Whether you are a new manufacturing business or one that has been around for decades, the problem of fragmented data can quickly create serious issues and inefficiencies across your business. Many businesses may not fully understand this risk because, in manufacturing, processes to fix a problem are often physical. Yet, there is digital data involved in most areas of the business, and when issues do arise, they can be overlooked or deprioritized because it seems to have no immediate consequences.
Manufacturing startups often have the right resources to deal with data fragmentation, but they may not be aware of the problem. As manufacturing technology advances, there is undoubtedly an increase in opportunities to take and exploit new technologies. That could be the use of a 3-D printer, in which a computer-generated design is used to create something that’s ready to sell immediately. These manufacturers will be using the latest design tools, as well as applications and technologies to run the day-to-day business operations.
One of the most common issues with managing day-to-day operations is that those running the business may have a narrow mindset when it comes to managing the data that is generated. We find ourselves in a day-to-day cycle where we are highly focused on making sure we have the right amount of materials from suppliers to get the job done. It is about knowing what needs to be produced, how many, where it will be delivered, and when. Then, there is the most critical day-to-day job: managing the money and knowing which customer accounts are up-to-date, which suppliers need to be paid, and when.
But there is also a broader aspect of modern business that manufacturers need to address: what can be learned from all of the data that is being generated throughout the manufacturing lifecycle, from design to manufacturing to shipping and accounts payable. By examining historical data, there is nearly always something to learn that can positively impact a business’ bottom line. The trouble is finding it and knowing what is of value, and what is noise. As data is spread across silos around the business, it is easy to be overwhelmed with useless information. Data could be misinterpreted by different parts of the business as it is generated by various applications and often stored in different locations.
This is where well-established manufacturing businesses run into bigger problems, for the simple reason that they have much more historical data that has been created over time, across numerous legacy applications that are infrequently used or completely retired — making data hard to locate, or incompatible with today’s newer technologies. However, manufacturers can’t do away with this data because it likely still holds immense value to the business.
Defragmentating data eventually becomes crucial for all manufacturers. As the volume of data collected continues to grow at an exponential rate and competitive pressure grows across industries, there is a need to gain insight and value from that data.
Step one is to locate the data. This may not be easy, as a majority of a manufacturer’s data is likely hidden deep within applications that are not responsible for running the monotonous day-to-day business operations. Much of this historical data is now considered “secondary” and is archived for safekeeping. But value is lost in this process, because archived data is just as critical in deriving insights as the rest of the data produced across the business.
For example, data held in development and testing archives may provide insight into early attempts with new processes that failed but still hold promise. If that data is combined with information on new materials, tools, or techniques, it could show how these new processes might work now. But in order to do so, manufacturers must have the ability to locate and access the data, and analyze it, no matter how old it is or where it is stored.
This is the most critical factor in ensuring that data is available throughout the business. Once the data is located, the task of extracting value from that data can begin. This will enable all manufacturing businesses — small or large, new or well-established — to bring together and ask questions on information that can drive new efficiencies across the business and provide the competitive advantage they need right now.
Theresa Miller is principal technologist at Cohesity.
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