Like many manufacturers, there’s a good chance you have loads of raw data spread throughout your business. But that data isn’t useful in its raw state.
To tap into its value, you need tools that can transform raw data into actionable insights about everything from your supply chain to your production line. In other words, you need data products.
A data product taps into your raw data to help you achieve specific business outcomes. Just like your smartphone’s weather app uses data from various sources to give you an hour-by-hour forecast, an industrial data product might use IoT data to predict equipment failures throughout your facility – and help you avoid costly downtime in the process.
Data products are absolutely crucial for any manufacturer that wants to become data driven. But don’t worry if you can’t quite wrap your head around the specifics. In this article, we’ll explain everything you need to know about data products and how they create value for industrial orgs.
The 4 core components of a data product
To understand what a data product is, it’s important to break down its core components. Each element plays a key role in transforming raw data into a valuable business asset.
1. Data sources
As you might have guessed from the name, the foundation of every data product is the data itself. Where that data comes from can vary: think IoT devices, ERP systems, third parties, APIs, metadata, and more.
These data sources provide the raw data (often stored in data lakes or data catalogs) that a data product will use to provide helpful knowledge about your operation. For example, IoT sensors can provide real-time metrics into a robotic arm’s wear and tear. The resulting dataset functions as a crucial data asset that feeds into your data product – say, to predict maintenance needs.
2. Data processing
Once data is gathered, it needs to be processed before it can be useful. This step involves cleansing, integrating, and transforming the raw data.
Here, data management comes into play: it’s important to build data pipelines and implement access controls to maximize data quality and security. This way, your data product can deliver reliable and actionable insights.
3. Analytics and modeling
After processing, the data is ready for analysis. Your data science and data engineering teams can work together to build machine learning models and algorithms that support this process. These data models will make it easier for data teams to forecast facility trends or identify operational issues before they occur.
4. User interface
Whether they’re frontline workers or back-office analysts, your end users will interact with your data product via the user interface. This might look like a 3D model that visualizes a factory floor in real time (say, in the form of a digital twin). Or a dashboard that displays performance data for every piece of packaging equipment.
The user experience is critical here: great design makes it easy for end users (whom you might even call “data consumers”) to parse and act on the insights provided.
3 types of data products: descriptive, predictive, and prescriptive
Not all data products are created equal. The three main types – descriptive, predictive, and prescriptive – each cater to different business needs. Here’s a detailed breakdown.
1. Descriptive analytics products
Descriptive analytics products focus on understanding past and current operations. They help organizations make sense of what has happened by analyzing historical data.
One real-world use case is NUcore, a descriptive analytics product that we developed for Northwestern University’s research facilities. Among other things, this product functions as a comprehensive data repository that makes it easy to report on research instrument utilization. This saves staff manual effort and yields huge cost savings over time.
2. Predictive analytics products
Predictive analytics products take things a step further by using data to forecast future trends. These products rely on machine learning models and data engineering to anticipate what’s likely to happen next.
In an industrial context, predictive analytics products might forecast demand for materials or predict when equipment is likely to fail. This empowers businesses to make data-driven decisions that improve efficiency, boost equipment functionality, and reduce costs.
3. Prescriptive analytics products
Prescriptive analytics products don’t just predict future trends; they also recommend specific actions to take.
For example, a prescriptive analytics product might analyze supply chain data and recommend changes to optimize fulfillment promises. This can be a powerful tool to both mitigate operational costs and improve customer satisfaction.
The value of data products in industrial organizations
Used right, data products can help you boost your operational efficiency, transform your team’s decision making, and even enhance customer satisfaction. Here’s how.
1. Operational efficiency
When it comes to industrial data products, operational efficiency is one of the biggest benefits. They can simplify traditionally manual processes, extend the life of your equipment, and help you pinpoint bottlenecks throughout your facility.
One real-world use case? We worked with Dickson to build a mobile app for their environmental monitoring solutions. The app connects to wifi or Bluetooth-connected data loggers, collecting 24 / 7 data about an industrial facility’s temperature, humidity, etc. If a specific environment (like a section of a warehouse or a room full of refrigerators) goes out of a preset range, users will get an alert via push notification.
