Based on a study by Forrester, organizations, which are strongly data-driven are 162% more likely to exceed their revenue goals in comparison to their less data-informed competitors.
Yet, many businesses don’t always get it right in their efforts to become a truly data-driven company. Many times, they might fall victim to human errors, or find themselves lost in inconsistent, unorganized, low-quality data which is hard to draw insights from. Luckily, if you choose the right strategy and tools, you can be sure not to follow in their footsteps.
In this piece, we discuss the benefits of sharing data and how to get your approach right.
Let’s start off by explaining what is data sharing.
What is data sharing?
In short, data sharing is about distributing the same data objects or sets between several users or applications all the while retaining their ownership and consistency. It’s important to bear in mind that there are two types of data, i.e., operational and analytical data, both of which serve a different purpose. This in turn impacts the data sharing definition. In the case of the former, sharing of data relates to data shared by multiple partners in order to build shared processes that all participants are involved in.
The main data sharing benefits
Data sharing can bring a ton of advantages for your business – provided that you know how to approach sharing of data correctly. Here are the top recommendations from our CEO. By following the practices laid out below, you can expect the following benefits:
Unlocking the full digital transformation potential
In the last decade, many companies have taken steps towards implementing digital transformation. However, as many of them continue to rely on semi-automated processes, there’s still a big journey ahead of them. What’s more, these organizations might not even be aware of how much potential they have yet to uncover if the go through the entire digital transformation process.
Sharing data via emails or as CSV files might seem like part of the digital transformation process. But when you think of it, it’s only about changing the medium while the processes remain traditional and human-based. They are still prone to delays, inconsistencies, and other issues that are commonly seen in traditional models.
Real process automation is always connected with operational data, where the “human factor” is eliminated and data is processed through IT and OT systems via APIs.
Continuous access to real-time data
Continuous access to real-time data is another data sharing benefit. How effective and optimized your processes are will depend on the access to real-time data. It plays a particularly important role when it comes to shared processes, which call for collaboration between multiple unassociated partners. Imagine there is an interruption in data flow or a delay in data exchange. This could negatively impact the entire system as decisions would be made based on obsolete data.
It’s an easy equation, just think about what the cost of negative consequences would be in comparison to the savings that uninterrupted access to real-time data potentially brings. Not only by eliminating delays but also by enabling you to react faster.
Improved processes and fewer human errors
Human errors have always been and remain a top struggle for businesses. They take place in all industries, from cyber security to manufacturing and healthcare, and cost organizations worldwide trillions of dollars in inefficiencies and lost opportunities.
Studies clearly show that, wherever manual data processing is involved in a process, the data accuracy drops to 96%.
Here’s where process automation comes in as a game-changer. It significantly reduces the probability of human mistakes. Naturally, it doesn’t eliminate the risk entirely as system errors still happen. Yet, they become less likely and, once fixed, are never repeated.
Let’s see how this could roll out in practice, and take a medical equipment maintenance company as an example. The company we’re looking at uses automation to compare operational, i.e., real-time data from medical devices to failure patterns that it’s been trained to detect (or has experienced in the past). This means that operational data is compared against historical records.
The system is trained to detect the probability of a specific failure by comparing live data to a predetermined threshold. If the patterns exceed these limits, the system will automatically generate a service or repair request for the device. This request can then alert the relevant technical team who can inspect and fix any errors quickly.
As you can see, it wouldn’t be up to a human to spot the anomaly and make the call to identify it as a failure and scale it. This is a huge benefit in an industry as dynamic as healthcare, where every second is of importance.
High data quality and verifiability
Do you know how much bad decision-making costs businesses annually? According to Harvard Business Review it’s $3.1 trillion. What’s the reason for it? Lack of data consistency and reliance on poor-quality data. By implementing proper operational data sharing technologies, you can base more and more crucial processes on actual and high-quality data.
If you use the right approach and solutions to operational data, you’ll be able to build shared processes with multiple partners contributing their own data. Each party will be able to retain full control over the data they provided. As a result, with clearly established governance and ownership in place, you’ll have certainty that the data you’re seeing and using is trusted and valid.
Increased competitiveness and revenue potential
Unfortunately, small and medium businesses hardly ever have a budget for collecting large amounts of data required for big data analysis. This results in large corporations gaining an unfair competitive advantage thanks to having access to large datasets.
Luckily, there is a way around it. If we combine a few smaller data sets owned by multiple, cooperating partners then we can create a larger data set, through synergy. Imagine a scenario where a device manufacturer shares data on device operation, a service provider shares service history, while users provide data on usage. This would give you and all the remaining participants access to substantial amounts of real-time data, without a significant financial investment.
Data shared with trust and proper ownership control not only helps improve many business areas but also boosts competitive advantage. And the great news is that sharing of operational data benefits all the cooperating partners, irrespective of what their business goals are.
More organized and consistent data
By using a trusted, versatile platform for sharing of data, companies are empowered to gather and store data in an organized and consistent manner. Even more so, by engaging in operational data sharing, businesses start building an invaluable asset that will be beneficial not only now, but in the future. How so?
While operational data is real-time in nature, over time, it becomes historical data that can be used for analytical, business intelligence purposes. Moving forward, the patterns determined in the process of analyzing data allow optimization of processes that run on operational data. So, as you can see, it’s a win-win strategy.
Collecting data for analysis is a long process, but once implemented, it allows you to outperform the competitors.
Sharing of data – how can Trusted Twin help you succeed?
As already mentioned, to reap the full benefits of sharing data (i.e., make it flexible, scalable, and verifiable), it’s essential to use the right tools and infrastructure. This is all possible if you choose Trusted Twin to support you with your data sharing objectives and needs.
Here is a short overview of what Trusted Twin offers:
- Trusted Twin acts as a data exchange layer, meaning, that it fuels processes with continuous and the most up-to-date operational data you can trust.
- Flexible integration methods which simplify the automation process for all partners. As a result, the dependencies between all partners are reduced.
- Designed for processes involving multiple partners who contribute their own data, all the while retaining control over it. More ‘partners’ means more data and more bilateral relations to handle.
- Limited risk of human errors. Trusted Twin implements an API-first approach, which helps eliminate manual, human work (such as uploading a CSV file).
- Access to full process automation, as opposed to using semi-automated systems that still rely on manual work. Instead of data upload, it circles around giving you continuous data access.
- Access to data needed for short-term and long-term goals. Trusted Twin allows you to start collecting your operational data today, so you can have high-quality analytical data tomorrow to outperform the competition. This is worth knowing, as collecting high-quality, analytical data is a long process.
Benefits of sharing data – key takeaways
While many businesses claim to have undergone full digital transformation, it’s not the case if they don’t know how to approach operational data sharing. Creating a strategy for it is one of the most important decisions you need to take on your path. The reason is that operational data sharing forces you to think about your data in an organized, strategic manner. This, in turn, can unleash many new opportunities for your business.
Benefits of sharing data include the following:
- Unlocking new business models and services
- Gaining continuous access to real-time data, which positively impacts decision making
- Democratization of data – small and medium businesses can access large data sets without significant money investment, helping them gain competitive advantage
- Fewer human errors and improved process effectiveness and efficiency.
Ultimately, to get your approach right, it’s important to choose your partner and technology carefully. Using a versatile solution like Trusted Twin will allow you to focus on your goals, and not on the limitations and capabilities of your infrastructure.