When it comes to data-driven decision making, the big data concept is, without a doubt, in the spotlight. No wonder, since it has the potential of significantly improving business intelligence, especially if data is efficiently shared among collaborating partners. And while this is clearly a game-changer for both the tech industry and the overall business world, there is a huge potential in building processes based on operational data shared in real-time. In this article, we’re going to discuss how these two approaches differ, and how tracking different types of data in real-time can benefit your business.
Big data vs operational data – the different ways of sharing information
While operational data and big data both serve improving business processes, these data types differ in how information is shared, processed, and eventually leveraged. Big data is a concept that – thanks to the increased computing power – allowed for analyzing entire, huge datasets. Before the era of big data, the only option available was a statistical approach, which allowed analyzing small data samples that were representative of these big data sets. As a result, the quality of the insights derived through these methods were limited. Big data allows us to identify information stored in data at a much higher level of detail, previously unattainable via traditional analytics methods.
Big data analysis lets businesses get a high-level overview – so, for instance, drive decision-making by looking at joint information for 300,000 IoT devices. If all of these devices were to experience premature battery depletion, then their manufacturer could decide to use another type of battery in the future. As you can see, the aim here isn’t to create digital representations of each of those 300,000 objects to track their usage individually. That’s where operational data comes in.
While it might seem like a novelty, operational data has been around for a while. One of the more relatable examples of instant data delivery is GPS systems that tell drivers whether they can expect heavy traffic on the road ahead of them. So, it helps people understand what is currently going on in the object’s surroundings, and how this affects it (in this case, their vehicle). But the true value of real-time, operational data starts on top of GPS technology, which allows for building shared processes. Imagine a logistics chain, where a manufacturer, transport agent, and retailer can use shared operational data to synchronize their business operations and improve the efficiency of each and every delivery. GPS data is just one of the data sources essential to the process.
Another use case is how some hospitals now keep track of the efficiency & quality of their medical procedures (e.g., medical equipment). For example, how many procedures a device is delivering daily or how exploited it is. This is enabled thanks to a unique, irrefutable aggregated digital documentation for each item, including its usage, location, and staff engagement. This level of detail is unattainable if you only have access to a set of separate IT systems or can’t share operational data in real-time with external partners, responsible for maintenance and quality assurance.
The advantages of operational data sharing for business
Detecting operational issues
Unlike big data, operational data allows you to make quick, reactive measures whenever there’s an issue with a device. Say that you’re a wind farm service company, and track about 25,000 wind turbines, 24/7. What happens if one of these devices stops working properly and needs to be fixed? If you use an operational data sharing platform like Trusted Twin to track metrics for each turbine, then they would be able to spot the issue immediately and send over a maintenance team ASAP.
It’s worth adding that operational data handled within a single organization is not a problem; the true challenge starts when there are multiple organizations involved in shared data. To make data exchange possible, it’s important to coordinate real-time work among partners engaged in the same process. It’s also key to ensure that there is a real-time flow of data between these parties – think of data that relates to diagnostics, maintenance, or equipment performance reports, among others.
Opportunity for improving business processes
This one is about observing the ways different, third-parties / external ‘actors’ engaged in their work. The term ‘actor’ is quite suitable, as operational data can help orchestrate both personnel work and optimize device usage. Let’s jump back to the hospital analogy to give you a practical example.
In terms of a clinic, the human ‘actors’ are hospital staff and patients. There are also a number of connected devices, like surgical robots, heart rate monitors, and a number of supporting IT systems, etc.
If a sensor is sawn into medical staff uniforms, then you could track each personnel member’s actions throughout the day. The sensor would communicate with anchors dispersed across the building. You could then see:
- How often a doctor makes rounds per day
- How many times a day a nurse comes up to each patient (for example, by placing a receiver under each hospital bed)
- How many times each staff member uses a given device.
Although a hospital might seem like a single entity, in fact, they use dozens of different IT systems, maintained by third-party providers. Hence, it is crucial to share data and cooperate in real-time with these systems, as human life is at stake.
As you can see, real-time aggregation of operational data from engaged actors and IT systems (including the tracking system) allows to deliver reliable reporting regarding the quality of patient treatment. It also monitors whether everyone works to a standard needed for hospital safety accreditation (which might be needed to prove to authorities).
Making decisions on the spot
Nowadays, organizations get access to large volumes of data, and use advanced analytical software to draw insights from it. While this provides support in optimizations, unlike operational data, it doesn’t help them execute processes – especially those involving third parties. Considering how competitive the market is and how fast things change, building shared processes among collaborating partners empowered by reliable, operational data, proves invaluable to improving business processes and gaining a competitive advantage.
Enhancing customer service
When you ring a call center asking for help, what’s the main factor that impacts your satisfaction? I bet it’s the speed with which your issue gets resolved.
One of the leading Polish insurance companies wanted to reduce the cost of customer support and deliver a better experience by answering calls and resolving issues faster. They knew that people hate picking numbers from the dialpad multiple times and waiting to be connected to the right consultant.
Instead, they analyzed what they knew about each caller and tried to predict why they were calling. Since they were not well-versed in recommendation engines, they needed to cooperate with an external partner.
After joining forces with Trusted Twin, they were able to build a shared process with an external recommendation engine provider. By using operational, real-time data about the customer, they can now draw better conclusions regarding the reason why each customer is calling. As a result, this reduces the number of calls with consultants (i.e., saved money) and decreases the call time (better experience).
Ways of sharing information beyond big data
While having access to big data can’t be overestimated, focusing on it solely and ignoring operational, real-time data will negatively impact your business operations, as it limits your ability to make decisions on the spot.
By using an operational data sharing platform, you can:
- Coordinate actions between multiple actors engaged in a specific process in real-time
- Allow for operational data flow between different partners including diagnostic and servicing data, and performance reports.
These (and many other use cases) are possible with a data-sharing platform like Trusted Twin.