Industrial internet of things (IIoT) capabilities, automated workflows, enterprise-level platforms and other components of Industry 4.0 have opened the floodgates on data. From sensors on machines to enterprise resource planning (ERP) systems, manufacturers have a wealth of information at their fingertips.
As a result, the conversation is rapidly shifting from how do I get data to what do I do with it?
This is a question that manufacturers need to resolve sooner rather than later. As the industry increasingly leans into digital transformation, the ability to use data will no longer be a competitive advantage. It will be table stakes to staying in the game.
Relying on people to manually pull information and update Excel spreadsheets won’t cut it. To maintain an edge, manufacturers will need to get smarter about how they retrieve, sift through, measure, analyze and use their data. This will be key to making fast decisions based on relevant, real-time data culled from multiple sources instantaneously. And it requires automated processes for getting data into shape and for moving it between systems.
To help guide this journey, we have compiled a list of frequently asked questions for getting the most value from your manufacturing data analytics and operations.
How do I know if it’s the right data?
From the shop floor to the back office, there is no shortage of data. But not all of that data is the right data.
To get the data you need, start with a specific question or area of the business that you want to better understand. Then construct a data strategy document. The document should identify what data you need, how it will be collected, and from where, by whom and when. This exercise will narrow down your data sources and ensure you don’t overlook data that you need but are not collecting. Your end users are key to this process and can provide valuable insights into the data you have at hand as well as gaps in visibility.
How do I know if it’s good data?
It’s not unusual for manufacturers to discover sizable gaps in data uniformity when they start this journey. More often than not, these gaps are the legacy of information silos, disparate platforms, a lack of defined processes or training, and even staff turnover. The result? Incomplete, inconsistent, incorrect and duplicated records.
Perfect data is an anomaly, but manual data cleansing is time consuming. It’s also a costly and never-ending task. As timeliness, speed and accuracy become key components to customer satisfaction, and as the labor market continues to tighten, it simply isn’t economical to devote hours and days to clean-up efforts over the long term. The good news is, data profiling and governance tools are available to automatically identify data gaps or inaccuracies to ensure that the data you pull is good.
How do I protect data integrity?
To ensure that data cleansing is a one-time exercise and not a weekly activity, create a governance structure for how information is recorded, accessed and used. At the most fundamental level, the structure should specify how data is formatted and entered. Make data entry as foolproof as possible by providing defined choices, rather than free-form boxes, wherever possible. And dedicate time to train staff about how the data will be used, why it is valuable and what their role is in protecting data integrity. The more you can automate how data is entered, cleansed and shared between systems, the better you can safeguard data integrity.
How do I create actionable manufacturing analytics?
To make data actionable, it needs to be complete, clean and timely. You want to give users access to real-time information and intuitive self-service tools to help them analyze data. This can only be achieved with an automated pipeline that can pull, transform, cleanse and normalize the data.
Many manufacturers discover that, while they have systems in place to manage or even analyze data, the systems are not connected. This requires employees to manually enter, extract, manipulate or distribute data between disparate platforms, putting the brakes on information flow and work streams.
If this situation sounds familiar, rest assured that it doesn’t necessarily mean all your systems need to be scrapped. Often, data pipelines can be rebuilt to provide the automation you need to make quick, informed decisions; take prompt corrective measures; and accurately forecast for the future.
How can I make sure I’m positioned to scale for the future?
The data available to your organization is only going to grow exponentially. Creating a roadmap will ensure your people, processes and technology are aligned to easily scale up as new innovations come to market and your use cases expand. The roadmap should cover:
- The data requirements of your end users
- How you intend to use data in the near term
- Where you want to expand your data analytics capabilities in the future
- A governance structure to keep data structured and usable
- Infrastructure investments for moving and storing data
A sound, well-built roadmap is essential to identifying and avoiding unanticipated hurdles as your operations grow or change. It is also key to gaining staff buy-in. As you begin your data journey, invite employees to share their perspective on how data can best be collected and used. Building a business case with input from your employees will be foundational to creating and sustaining a data-driven culture.
How can Wipfli help with manufacturing data and analytics?
At Wipfli, our manufacturing and technology specialists can help you achieve a greater return on your investment by tackling your current challenges and anticipating future needs. Decades of experience in the manufacturing sector have equipped our team with knowledge about how to structure solutions to your challenges. Whether you need to develop an achievable roadmap, rebuild your data pipelines or overcome your data limitations, we deliver results that give your business a market advantage.
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