How to Effectively Implement Data Sharing Between Organizations


For many years proprietary information was so restricted that only top executives in a company had access to it all. Production orders and information flow was one-way—always from the top down. Information sharing was practically unknown.

Lower echelons commonly hoarded information. Department heads hoped to gain an edge over other departments to preserve their own importance and protect their jobs. This methodology dominated through the 19th and 20th centuries and made conducting business strikingly inefficient.

Nowadays, the most important commodity in a business is still information. The most important skill is coordinating your information sharing.

In the 21st century, multiple companies coordinate by data sharing—revealing customer usage patterns, habits, interests—and their accumulated knowledge integrates—allowing upsell and cross-sell opportunities for their partners. By using each other’s data, a partner can meet a need beyond your scope, or you can find an opportunity to fulfill a need based on partner data. Everyone benefits by collaboration, revealing new opportunities for all concerned.

Operational Data vs. Analytical Data

Operational data (aka, Real-Time Data) becomes available as soon as it is created or acquired. Operational data is always current and reflects the existing state of objects used in processes. It must—since it supports real-time processes.

Analytical data, on the other hand, is inadequate to support real-time processes; it lacks sufficiency because it is based on statistical data, and not the state of single objects. Analytical data is a collection of data that is leveraged to support organizations in processes improvement and optimization. It is mainly extrapolated from historical information (aka non-real-time data). As the name indicates, this data type is used for analysis purposes, and is usually stored in so-called ‘read-only’ databases.

Historical data lends itself to Machine Learning (ML) and Artificial Intelligence analysis, providing verifiable information to support business choices. It is one of the main pillars of Business Intelligence (BI) generation and veracity.

The primary benefit of analytical data is that you derive statistics needed to drive decision making. Even low quality data is useful if you have enough of it.

Source data of the largely unsorted variety called a Data Lake whereas data that has been cleaned and refined is a Data Warehouse. That being said, one source of analytical data can be your Operational data, generated during the implementation of many business processes, and gathered over time.

If you want to learn more about the differences between these two data types and see an example of how these two can co-function, read our dedicated article on analytical data vs. operational data

Different data = Different sharing — big data vs. operational data

Thanks to contemporary computing power, Big Data allows for analyzing massive datasets. This allows us to identify information stored in data at a much higher level of detail, previously unattainable via earlier analytical methods.

Before big data became possible with modern computing, the single option was the statistical approach, which permitted analyzing only limited data sets that were merely representative of reality. Consequently, the insights derived through this method were of limited utility.

Big data analysis provides high-level overviews. For example, decision-making could be enhanced by assessing the combined information of a hundred thousand IoT devices. If it was revealed that the majority of these devices were experiencing premature battery failure, a new more durable candidate could be selected for future versions. The objective should never be the creation of digital representations of individual objects to track their individual utility—that’s the realm of operational data.

Seemingly novel, operational data has been around for a long time. Consider the instant data delivery of GPS navigation systems which inform drivers about traffic and road conditions ahead of them. Users understand what is occurring around the object (driver’s vehicle), and how circumstances affect it.

However, the true value of this real-time operational data derives from creating shared processes on top of the GPS technology. Envision a logistics chain, where the producer, delivery agent, and ultimate retailer use their shared and combined operational data to harmonize their business operations. This results in improved efficiency during the entire process. GPS data is just a single piece of the puzzle, yet it is indispensable to the overall process.

A different use case could be drawn regarding hospitals tracking the quality and efficiency of their medical procedures. How many procedures are a given device delivering daily or how well utilized is it?

Irrefutable, aggregated digital records for each item, stating its usage, location, and the number of staff engaged makes this possible. Without shared data, this level of detail is unattainable—particularly if you access the data on disparate and disconnected systems. Typical IT systems cannot share real-time operational data with external partners responsible (for example) for quality assurance and maintenance.

If you want to learn more about this subject, then take a look at our dedicated article on different types of data.

Beneficial Data Sharing

Unlocking the full potential of digital transformation

Semi-automated processes don’t unlock the full benefit from digital transformation. Sharing data via e-mails or in CSV files may seem to be a step in the right direction, but in reality that is only about altering the medium for traditional manual processes. These are still prone to errors, inconsistencies, and delays just like the outdated models.

Genuine process automation is inevitably linked with operational data sharing, specifically where “human error” is eliminated. To be truly effective data must be handled through automated systems via APIs.

Automated processes mitigate human errors

Eliminating human errors remains a top priority for businesses. They occur in all areas of endeavor, from cyber security to manufacturing, to healthcare. These penalize organizations worldwide to the tune of trillions of dollars in losses and missed opportunities and necessitate expensive capital improvements.

Studies clearly demonstrate that wherever manual data procedures are part of a process the data precision decreases to just 96%.

Process automation is the game-changer here by virtually eliminating the likelihood of human mistakes. Systems may still be prone to processing errors but they become more reliable over time and, once fixed, are never repeated.

Superior data quality and validation

Bad decision-making costs businesses $3.1 trillion annually, according to Harvard Business Review. This is attributable to data inconsistency and businesses relying on second-rate data. The implementation of proper operational data sharing tech makes beneficial decisions based on accurate, current, high-grade data.