How does this improve operational efficiency? For starters, workers can make more proactive decisions based on environmental changes. They can perform an in-person check, order emergency maintenance, or relocate sensitive products entirely. This helps manufacturers prevent product spoilage and preserve equipment health. The impact: fewer wasted products, longer-lasting machinery, and smooth-running operations.
2. Informed decision making
By putting data-backed insights at your team’s fingertips, data products can empower end users throughout your operation to make more informed business decisions. For instance, a data product that monitors your production line can alert product managers to potential issues before they escalate and suggest helpful solutions. This way, they have the information they need to correct problems with minimal guesswork.
With the right internal research, you can also use modern data products to inform decision-making about, say, a new product you want to bring to market. We helped a biopharma company build such a tool for a team of drug researchers. The resulting data product – a self-service search engine of sorts – empowered end users to…
Mine over 100 datasets (including clinical trial data) for scientific use.
Identify over 1 billion drug relationships using an advanced knowledge graph.
Leverage more than 30 dashboards to drive down R&D costs.
The overall outcome: new drugs discovered in a fraction of the time.
3. Tailored products and services
Data products also empower businesses to tailor their offerings based on customer needs and preferences. By analyzing customer data, businesses can stay on top of trends and adjust their products or services accordingly.
For example, if your company has an ecommerce front end, you might use a data product to personalize product recommendations or suggest ways to adjust the presentation on each product page. This way, you can enhance the customer experience and boost sales.
The five-stage life cycle of a data product
If you’re intrigued by data products and want to build one of your own, make sure you understand the typical product life cycle. Let’s take a look at the five primary stages.
1. Design
The design phase is where you define which business problem you want to solve, who you want to reach with a data product, and what data platform you’ll source your data from. Here, it’s important to identify the needs of your end users, along with any business stakeholders – and tailor your product design accordingly. Design thinking principles can help. Ultimately, your users’ specific needs should make up the foundation of your data product strategy.
2. Development
During the development phase, you’ll actually build your data product. Engineers and data scientists will process any raw data, build data pipelines, and develop machine learning models. Designers will also build out the user interface.
Here, you’ll want to take an iterative approach to product development. This will help you maximize user satisfaction without incurring massive costs. One crucial step: continuously test prototypes with end users and gather feedback to ensure you’re meeting their needs.
3. Deployment
Once the data product is developed, you’re ready to deploy it within your organization. Take the time to…
Integrate your product with your internal systems.
Train employees so they can use your product effectively.
Monitor your rollout for hiccups so you can make adjustments if needed.
A successful deployment can go a long way toward maximizing adoption and user trust.
4. Maintenance
The life of a data product doesn’t end with deployment. You’ll need to have a plan for ongoing maintenance and regular updates. Maybe new and useful data will burst onto the scene. Or new regulations change your end users’ core needs. No matter the situation, make sure you have a strategy to keep your data product relevant.
5. Retirement
Even the best of data products will reach the end of their useful life, whether that’s due to the rise of new technology or a core strategic pivot. When it’s time to retire your data product, take care to preserve any useful functionality and transition your data to whatever comes next.
Data product implementation challenges
We’ve talked a lot about the value of building data products. But like any product, there are challenges when it comes to implementation.
For starters, it can be difficult to maintain high-quality, well-integrated data. But the cost of failure is steep: you’re likely to get unreliable or fragmented insights that aren’t worth your initial investment. Our recommendation? To avoid this outcome, focus on developing robust data management and data governance practices. They’re key to a strong data product roadmap.
Another challenge to consider: how to keep your data product relevant as business needs evolve. We’ve touched on a mitigation strategy already – regular maintenance can go a long way here. But more than that, it’s important to continuously seek out new data sources and data product features. This mindset can keep your data product – and, by extension, your org – at the edge of Industry 4.0 innovation.
Make data your competitive edge
Data products can dramatically transform your manufacturing operations. But more and more industrial orgs are clued in to this technology ecosystem – and they’re rapidly embracing the digital transformation needed to tap in.
To stay ahead, lay the groundwork now to make data your competitive edge. This way, you’ll be better equipped to navigate today’s challenges and prepare for tomorrow’s.
If you’d like a partner to help you chart a path forward, let’s talk. We’d love to help you realize your data product ambitions.
