How Data as a Service is Transforming the Supply Chain

In an increasingly digital world, the benefits of data-driven decision-making and embracing Data as a Service (DaaS) in business are becoming more and more apparent. Gartner research showed that 42% of sales leaders found their sales analytics return on investment was “significantly higher than expected,” supporting the idea that businesses that embrace the Cloud and leverage big data will hold a leading edge in the market.

But what does it mean to truly take a data-first approach in your business, and why is it so important today? Here are some helpful definitions and emerging opportunities:

What is Data as a Service (DaaS)?

Epicor defines Data as a Service as a business model where machine-readable data is exchanged for something of value. Data is stored in the cloud in both data lakes and data warehouses, and these data repositories enable invaluable functionalities and insights including:

• Business Intelligence and Analytics that discover and transform data so it can inform business decisions.
• Chatbots and tools like OpenAI that explain data or the resulting visualizations in plain language for the everyday business user.
• Machine learning and artificial intelligence (AI) that make predictions based on data.
• eCommerce websites and marketplaces that allow users to exchange data as transactions, driving new revenue streams for businesses.

Why is Leveraging Data Important in Supply Chain industries Today?

It's no secret that the supply chain—along with the rest of the world—experienced unprecedented challenges during the pandemic. Utilizing Data as a Service can help smooth out those big-picture and day-to-day disruptions that have hindered operations over the last few years by offering two major opportunities: increased efficiency and new ways to drive revenue. 

Increased Efficiency

Within the DaaS business model, automation takes your data's value to the next level, automating the flow of data to streamline systems and processes. In addition to reducing time spent on tedious tasks, automation empowers your workforce to prioritize crafting excellent customer experiences—so they can deliver the right part in the right place at the right time, every time. 

Some examples of automation-supported functionality we’re seeing grow in popularity include:

• Robotic Process Automation: A form of business process automation that makes it easy to define a set of instructions for robots to perform tasks.
• Workflow Automation: Integrating your business applications to automatically set off action in one system based on an event in another.
• AP Automation: Using machine learning and content services to digitize invoices, read, route, track, and ultimately pay suppliers.
• Predictive Inventory Assistance: Using data to predict inventory needs and keep shelves stocked, reducing obsolescence and improving customer experiences.
• Predictive Maintenance: Using data to predict future maintenance needs based on current product searches within the warehouse.
• Omnichannel Experiences: Uniting data from all channels of the buying and selling journey to reduce purchase friction for B2B and B2C businesses.

Customers are no longer loyal to brands—they are loyal to great experiences. By leaning into digital transformation and incorporating DaaS into your business, you can help ensure you’re prioritizing fast, seamless experiences for your customers.

Creating New Revenue Streams

The data you collect and analyze every day is your market intelligence—your unique collection of product, customer, and pricing analytics—and an invaluable asset you can use to create new revenue streams for your business. 

By investing in a business solution like Epicor to help aggregate, normalize, and visualize your data in user-friendly way, your internal data becomes an externally desirable, viable resource. 

What does this look like in practice? Here's an example:

Epicor Parts Network is one of the world’s largest automotive aftermarket eCommerce networks, allowing buyers and sellers to share real-time parts inventory and pricing information. It contains 50 years of data, it catalogs more than 17 million unique parts and processes, and it processes 700 transactions per minute every day. When you take an automobile to a mechanic, it’s likely the dealer is part of Epicor Parts Network. The network quickly determines the part needed for the vehicle make, model, and year, whether it’s in stock or available at alternate locations, and gets the car back on the road as fast as possible. That’s Data as a Service in action today.

How To Begin Your Data Transformation

With all the possibilities data has to offer, it can be hard to know where to begin when committing to this new approach. Here are some best practices to keep in mind during your transition:

1. First, get a handle on what data you have. Many companies don’t know what data they're capturing or where it is stored. To get started, organize and consolidate your information and understand where it's coming from before digging for insights. To make this project less overwhelming, you can gather data in phases and collect it department by department, with executive sponsors helping to lead the effort.
2. Make certain your data is protected: Companies must protect data privacy and security all day, every day. Help ensure you’re prioritizing privacy and security and adhering to compliance regulations while making data accessible. If you are unsure of what government regulations apply, seek legal counsel.
3. Develop a solid plan for data governance and maintenance: Good data hygiene comes from good data governance. Make certain the data you’re using to inform important business decisions is accurate and up to date.
4. Stick to your strategy: It’s easy to over-complicate the data deep-dive process. Data overload is real. To stay focused, paint a clear picture of what success looks like. How should progress be measured? Pick three to five key performance indicators (KPIs) to guide data-collection decisions. Baseline metrics could include production volume, downtime, inventory costs, and production capacity.
5. Make your data-first strategy a people-first strategy instead: Change management is challenging. As with any other transformative effort, pay attention to your team and address their concerns early and often. Get buy-in by forming a team to champion the new approach. Managing culture change successfully is a key part of becoming a data-first company.

The way the supply chain operates has changed dramatically—and most likely indefinitely. When you engage Data as a Service, you can quickly garner a deep understanding of your business and make informed decisions on where to invest time and resources—helping ensure that you stay agile and resilient in any climate.