Have you ever been asked to provide a data point that is nearly impossible to find quickly or in some cases doesn’t even exist in a trusted format?  

You’re not alone. Getting accurate information about how your business is operating and performing is a common hurdle many companies face today. Data silos are a shared headache across many industries, but it is highly prevalent in Manufacturing.  

Data Challenges Faced by Manufacturers

In Manufacturing, most companies grow through acquisition. Acquisitions are great for expanding the business, but it throws a wrench in core data management tasks. We have found that this causes companies to begin a ripple effect of establishing several databases with numerous product lines or geographic regions that allow for an individualized assortment of data management solutions per location.  

Simply put, Manufacturing companies historically have been weaving a very tangled web of critical data.

If you are in a data-centric role, you already know that ungoverned siloed data can cause a domino effect of avoidable questions and errors. Data Architects and Analytics Architects alike are forced to take off their growth-mindset hat and put on one representative of Sherlock Holmes.

With a decentralized data strategy, providing trusted insights is anything but elementary. 

Digging up data that has been buried deep into systems, carefully shuffling through duplicate data, and hunting for clues to trusted data points becomes a part of the hurdles data experts face every day.  

In today’s pandemic and competitive landscape, Manufacturing companies need to look for a modern approach to the data hurdles in their way. 

As we all move into another uncertain year the desire to take advantage of scale, protect the time of employees, and have clear insights into all the data we collect are key to staying competitive – let alone operational. 

Navigating the path to insights and agility within your data can be confusing, especially in Manufacturing. So, we took the time and laid out the main issues that are causing the Manufacturing data dilemma, and how you can counteract it.