The right approach to operational data will build shared processes and solutions with multiple partners who each will add their own data. Individual contributors will still retain full control over their shared data. Consequently, with established governance and clear ownership in place, veracity of the data you are seeing is assured.

These are just a few benefits related to data sharing across organizations. If you want to learn what the remaining data sharing benefits are, check out our guide.

Best Practices for Data Sharing

Operational and Analytical data have different purposes, requiring different approaches in your data sharing policy. Operational data must reflect the specificity of real-time data, and its use cases, for sharing amongst cooperating partners.

Best Practices:

Don’t give your data to others. “Sharing data” and “giving data” are completely different. Transferring control over your operational data to third parties is a risky decision. Your data is valuable and you should know how it is used by others, at all times.

Setting appropriate access rules solves this problem before it arises. You can create predefined permissions or expressions for users. You can thus have your cake and eat it, too—providing data access only to those who need it, while retaining control of what happens to it. That flexible methodology to sharing data is empowered by the Trusted Twin platform.

The “structure of your data” POV is overused. Approach it from the “shared process” perspective, defining the objects available for use in shared processes and aggregate operational data (contributed to by all the partners). These objects should be common for every partner. Data structures used in partners’ IT systems can vary significantly, so we ignore them.

Be flexible with your integration strategy and the technology you use. While your shared business objects will generally remain the same, data sources can change; your system must be capable of evolving to accommodate the new data streams.​​ 

As your business evolves, you may have to switch from one data source to another—and that could require a markedly different integration strategy. Fixed integration strategies could cripple your ability to enhance processes.

These were just three of the 11 practices we’ve discussed in our article on sharing of data – be sure to give it a read.

Handling Multiple Integrations Concurrent with a Growing Customer Base

Selecting the proper platform for operational data sharing

Different organizations can have customized IT systems. SME-targeted systems like SAP offer near-infinite customization, and users take advantage of it. Most IT companies offering similar products rely on integrations of the operational data and they struggle with the complexity. By subscribing to an operational data sharing platform like Trusted Twin, tackling these issues is already built-in, making the evolution much easier.

How does this happen? The Trusted Twin platform encourages users to detach the product and its growth from the integrations. This is accomplished by introducing a distinct layer that defines common business objects (models) and then offers several integration tools and useful technologies for disparate systems.

Keep integrations separate from core systems development 

By separating integrations from the core development of your system, you retain agility and competitive advantage. A platform like Trusted Twin simultaneously can increase your system security. It becomes a layer separating your system from Internet-derived cybersecurity threats, including those posted accidentally or purposefully by your customers, clients, or users.

Emphasize your core product; subcontract the rest 

Dedicated infrastructure development is both time-consuming and costly. In 2020, 40% of global IT expenditures were earmarked for integration services. You should ask yourself, however, “Why reinvent the wheel?

A ready-made solution already exists that will save you time and expense while handling everything necessary to create and update your custom integrations. This avoids needless expenditures, supports business growth, and lets you focus on developing your core product. You also eliminate time and money wasted on maintenance and upgrades while your customer base grows.

Creating bespoke solutions is valuable only at very large scales. That is why it is well worth considering buying a solution (rather than engineering it). You can significantly speed up the system development time to deploy your IT product to your growing customer base. Develop your core product and avoid getting trapped into building, applying, and preserving custom integrations—especially as your customer base needs your attention as it rapidly grows.

To learn more about sharing information and handling multiple integrations while growing your customer base, take a look at our article.

Managing large IoT-generated data sets

Cheap IoT devices abound, including sensors, software, Bluetooth, NFC, and more; data utilization is truly limitless. Managing large amounts of data flawlessly is vital, so knowing the best option requires you to take some aspects into account:

High Data Volume

Ideally, software should have the capability to derive insights from unlimited numbers of IoT devices through cutting-edge analysis, plus the ability to assess processes on an individual unit basis. These steps drive that process:

  1. Collecting IoT Data — gather facts
  2. Systematic Analysis — evaluating data
  3. Drawing Conclusions
  4. Implementation while the process now runs continuously, or as needed, to generate a stream of data
  5. The Execution Phase where all gathered information is used for a purpose.

And remember, analytical data is a great way to derive insights, while operational data that’s vital to execute processes.

Storage and Data Structure

Operational data is essential for flexibility, giving the ability to arrive at precise conclusions and enhancing the ability to make good on-the-spot decisions. Operational data is always up to date, so it can be used for the ongoing automated process execution or empower analysts so they do not have to rely on historical statistics that may not be entirely trustworthy. Data normalization helps to remove anomalies in a database to render more accurate analytics.

To effectively handle IoT data, it is essential to:

  • Stay connected to real-time operational data
  • Highlight security, by leveraging security protocols. You require reliable security standards such as ISO27001 and SOC2 for proper protection
  • Demand uptime because even though managing IoT data from or for innumerable devices means synchronizing and delivering it, that might not be the issue. Instead, connecting with millions of devices in different time zones can’t happen, if the system isn’t readily unavailable.

