5 Common Data-Sharing Challenges and How to Overcome Them

Whether you use it to guide business-critical decisions or as part of a collaborative research project, data sharing is an essential practice for any flourishing business. Nevertheless, while its importance is undeniable, data sharing does come with its fair share of significant challenges.

There are five main concerns that businesses encounter with data sharing. We will explore what they are and the best solutions for overcoming them. But first, let’s cover the basics. 

What is data sharing?

Data sharing is the process of sharing datasets internally with authorized people, departments, or externally with other organizations — but its uses and implications vary. Take, for example, a company that uses the best auto dialer software for their outbound calls. This tool works even better when integrated with the company’s data-sharing infrastructure. 

Agents on the phone can then be provided with real-time data on the customer they’re speaking to. Information like their purchase history, appointment schedule, or compliance information can all be made readily available in this way. 

This is just one instance of data sharing, but there are many ways in which data dissemination can support decision-making, generate new ideas, and solve problems. A culture of open data sharing creates a transparent workplace where everyone works together towards common goals.

5 Data sharing challenges and how to overcome them

If you’re already deeply involved with data sharing, then the chances are you’ve encountered some challenges. Read on if you’re wondering how to handle them and get the best out of your data. 

1. Privacy concerns

You need your data to be accessible, but this availability automatically presents concerns around customer data protection and privacy rights. Depending on what geographic markets your company sells products and services within, you are almost certainly governed by data protection regulations like the California Consumer Privacy Act (CCPA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe. 

Non-compliance with these laws can bring hefty financial penalties, not to mention irreparable damage to your company's reputation. Therefore, it is paramount that data privacy is a top priority to address in your data-sharing practices. 

Mitigating privacy concerns

Documentation

To create a successful foundation for data sharing, you must know and document your data. Typically, this foundational step of a data privacy program is called data mapping. It incorporates documentation of your data assets, i.e., what systems in your organization contain personal data, what kind of personal data they hold, and much more. Also, when you capture the records of processing activities (ROPA), as noted per Article 30 of GDPR, you will understand what your organization is doing with the personal data you have collected, processed, retained, and perhaps disclosed. Mapping the data will help you see any existing data-sharing risks and consider how you may reduce other risks as your organization moves forward.

Data Sharing Agreements (DSAs)

Ensure that your organization incorporates data processing agreements (DPAs) into your agreements when your organization shares personal data with a third party. In addition to conducting appropriate initial and ongoing due diligence about a third party, it’s vital to incorporate DPAs to ensure that all data processing aligns entirely with applicable data protection laws and regulations, given the sharing of personal data. The DPA will detail the what and why of the personal data that will be shared along with the technical and organizational measures (TOM) to protect the personal data, among other details of the relationship. These agreements are essential to the baseline set of protections in place when personal data must be shared with a third party. 

Data minimization

Rather than indiscriminately sharing all available personal data, only share the information that is absolutely necessary for recipients to achieve their purpose. For example, if an individual does not provide food for attendees at an event, then this individual would not need to receive information about attendees’ dietary restrictions. Consider data minimization before data sharing occurs so that the organization’s risk is reduced, whether the data sharing is internal or external to your organization.  

2. Security risks

As data accessibility increases, so does the risk of unauthorized access, hacking, and insider breaches. Safeguarding sensitive information becomes requisite to avert data breaches and protect against cyber threats. 

This is expected to become even more critical in the coming years, as the future use of AI will likely bring significant advancements and higher security risks. As such, your dating-sharing framework must have iron-clad defenses against potential attacks. 

Protecting against security risks

Data Aggregation

Data aggregation is an excellent technique for both internal and external data sharing when the value of the data can be grouped to avoid exposing sensitive information. Take, for example, 911 calls where the caller's information must remain private. Still, the data can be aggregated by call type, date, and other categories to be highly beneficial for research.

Data Anonymization

Data anonymization is the process of protecting private information by scrambling the contents with different characters so that the item is no longer recognizable. When data has more value at a detailed level to be shared, organizations can use this technique, especially when sharing data externally.

Access controls

To successfully address security risks, use access controls liberally. They serve as a digital barrier, permitting only authorized personnel to access specific datasets. This can be achieved by establishing distinct user roles with varying access levels, employing secure authentication methods like passwords, vigilantly monitoring data access, reevaluating the need for users’ particular roles and permissions regularly, and removing or reducing roles and permissions when users’ no longer have a need to know.

