.
While sometimes derided by analytics experts as being too much like ‘black box’ solutions (because the underlying code when constructing and evaluating data models is largely hidden), they can nonetheless guide users with pre-configured code for common analyses. While these easier-to-use solutions do not necessarily replace the highly trained and experienced quant personnel, they allow operations and maintenance users to conduct ‘what-if’ modelling and analyses and make better use of analytics experts’ time to validate the underlying methodologies and models.
Many of these solutions also offer open APIs and other options to allow connectivity options to a wide range of data sources. In many cases, SaaS solutions are available, which can offer rapid time to implementation and a lower total cost of ownership compared to on-premise variants that require the purchase of perpetual licenses and associated hardware.
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