To learn more about the recommended approach, make sure to read our dedicated piece on IoT data collection and analysis.

Typical Hurdles to Digital Transformation

Resisting access to data 

Historically, most officialdoms have restricted access to their data. By discouraging data sharing, they also nurture existing silos and data hoarding. This is driven by the dread of data breaches; this approach prevents companies from manifesting their full potential.

As Gartner so aptly puts it, the “don’t share data unless” approach should be substituted with a “must share data unless” idea. All it requires is an adequate access & security protocol to turn this into a reality

This comes with many benefits. Your team members will have access to reliable data whenever the need arises. They’ll achieve agility, make well-informed decisions, and end up getting maximum benefit from your digital makeover.

Data isolation 

Businesses use diverse legacy systems, making it tough to recognize the same objects—particularly when using isolated data not shared elsewhere. Consequently, a lot of businesses fail to gather enough distinctive information about a lone object when disparate systems use dissimilar identities, making equivalency difficult.

Managing different identities becomes even more vital when cooperating with external partners in a larger ecosystem. Connecting and aggregating distributed knowledge of a single object, from multiple dissimilar data sets, with different owners working together, can create a larger and more useful dataset through synergy. All partners in a common process benefit from this agglomeration. However, it doesn’t work without accurate identity matching.

The data isolation issue is easily managed with the Trusted Twin platform. It delivers the system for aggregating related information from dissimilar systems by translating custom identities into common identities. Trusted Twin provides a setting where all-inclusive and accumulated business-relevant knowledge is available for all of your digital objects and processes.

Data quality varies depending on the legacy system used

In an Experian study, 68% of businesses surveyed agreed that low quality data impacts digital transformation implementations. Data quality refers to “the overall utility of a dataset(s) as a function of its ability to be easily processed and analyzed for other uses”. Since low quality data is subject to variation, it can deteriorate. This is especially true when there is no definitive source of truth due to divergent or inaccurate sources like antedated legacy systems.

Operational data is aggregated from different systems in the form of “standardized shared objects”. For these you can implement verification mechanisms to assure high data quality. When working on “shared objects” data sources can change, significantly improving data quality, while keeping the objects unchanged.

After determining the structure of the shared object, each partner determines how to provide the most reliable data. They can also implement techniques driving automatic verification through parallel resources.

In a dedicated piece, we discuss the remaining obstacles to digital transformation, including lack of buy-in and data governance. Be sure to give it a read.

Selecting the ideal sharing platform

When seeking the ultimate platform for operational data sharing, think through your goals. Ask yourself:

1)   Why do I want to share the data with other participants & organizations?

2)   How much data do we have to handle?

Setting your priorities is important as you search for the ideal solution. These suggestions relate to joint processes, based on Real Time, Operational data. The elements and techniques you’ll read below do not relate to Analysis and Optimization processes.

Here are a few recommended ideas required in platforms for operational data:

Security of Data 

When you’re sharing a database, moving data to the cloud, and must consider the data’s value (and any associated risks of it being taken over by unauthorized persons), security is the single most important feature.

Data sharing is out of the question without reliable security. Even when the data is low-value and not particularly sensitive, manipulation or acquisition by rivals can be very detrimental to your company. Choosing the Trusted Twin solution guarantees an ISO27001 & SOC-2 certified infrastructure. Put your mind at ease with this platform; your data is secure.

Verifiable Data

Knowing the source of data and assuring its integrity is penultimate after security and accuracy. You cannot make decisions based on weak or inaccurate data. That will have serious repercussions if you fail to eliminate guesswork.

Where trustworthiness is not established between people sharing data, it is essential to implement tools enabling each party to verify data origin, integrity, and authenticity.

Collaboration Capabilities

Collaboration is of utmost importance when it comes to solutions for operational data sharing. The technology you select will permit you to create and use objects cooperatively with partners in real-time.

If you run a wind farm company, logically, all maintenance companies should have access to complete information on each of the turbines. This also provides the means whereby they can contribute their individual data to ensure continuity and enhance quality.

For a full list of the features worth crossing off your checklist, refer to our article on choosing an operational data sharing platform.

Data as the most powerful resource

Data remains the most important commodity in a business. The most important skill is coordinating your data sharing. Hoarded data results in limited information, and may go entirely unused benefiting neither the owner nor business partners/associates. Ultimately, everyone benefits from collaboration, revealing new opportunities for all concerned. Customers receive better service, more reliable products or information, and you obtain customer loyalty and support, as well as access to insights from partners that would otherwise be unavailable.

Operational Data and Analytical Data are discrete pieces of information and must be treated differently to maintain data integrity and data security. They can and should be blended for information and process optimization purposes—yet they are distinct and complementary—providing significantly different data.

It is not a question as to whether data should be shared—it should. The real question is do you want to spend millions developing a system in-house with all its associated costs and permanent maintenance requirements? It would be smarter and more efficient to find a platform that has already done the work and makes a business out of maintaining the infrastructure, making your business more savvy, intelligent, and profitable.

We’d like to suggest that the answer is Trusted Twin.

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