Data-sharing platforms

Leverage your system settings extensively! Systems that enable data sharing may have many built-in access controls, including capabilities such as sharing rules, limited roles and permissions, and more. They are virtual strongholds, ensuring that only authorized personnel with a legitimate need can access and interact with relevant data. Also, stay on top of changes and updates from your third parties to maximize the value of your settings and continue limiting data sharing as appropriate. Using these capabilities effectively reduces security risks associated with data-sharing practices.

3. Accountability and compliance

Adhering to data compliance regulations is a non-negotiable responsibility for every business, but this can be a complex and constantly evolving task. Plus, you must also be able to clearly demonstrate what measures you’ve taken to abide by the rules. 

With the right tools and practices, it can be easier to effectively showcase your adherence to accountability principles, potentially exposing yourself to legal and reputational risks.

Ensuring full compliance

Modern data catalogs

Catalogs are equipped with advanced features like data lineage and query history, which allow organizations to carefully track the origin, movement, and usage of data throughout its entire lifecycle. For example, if you have a cloud call center solution that manages data from all communication channels, you could use a modern data catalog to organize all the information and offer clear insight into managing your communication data.

• Data lineage is a powerful tool that offers crystal-clear transparency into your data processes. By enabling the tracing of data back to its source, it provides a comprehensive view of the data's journey. This includes insights into the original source of the data, who accessed it, when, and how it was used. This level of detail is invaluable for demonstrating compliance with data protection regulations. For example, data lineage information can be used to identify whether specific personal data is in or out of scope for particular regulations, such as the Gramm-Leach-Bliley Act (GLBA). It can also show that an organization has corrected data per a user’s data subject request. Data lineage can also be very useful in the unfortunate case of a data breach. By examining data lineage, you can determine what data flows may need to be stopped or reduced to minimize further impacts of the breach. 

• Query history acts as a digital paper trail, meticulously recording every execution on the data. This includes need-to-know information, such as who initiated the query and when it occurred. By preserving this comprehensive record, you can establish an unambiguous history of data interactions. In addition, some of the modern cloud databases can also track policy control conformance, adding to better auditability.

4. Documentation and data context

Data is nothing without proper context and detailed documentation. Say, for example, you receive a report highlighting an increase in web traffic on a specific day, but the report doesn’t give any background information as to why. 

This information is only valuable with the proper context via extra documentation. In some cases, this might include a business memo that explains the marketing campaign launched on that particular day. Data can be misinterpreted or misused without this context, undermining its value and purpose. 

Providing more context

Strengthen documentation practices

Enrich your data with plenty of contextual information, clearly outlining each data field's purpose and permissible use. Take, for instance, a column labeled “transaction amount” in a financial dataset. A description indicates that the data is the purchase amount and specifies it is only for billing and financial reporting purposes. 

Accessibility

Documentation should be in a user-friendly data catalog that helps to enhance the context and is easy to navigate for all authorized users. When information is readily accessible in a format that aligns with their established workflows, it allows employees to make the most effective use of the shared data. Utilizing a data catalog contributes to the overall success of data-sharing goals.

5. Data quality assurance

Maintaining data quality throughout sharing procedures is another tricky challenge. When the standard of data is poor, it can have wide-reaching implications. Take, for instance, an outbound power dialer that relies on shared contact data. If the data is inaccurate or outdated, it can result in wasted time and a poor use of resources. 

Getting the most value out of your data-sharing practices relies on that data being of good quality. Confidence in your data's integrity makes it easier to make well-informed decisions and achieve data-sharing objectives. 

Preserving data quality

Data governance

Airtight practices that incorporate regular audits and validations uphold the highest data quality standards. Robust data governance procedures safeguard against unreliable and inaccurate data, so you can be confident that you are maintaining consistency in quality. 

Quality checks

Keep your data in good order with stringent checks using data quality metrics and validation procedures — before sharing occurs. Scrutinizing data at the start ensures you avoid potential quality issues, so any discrepancies are addressed before they can cause any problems. 

Data standardization

When there is inconsistency in data formatting and structuring across platforms and systems, it can cause mistakes and issues. Shared data may be subject to misunderstanding or may even be inaccessible. Ensure data format and structure are standardized to avoid errors and hold-ups.

Make the most of your data

There are many concerns to be considered when sharing data, and if not handled correctly, they can hinder many areas of your business operations. But when you encounter the issues discussed in this article, you now know that there are multiple solutions to alleviate your woes. 

Data sharing may be complex, with so many difficulties inherent around security and reliability, but if you can address these issues directly, the value you extract can be immense. Being proactive can pay huge dividends, so take measures before you find yourself up against any real problems. 

When your data-sharing practices are thoroughly fortified, you can enjoy all its benefits for informed decision-making and exciting business innovation.