Every organization, no matter the industry, has a need for data analytics to better understand their business and improve the decision-making process.
In the manufacturing industry, the importance of data analytics was first realized when major companies such as General Electric sought to compare the performance of their business across various countries. The goal was to identify which location would become a world leader in industrial production by using predictive modeling on financial indicators like sales and leasing rates. Since then, the manufacturing industry altogether has become inherently more aware of the need for effective data analytics, but the practices to support these goals have not developed at a comparable rate.
Today, there is no shortage of applications that organizations of all industries can access to better support their success by use of data predictions and monitoring. However, implementations of these modern data practices haven’t proved to be completely successful for businesses across the board. Manufacturing companies continue to struggle with taking advantage of data analytics due to the quality and nature of the data they are using, as well as the importance places on traditional practices of operations.
The volume of data one organization produces can seem overwhelming at first, and if not properly approached and managed, might cause more of an obstacle than a solution.
For example, if a company wants to use historical data to better understand trend predictions, and they lack processing to filter out the noise, the amount of cluttered data will prevent or slow down the process, not allowing an answer or understanding to be determined. In this case, the team would need to take a step back and re-evaluate which data is useful for furthering their project.
This "business transformation" is important for companies that are trying to advance their operating procedures via data analytics. If a manufacturing company cannot determine how useful each piece of information will be from one step of the process to another, (i.e.; from raw materials to final product) then they will never allow their processes to run at 100% efficiency.
Another problem manufacturing organizations face is data siloed into disparate systems. At first thought, segregating data into specified categories may seem like a good way to keep data secure, but it can be detrimental when the siloes don’t communicate with one another. If a company is paying attention to the performance of one system or instrument, and some other instrument or system is not producing accurate data, then the company will never be able to fix that issue. Data in different systems should talk to each other and work as one entity so that any issues can be identified and resolved immediately.
The final and perhaps the largest problem faced when trying to utilize and analyze data is an outdated mindset or lack of willingness to change business practices. Large manufacturing companies, like Airbus Group SE have become accustomed to dealing with large amounts of data from many different sources through traditional methods like warehouses, Hadoop clusters, etc. With the number of accessible applications and the advancements in data technology, these traditional practices and beliefs are outdated and not enough to keep up with modern day processing.
Many in manufacturing have seen the value in prioritizing data and analytics to drive success and have made organizational changes to meet these goals. For those companies who have yet to overcome these challenges, they need to rethink their approach to data and focus on forming a holistic data strategy. If you don't establish a plan that starts with your business objects in mind, and then aligns your data, systems, and solutions to meet those objectives, you will be caught in an endless cycle of dead-ends, revisions, and one-off solutions duct taped together.