Data sharing is essential to digital transformation. Enterprises cannot maximize their data’s potential if it remains locked behind organizational, technological, and regional boundaries. By contrast, companies that break down those barriers make better use of their data resources. Gartner’s Chief Data Officer Survey found that data-sharing enterprises made their data and analytics teams nearly twice as effective at showing value to business leaders.
This guide will introduce the concept of data sharing, its benefits and challenges, and how sharing can make data analytics more productive.
What is an example of data sharing?
Consider what happens when a consumer interacts with a traditional retailer’s website, stores, service organization, and social media. Each interaction is recorded in different storage systems that other channels cannot access. The customer believes they are dealing with one company, yet the company sees multiple customers.
Once organizational barriers fall, the company can build a better relationship with its customers and fully understand each customer’s journey. Marketing campaigns become more consistent. Sales teams maximize their productivity by reaching out to people at the right time with the right offer.
What is the difference between data sharing and data transfer?
Data sharing focuses on how people or apps consume data. They need access to common datasets to collaborate, optimize performance, and make data-driven decisions.
Data transfer is the technical means by which data teams move data from one location or system to another.
Although modern systems make it less necessary, sharing has often depended on large-scale data transfers to move information from its source to data repositories suitable for use by analysts.
Why is data sharing important?
In today’s competitive environment, businesses can’t afford to let their valuable data assets lie dormant. Enterprises make sharing a key performance indicator because they recognize data’s contribution to long-term success.
3 Benefits of data sharing
Data sharing enhances workplace performance
Barriers between business units and with external partners fall. Different teams working with the same data generate actionable insights faster when they spend less time reaching consensus.
Data sharing unlocks insights
Increasing the quantity and variety of data available for analysis raises the value of data analytics teams. With more robust data resources, analysts contribute better insights into questions about customer behavior, business performance, and more.
Data sharing optimizes efficiency
With data no longer isolated behind organizational boundaries, business domains can eliminate redundant infrastructure. For example, the company can rationalize its cloud data ecosystem by consolidating providers.
3 Challenges to data sharing
Although the reasons for expanding data access are clear, internal and external forces combine to limit these opportunities. Enterprises must overcome three challenges before they can unlock data-sharing benefits.
1. Data silos
A company’s operational structure dictates the structure of its information architecture. Without strong data governance systems, an enterprise’s manufacturing facilities, field offices, distribution centers, and overseas subsidiaries create data silos when they deploy systems to meet their narrow needs.
Tying these assets together takes enormous effort and resources. Engineers must develop ETL pipelines to integrate each type of data asset. Adding to data teams’ burdens, pipelines developed for previous use cases can’t be reused to support new projects.
Data-sharing initiatives must find ways to eliminate data silos. A common approach has been to invest in additional storage infrastructure to move data from business domains into centralized data lakes and warehouses.
2. Security and regulations
Concerns about regulatory compliance also get in the way of data sharing. Companies that handle consumer data must navigate a complex web of data protection laws. These regulations require controls that limit access to personally identifiable information.
Further complicating matters, privacy regulations vary within and between nations. For example, the European Union’s data sovereignty rules prevent companies from transferring protected data beyond the EU’s borders.
Data security is also a concern for unregulated yet still sensitive data. Preserving the confidentiality of customer data, for example, is good business practice that inspires trust and requires controls to prevent unauthorized access.
Enterprises must develop secure data-sharing practices that minimize security risks and comply with all applicable regulations while broadening access to customer data wherever it is.
3. Vendor lock-in
Many data silos are the result of information technology’s constant evolution. Migrating to cloud-based SaaS providers does not eliminate every legacy system, leaving data in proprietary applications that may lack APIs to interact with other systems. Even with modern cloud platforms, high transfer fees and unique SQL implementations raise switching costs.
What are 5 types of data sharing?
Here are five common ways companies use data sharing to improve their operations.
1. Data-driven decision-making
Data sharing is essential to creating a data-driven organization, making data architectures more efficient and streamlining the decision-making process.
Organizational barriers and data silos result in excessive data collection as teams in various parts of the organization collect the same data independently. In a data-sharing culture, every business domain makes its datasets accessible to the entire organization, significantly reducing this waste of time and resources.
Better decisions get made faster in a data-sharing culture. Business intelligence analysts produce more robust, holistic analyses with access to every data source. Dashboards can be updated in near real-time. And decision-makers at every level can take action based on accurate and timely information.
2. Personalized customer experiences
Businesses with a 360-degree view of their customers can better monetize their customer relationships through personalized experiences. Modeling customer behavior based on cross-channel data makes promotional offers more relevant to each customer’s journey, increases engagement, and drives revenue.
3. Data integration
Adopting data governance practices based on data sharing introduces consistency across the organization’s many data sources. Engineers spend less time developing data collection and data processing pipelines. Cycle times between data request and delivery drop significantly when data sources meet common standards for data quality.
4. Risk management and detection
Open data cultures streamline data collection at each stage of the risk management process. Risk assessments evaluate the company’s risk exposure more completely. Monitoring systems can integrate data in real time to accelerate incident detection. Response teams can quickly gather the information they need to mitigate an incident and minimize its impact.
5. Research and development
Data sharing first emerged in academic settings where scientists included access to research data with their peer-reviewed studies. Making data accessible allows other scientists to evaluate results and makes the scientific process more robust.
Within the enterprise, data-sharing fosters a similar culture of collaboration. Giving data science teams larger, more complete data sets results in more innovative projects based on machine learning and artificial intelligence. Afterall, if you don’t have a good data strategy, then you don’t have an AI strategy.
How Starburst helps with data sharing
Starburst’s modern data analytics platform unlocks the potential of enterprise data by addressing the three challenges of data sharing.
Eliminate data silos
Rather than creating yet another centralized data repository, Starburst unifies the organization’s disparate data sources within a virtual data access layer. This single point of access lets data consumers tap into sources in every business domain without engineering support. Freed from routine support and maintenance duties, data teams can focus their pipeline development efforts on more complex data projects.
Improve security and regulatory compliance
Starburst’s virtualized access layer streamlines security and compliance enforcement. Companies can implement extremely granular role-based and attribute-based access controls. Users only see the schema, tables, rows, and columns they need to see. This fine-grained control simplifies the enforcement of data privacy policies.
At the same time, Starburst makes it easier to monitor compliance. Activity logs document user behavior as well as changes to access policies.
Prevent vendor lock-in
Storage vendors use their implementations of SQL to make their services stickier. A pipeline developed to process data in one service is not easily recycled for another. As a result, data pipeline investments bind companies to their storage vendors.
Starburst prevents vendor lock-in by offering more than fifty connectors to enterprise-class data lakes, relational databases, and other data sources. Abstracting sources as varied as Snowflake, IBM DB2, and Redshift within a unified access layer lets analysts and engineers use the SQL tools they already know to create vendor-agnostic data products.
