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Remove data analytics bottlenecks by upskilling your team

February 28, 2022 — by Andreas Riess & Heather Fyson
Upskill your team and remove your data analytics bottleneck

Digital technologies enable vast amounts of data to be automatically generated and collected. All these diverse, unstructured data from our interconnected machines and systems make analysis complex. Increasing complexity and the limitations of conventional methods and tools unable to handle big data are forcing us to find new ways to deal with our data.

The adoption of data-driven tools has gained momentum, but there is still a shortage of people in manufacturing companies who can use them. The data experts, often sitting in IT, are the bottleneck to efficient analysis and data-driven insight, impacting a company’s ability to compete. There is a growing need to train engineers and operators on factory floors to manage, analyze, and interpret data.

In this article, we investigate seven typical challenges in the manufacturing industry that create bottlenecks in the data analytics process due to the lack of data experts. These challenges are easily overcome when the workforce is upskilled to use self-service data analytics.

1. From time-consuming processes to independent solution-building

Setting up an analytics process takes time. Teams have to write down what they need, develop specs for parts, calculate variables, and send this all off to IT. IT works toward a solution, which in turn is reviewed by the data quality engineers. Going back and forth, it can take up to six months for complex processes to be implemented … by which time they need to be changed again.

With the skills to set up an analytics process themselves, a team will save time. When additional requirements are needed down the line, teams can add these themselves. Due to the self-documenting nature of a low-code tool, the process is transparent and shareable. Standard analysis processes can also be run on a server and made accessible to everyone.

2. From multiple applications to a single analytics tool

Currently, teams have to use too many different tools to conduct any kind of data analysis. They need multiple software applications to clean the data, analyze it, and produce a report. Jumping from application to application slows down the entire process.

Upskilling teams to use a data science tool that is able to handle all these steps saves a huge amount of time. Now, with all team members able to perform the data analysis and create reports, no bottlenecks are created, not even when last-minute reports are needed.

3. From isolated data sources to data connectivity

Data analytics bottlenecks also ensue when teams can’t access data easily. Data quality engineers complain that formats aren’t supported by the conventional tool, or that it’s difficult to access data from multiple, disparate sources. Conventional methods also restrict how frequently teams access their data—e.g. only once a week, because the process is computationally intensive.

Data science tools are designed to easily access data from different storages and formats, and to manage unstructured data, freeing up computational resources. It’s simple to connect, and there are no limits on size. Data is read into a workflow, cleaned and processed, and then merged into a single source, giving teams fast and convenient access to all the available data. 

4. From laborious trouble-shooting to fast analysis of machine data

Scrap and rework is expensive. Currently, quality engineers have to open huge Excel sheets to look for problems. This is time-consuming and costly. Increasing complexity in the modern factory is also compounding the situation, with conventional methods unable to handle the huge amounts of data generated by the sensors on machinery. As a result, analysis tends to focus only on a certain area we know we can handle, restricting our analysis.

By enabling easy analysis of maintenance and machine data, we can ensure consistent quality throughout production processes. When each stakeholder in the process is upskilled to use the data science tool, valuable subject matter expertise can be injected directly into an analysis, producing faster and more accurate results. The analysis workflow can be stored on a server, making it accessible across teams. If changes need to be made to the process, any upskilled team member can adjust the workflow and make it immediately accessible to everyone.

5. From a closed system to an open system

External stakeholders also impact our processes, but this is never connected with our own company data. This means we have only half the picture, which can have a negative impact on tasks like root cause analysis.

The data analytics bottleneck could be resolved up with an open platform that enables collaboration with externals. “Open” means that integration with other systems supplier data can be fed into the analysis to provide the full picture. This would have a significant impact, improving tasks such as root cause analysis, and would promote collaboration across companies.

6. From complex tracking to efficient traceability

It’s a nightmare to get batches of products back from a certain date range. Depending on the product, the number of parts can range from a handful to several hundred. This is because we often just can’t access the data properly. This task is usually handled by IT departments, which slows down the process and restricts our ability to track and recall defective parts quickly.

With self-service access to traceability data, we can remove this data analytics bottleneck. When teams are able to use the data analytics tool themselves, they can query the database, easily identify where in the chain the defective parts are, and if necessary trigger the recall process. 

7. From unforeseen disruption to predictable downtime

Unforeseen disruption has a massively negative impact on production and costs a lot of money. Predictive maintenance isn’t applied widely in manufacturing, primarily because conventional methods don’t support this type of analytics.

The ultimate goal would be to predict when a machine or part is going to fail, so that we can schedule downtime to replace it at our own convenience. When teams are no longer “stuck” with conventional tools, but upskilled with a data science tool, we could apply these advanced machine learning techniques, and unforeseen disruptions could be a thing of the past!

Agile manufacturing with self-service analytics

By making data analytics accessible to everyone, and enabling all business units to independently collect, organize, and analyze their data, we can remove the bottlenecks. Some companies are reluctant to upskill to data-driven technologies, fearing high investment. But digitization doesn’t need to be costly or resource-intensive.

Open-source, low-code tools are democratizing data science. They allow data integration from any source, scalability with reusable workflows, collaboration inside and outside of your organization, and agility for quick data exploration.

As a low-code tool, KNIME is quick to learn and enables even the most sophisticated data science work, while removing unnecessary technical complexity.

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