There's a lot of buzz about data these days: Big Data, having a data focus, using data analytics. But it's not just a fad. Using a data-driven approach in your organization can transform your operations, your processes, and your bottom line. This can be especially beneficial for manufacturing companies who are looking to improve their efficiencies, as taking a data-first approach can
improve forecasting accuracy by 85%, increase throughput by 30%, and can increase worker productivity by 30%.
By collecting data from across business units, from machine sensors, and from market sources, manufacturing organizations can analyze that data for patterns, extract insights into what's going well and what needs tweaking, and can make changes certain that they're basing their decisions on data. But while that may sound easy, organizations can run into a number of challenges as they begin to implement a data-first strategy that can set them back before they even begin.
Six Challenges When Implementing a Data-First Approach
As you begin strategizing around your data collection, analysis, and implementation, you're going to face challenges. Here are some of the obstacles to look for, and how to overcome them.
Challenge 1: Getting a handle on your data
One of the biggest challenges will be getting a handle on your data, as you’re likely one of the
majority of organizations today who are managing at least 5 petabytes of data, 80% of which is likely unstructured. But you can make your data less unwieldy by asking what data currently exists, what data do you need, and what do you need the data for? As you collect your data, ask if it's duplicated, is it accurate, and how much cleaning will it need? Above all, do you have a platform that processes, analyzes, stores, and secures that data?
Challenge 2: Overcomplicating
As you begin looking at and gathering your data, you may overcomplicate the process if you don't have a strategy in place first. Gathering all the data you can won't help you if you don’t know what you’re gathering it for. Instead, determine the problem you need to solve and identify what success looks like. How will it be measured? Then pick just three to five key performance indicators, or KPIs, to help guide your data collection decisions. You can add more later, but these baseline metrics will help your team avoid data overload and gain confidence. This could include measures like production volume, downtime, costs, and capacity.
Challenge 3: Protecting data privacy and security
As you begin to collect data from various sources in your company, prioritize data privacy and security while also allowing the data to be accessible and meaningful to you. There are growing restrictions around privacy and data collection today, so be sure to respect regulations and restrictions, including CCPA and GDPR. If you are unsure of governmental regulations, seek legal counsel.
Make sure that the data you're gathering is protected with multiple layers of security on your systems. Look at leveraging multi-factor authentication (MFA) for logins, and make sure that your cloud applications and access points are encrypted and backed up. Set up firewalls to control the perimeter of on-prem resources, have antivirus installed on machines, and above all, ramp up your threat intelligence and the anticipation of attacks.
Challenge 4: Change management
Another challenge will be overcoming organizational resistance to changing the way in which you work with data. Your organization may not be used to collecting and working with data, and you'll need to shift the culture in order to make it a data-first company.
To start, involve your workforce early on in the processes and changes. Form a key team that will lead the change. They can champion your new approach and elicit feedback from employees of all levels. Offering training and development for your workers in data analysis is a great way to upskill — according to our “
Voice of the Essential Worker” report, 80% of manufacturing organizations are making upskilling a priority for their workers. Data analysis is an emerging field, so look for new talent coming out of college or trade schools with skills in how data is used across the business.
Challenge 5: Data hygiene and governance
As you gather data from various sources across your company, you need to not only make sure that it's usable, but that it's up-to-date as well. Having good data hygiene — or ensuring that your data is complete, accurate, and true — allows for more accurate analysis and more applicable results. Evaluate how your data is being collected, when it's being collected, and if it's the right data to collect in order to get the best data possible.
Good data hygiene stems from good data governance, or deciding how your data is managed and used. Invest in a modern technology platform to manage all that data and that can evolve with data standards, security, volume support storage, and processing modern APIs.
Challenge 6: Building in business resilience to respond to a changing market
Something often overlooked is the impact of external data and how it could impact your operations. This necessitates that organizations have a way to ingest external data as well from both the market and the industry. This could include data on climate change, geo-political forces, material shortages, and other factors and impacts.
The top benefits of being data-driven include
improving business agility, improving automation of business processes, and improving products and services. Those who don't include external data in their data-first approach will lose out on the opportunity to benchmark their company and also plan for the future using predictive analysis.
Overcome the Challenges of Data Today
If you’re looking to reap the benefits of collecting data and leveraging its insights in your manufacturing company, be aware of the challenges and take actions to overcome them. Focus on taming your data, keeping it fresh, and keeping it protected, and don’t overcomplicate it. As you build your data-first strategy, you’ll soon see how it can not only help with operations today, but can set you on a successful path for the future.
Author’s bio:
Kerrie Jordan is Vice President, Product Management, Data Platform, at Epicor Software. In her role, Kerrie leads the strategic direction of Epicor’s cloud-enabled solutions to ensure they continue to deliver high-value innovation, security, and performance for Epicor customers. Based in Richmond, Virginia, she brings over a decade of experience in ERP, supply chain, eCommerce, cloud computing, and product development business solutions.
