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Self-service Analytics Technology for The Manufacturing Industry

Self-service Analytics Technology for The Manufacturing Industry

The manufacturing industry has recently adopted a more data-oriented process since the dawn of industry 4.0. manufacturing machines produce data at an astonishing speed. The amount of data the processes and machines produce is also humongous. Smart devices that enable Industrial Internet of Things technology are connecting everything together. With such a connected data-oriented background, the manufacturing industry is always looking for ways to use the intelligence trapped in the raw data.

Business Intelligence is a highly valuable asset to a company. Generating business intelligence is a challenge. Processing and manipulating collected data into information is a job for experts. In the manufacturing field, business intelligence helps the business predict and foresee purchasing, sales and marketing game plans. A group of expert practitioners help the business achieve such goals. Still, with the growing demand for fresh business insight, many manufacturing businesses face difficulties in employing enough business data analysts to create insight to keep up with the demand.

In such challenging situations, self-service analytics comes to the rescue. Self-service analytics is a business intelligence tool that automates some parts of the business analysis process. Instead of depending on expert employee knowledge, the automated business intelligence tool generates its own data models, metrics, and siloed analytics. Such tools increase the speed of the whole process and increase the accuracy of the overall process.

What is A Self-Service Analytics Tool?

The self-serving analytics tool is essential for enterprise management. It extracts data from sources and loads the data into a destination, making it available for self-service analytics or BI systems. It helps the users understand what is happening with the manufacturing process by creating information from the data the manufacturing process captures. Self-analytics tool automatically visualises data after processing with user-friendly dashboards that represent data to help understand trends and patterns. As the name suggests, self-analytic tools statistically analyse data for employees who need to make decisions about daily activities without waiting for heavily analysed in-depth reports about overall business. The tool help business users make operational decisions and get insights from it. Ultimately, the tool allows the manufacturing industry to automate and streamline their production lines by making critical information available throughout the system without depending on expert knowledge.

Why Does The Manufacturing Industry Need Self-Service Analytics?

Streamlining manufacturing processes to achieve a smooth production line is the key for a manufacturer to achieve an uninterrupted production flow. The smoother the production line is, the higher the company will get profited. Informed decisions through thoroughly analysed information help streamline a process smoothly. As discussed above, the rising demand for data analytics in the company might put human capital management in a bit of a difficult situation. If the manufacturing company have a self-service analytics system, the picture might look different. Self-service analytics tools help employees get familiarised with the systems and data it produces. When a legacy system handles the data analysis most of the time, a separate team of analysts or consultants does the job for the employees. Often, the team is outsourced. In such situations, the internal staff misses the chance to see the process of processing and understanding the behaviour of data within the system. Therefore a Self-service analytic system is helping manufacturing teams to get involved in the system better.

Organisations That Will Use The Self-Service Analytics Technology

In organisations where legacy analytical systems are still in practice, professionals spend a considerable amount of money just querying data and gathering reports. It is time-saving and cost-effective for an organisation to have a self-service analytical system in a manufacturing business. Data specialists such as expert analysts and scientists will have time to focus on more complicated analysis by lettering others handle mundane, repetitive tasks or automated insights driven by AI. The data analytics professional can hand over the repetitive work and daily querying data and generating reports and focus on long-term and high-value projects for the company’s betterment.

Why Is it Important For Legacy Systems?

Legacy systems are slow and time-consuming, which often leads to unnecessary expenses. Using a legacy data analysis system might not efficiently serve the system and make sure the reports and insights are reached the correct nodes at the right time. This affects the decision-making process. Self-service analytic systems solve the problem by automating generating the reports and connecting different departments or nodes within the system itself so there won’t be any delay to receive the report after it has been generated. Legacy systems always needed expert help to overlook the process, and the data analyst knowledge base played an essential part in the process. Such dependencies make the system slow, expensive, error-prone, and inefficient. Self-service analytical systems eliminate the need to have expert knowledge of analysis and computer programming because the Self-service tool automates the business intelligence creation. This feature allows the system to distribute the responsibility among many others rather than always depending on analysts. With the help of self-service analytics technology, employees such as accounting, sales, ERP, and HR can generate their own reports without any wait time.

Importance of Using This Technology in Other Organisations?

In most organisations, the manufacturing process holds considerable mission-critical information. When they use the services of a data analytics team, especially when they are outsourced, the company has to hand over mission-critical information to them. The company then has to completely entrust a third party outside the manufacturing department to protect its precious asset. Having a separate tool to automate the process lowers the risk of data breaches and IP theft.

Difficulties in Self-Service Analytics Systems

There are numerous self-service analytics tools available in the market. They are capable of connecting to any number of the file system, or databases. But still, they fail to satisfy the manufacturing companies. Because machine operations are different from loading data from static files. Data structures are complex, there are various teams involved in the operation by shifts, and the same process has different operational scenarios, data can be scalable from one machine to multiple machines, one plant to multiple factories. So the modern manufacturing companies find the existing self-service tools and BI tools fail to meet their requirement.

How Can Cerexio Help?

Is your digitally driven organisation taking too much time in generating actionable insights? Connect with Cerexio tmpower businesses to reach their top potential. Our state of the art software solutions is the answer to your worry about automation. You can now employ our AI-powered analytics tools that will make your smoothly streamlined automated manufacturing facility a reality.

Cerexio’s proprietary multi-protocol message broker is optimised for the manufacturing process and it has the intelligence to connect multiple machines in a real-time or batch process. Envision your empowered business with all your data players to capture essential data streams and take action promptly anytime, anywhere they want. Contact us today to take the first step towards seamless self-reliant data analytics.

Open Doors To New Manufacturing Processes With Self-Service Analytical Tools.

Self-service analytics opens up a whole new branch of streamlining manufacturing processes armed with top-notch technology to understand what is happening across the manufacturing process. Helping employees be part of decision-making and connecting with the process through data is empowering for both employees and the system. Self-service analytical tools not only protect critical data from going out of its’ most secure places but also helps to get processed by people who actually understand the importance of taking manufacturing decisions with the precious data they have. By removing the barrier to entering the world of creating knowledge out of data, the self-service analytical systems add so much value to the manufacturing processes of the manufacturing industry.

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