The Ins and Outs of Augmented Analytics

By Yaroslav Kuflinski, AI/ML Observer at Iflexion

A century ago, cutting-edge industry machinery could make any business a market leader. Now, data represents the driving force behind business prosperity. If you know how to collect, process and analyze data, it can not only increase your company’s income but also boost productivity and enhance product quality.

This all becomes possible due to technology such as the Internet of Things (IoT), Artificial Intelligence (AI) and Machine learning (ML). By analyzing meaningful data, AI and ML can implement predictive maintenance to diagnose and forecast equipment breakdowns and downtime, and schedule repairs in advance. As a result, you will be able to run maintenance services timely and efficiently, providing maximum productivity time.

According to IoT Analytics, the usage of predictive maintenance technology is likely to grow by 39% with $10.96 billion spent on the technology by 2022. These numbers span leading companies in 13 industries and 7 technology areas.

In this article, we will learn how predictive maintenance is already being used in heavy industry and how you can push your business further using augmented analytics.

How Predictive Maintenance Works

Predictive maintenance is a technique used to evaluate the lifespan of a product or a piece of equipment through monitoring to ensure repairs are done when they are required. In terms of technology, this involves several steps:

1. Data collection
   Raw data is collected by data acquisition systems, pre-processed by edge IT systems and transferred into a central hub – in-house or in the cloud – for further analytics and storage. It comes from two sources: business data from IT systems and asset data from IoT system. IoT represents a set of sensors attached to the assets monitored, collecting, exchanging and communicating the data gathered.
2. Data processing and analysis
   Raw data is cleaned and converted for analysis by ML technology. Predictive maintenance uses algorithms to detect possible faults and inform you if your assets need repair or replacement.
3. Results presentation
   Finally, the technology interprets the data for human understanding, providing you with recommendations on which maintenance tasks require your priority.

Predictive maintenance may seem to take time to deploy and requires investment and technical support; however, the results it gives are generally more accurate and profitable for business than its alternative preventive maintenance, a conventional method based on scheduled checkups.

Preventive maintenance tends to lag behind as it doesn’t rely on analyzed data and provides limited information on the condition of machinery, resulting in insufficient servicing – unnecessary servicing, late servicing, replacing parts when it’s not needed. Meanwhile, predictive maintenance is data-driven and performed in real time, meaning that it will inform you about the possible failure in functioning as it happens, for example, knowing that a piece of equipment is due to go out of service within the next month.

Benefits of Predictive Maintenance

• Asset condition monitoring in real time

You can track the working performance and conditions of any of your assets in real time in the form of understandable statistics on your computer, tablet or phone, monitoring equipment away remotely.

• Non-intrusive monitoring

There is no need for your assets to be assembled and disassembled for routine condition monitoring once you install sensors on your equipment, instead results can be viewed on devices through custom AI apps designed to make data understandable. The IoT sensors can track a significant number of conditions, such as acoustics and vibration, vision and light, electromagnetic, speed, chemical, temperature, humidity, position and proximity which are interpreted and presented in a form of statistical workflow and note even slight malfunctions.

• Easy-to-perform asset maintenance

Employees will find it easier to monitor and perform maintenance tasks on equipment as historical data and schematics for the machines can be aggregated and displayed on a dashboard.

• Asset servicing when necessary

With condition monitoring in real time, it’s become easier to schedule asset serving for the future. It also gives the possibility to better allocate the budget for major and minor repairs more accurately.

• Production process optimization

Sensor data and ML can retrieve and process vast volumes of scrambled data, extracting accurate and valuable information. Processed sensor data, along with historical maintenance data analysis, can provide you with insights on workflow optimization, maximizing uptime and reducing equipment breakage.

• Reduction of costs

In heavy industries, uptime is critically important, even one hour of interruption can cost your business billions of dollars, depending on your operating costs. Predictive maintenance helps to prolong equipment usage and lifetime, and streamline machinery workflow, reducing the number of maintenance tasks, inventory costs, breaks, and downtime.

While the benefits of predictive maintenance are impressive, drawbacks and system imperfections are unavoidable; no perfect system has been invented to date. The main system shortcoming is that it still requires much human intervention, especially when it comes to data analysis and physical effort for repairs, which, as of yet, cannot be avoided. 

Augmented Analytics in Data Analysis

Moving towards data analysis automation, a new concept of augmented analytics has emerged. With the help of AI, ML and NLP (natural language processing) it becomes possible to generate informed business insights based on data collected from business and industry sectors. In theory, this will change the role of data scientists and increase the capabilities of data processing and its costs in the future.

According to Gartner, augmented analytics is the future of business data analysis and about 40% of the work the data scientists are doing now can be automated by 2020.

How to Make the Most of Augmented Analytics?

Augmented data analysis can be divided into three development levels. Most of the businesses that provide this service, such as Microsoft Power BI, Qlik Sense, Tableau, IBM Cognos Analytics and IBM Watson Analytics are already operating with well-developed first-level data solutions; collecting and processing capabilities and moving towards the second level of data pattern detection.

Augmented analytics includes the following development levels:

1. Refine and combine data for deep-analysis
   The main purpose of augmented analytics is to clean and label the data, collecting it from various platforms. Although human assistance is unavoidable at this stage, the system requires less expertise in data integration.
2. Analyzing your data in business combinations
   After combining and analyzing the data, it’s essential to implement it to business reality. It’s not enough just to witness malfunction, you need to understand the reasons. For this, it’s important to analyze the data in all possible dimensions to understand the direction troubleshooting is needed in. Augmented analytics reduces the need for human resources on the combining and comparing process, creating a level of AI automation, although the support of data scientists will still be required to adapt this information for practical use in business.
3. Insight automation
   Insight automation, excluding human intervention, is only a future projection of augmented analytics development, which may be achieved within the next 5-10 years. This will allow the system to not only gather and organize the information but work out efficient steps for business development.

Although augmented analytics may seem to be future technology, there is a range of companies that are already implementing such practices. For example, Volkswagen, the famous vehicle-maker, harnesses the power of AI predictive analysis technology to boost sales revenue, relying on the recommendations from Blackwood Seven media agency.

The Predicted Effects in Heavy Industry

For businesses seeking to boost sales, improve productivity and take advantage of many more benefits, considering employing new technologies is a must. In terms of heavy industry, businesses engaging augmented analytics and predictive maintenance can expect the following advantages:

• Increased asset reliability;
• Increased product quality;
• Lower production costs;
• Improved safe working conditions;
• Improved data preparation and analysis;
• Automatically generated insights;
• Strengthening their competitive position on the market.

Combined, this makes a strong case for businesses to start exploring the potentials today and see where they fit with business objectives.