Table Of Contents
In the 18th century, one of the biggest challenges for sailors was determining their exact longitude while at sea. Without this vital information, ships risked getting lost, leading to lost cargo and lives. Various thinkers proposed astronomical methods and other complex solutions, but self-taught clockmaker John Harrison approached the problem from a data-driven perspective. He reasoned that if sailors had a clock that could keep accurate time at sea, they could compare local time (determined by the position of the sun) with the time at a fixed location (like Greenwich, England). The difference would give them their longitude.
To solve this, Harrison created a series of marine chronometers. His fourth version, the H4, was the size of a large pocket watch and lost only about 39 seconds over a 47-day sea voyage.
Harrison’s approach to the longitude problem illustrates the power of using the right data (in this case, time) in the right way. But imagine if Harrison kept this data or the method to acquire it secret or proprietary. Other sailors wouldn’t benefit, and more ships would have gotten lost at sea.
Strategic data sharing is a multifaceted approach that enables enterprises to tap into the information available, both within and beyond their boundaries. Businesses can use shared data to devise better-informed strategies, enhance user experiences, and ultimately drive better outcomes through efficient data access.
Defining strategic data sharing
Strategic data sharing means sharing data among stakeholders to achieve specific goals, create opportunities, or form partnerships. It may even involve providing data for strategic initiatives and collaborations.
The distinction between strategic and general data sharing lies in objectives and focus. General data sharing involves distributing data without a specific purpose. In contrast, strategic data sharing prioritizes optimizing data, generating trustworthy analytics, and addressing business challenges.
Strategic data sharing also demands more stakeholder involvement. As a result, it produces better data quality, tighter security, and stricter regulatory adherence.
The essential elements of strategic data sharing include:
- Stakeholder engagement: Building trust so that everyone is willing to share their data
- Data quality: Ensuring that the data is accurate, complete, and up-to-date
- Security: Protecting the data from unauthorized access, use, or disclosure
- Compliance: Making sure data usage complies with the law
Understanding product outcomes
Mastering the nuances of data products is essential. Delving deeper, this means understanding the key features and success markers of data products. Businesses can fine-tune their data-sharing strategies by studying successful data products. Knowing the essential traits of successful data products is the foundation for creating effective and impactful solutions. Acting on this knowledge makes all the difference.
The anatomy of a successful data product
A successful data product is responsive, reliable, cost-effective, and designed for positive user experiences. However, these attributes don’t exist in isolation. By prioritizing these traits, enterprises can create standout data products that impact business outcomes, especially when incorporating strategic data sharing.
Successful data products:
- Are designed specifically for the data consumer’s needs and capabilities
- Contain the minimal data set that solves a data consumer’s problem
- Are wrapped with appropriate metadata to enable self-service data discovery
- Can be easily and appropriately shared without violating licensing, governance, or any other key business concerns
Challenges in data product development
Enterprises must overcome challenges like integration, governance, and scalability when developing successful data products.
Integration challenges include:
- Ideation: Developing original and effective product concepts
- Market viability: Assessing if the product fits market demands and trends
- User demand: Understanding and anticipating the needs and wants of potential users
- Workflow management: Ensuring smooth processes from conception to final product
- Product roadmap issues: Addressing inconsistencies or gaps in the product development journey
- Product engineering concerns: Overcoming obstacles related to product design and function
Additionally, data quality and adherence to data governance and best practices often pose challenges. To overcome these challenges, enterprises must invest in robust data management solutions, such as governance-compliant automation.
How data sharing influences data products
Just as chronometers transformed 18th-century sea voyages with precise data, modern enterprises use strategic data sharing to improve data products. Enterprises can use shared data to enrich their data products, confirm their hypotheses, and mitigate potential risks. More innovative data products enable businesses to make better decisions, improve customer experiences, and gain a competitive edge.
Data enrichment adds information to raw data. In the business world, data enrichment typically involves:
- Demographic information
- Personal interests
- Purchase history
Businesses use this data to better understand their customers and target them with more relevant marketing messages. Data enrichment also improves the accuracy of predictive models, helping businesses to make better decisions about pricing and product development.
For example, Delta Airlines harnesses its passenger data to deliver a personalized experience. The airline company crafts tailored promotions and offers by analyzing travel histories, frequent flier preferences, and even ancillary purchase behaviors such as in-flight meals or seat upgrades. They use this approach to optimize seat occupancy and boost ancillary revenue. The airline can also ensure passengers receive deals and packages that align with their travel habits and preferences.
Decision validation is assessing the accuracy and effectiveness of decision-making models or rules. It involves checking if the decision-making regulations or models work as expected and produce the desired results. By using shared data, businesses can check if their product predictions are correct and ensure they get the desired results.
For example, a financial institution might deploy a new credit scoring model to determine loan eligibility. By accessing shared data from other institutions or credit bureaus, they can validate their model’s decisions against actual loan repayment histories and outcomes. Collaborating in this manner ensures the model’s accuracy and reduces the risk of bad loans.
Risk reduction aims to prevent bad outcomes. It involves:
- Identifying potential risks
- Evaluating their probability and impact
- Implementing measures to mitigate them
Additional steps may involve implementing security measures, training employees, and developing contingency plans. By using broader data perspectives, enterprises can get a better understanding of the risks associated with data sharing. As a result, they can develop more effective risk reduction strategies.
The Black Monday stock market crash of 1987 is a striking case of the importance of risk reduction. Rapidly evolving computer technology and algorithm-based trading led to a domino effect, causing markets to plummet worldwide. In the aftermath, analysts pointed to a lack of understanding and mitigation of the risks new trading strategies posed.
If businesses had used a broader data perspective to assess the potential consequences of such trading algorithms, they might have minimized the severity of the crash. Just as in that scenario, today’s enterprises can use data to better foresee and address potential risks.
Benefits of strategic data sharing in product development
In the same way Harrison’s chronometer transformed navigation by addressing critical maritime challenges, strategic data sharing is revolutionizing product development. The most profound impact of strategic data sharing includes:
- Increased agility and adaptability
- Comprehensive insights
- Improved user experiences
By leveraging data from various sources, enterprises gain valuable insights that allow them to make informed decisions and tailor their products to meet the ever-changing needs of their customers.
In turn, this leads to better business outcomes and new business opportunities.
Agility & adaptability
By sharing data strategically, businesses can quickly respond to changes in the market and stay ahead of the competition.
In its prime, Nokia dominated the mobile phone market. As Apple and Android harnessed data-driven insights to fuel the smartphone revolution, Nokia lagged behind. The company didn’t harness data to adapt to shifting consumer preferences as effectively as Apple did. That no one uses Nokia phones anymore underlines the importance of leveraging data to remain agile and adaptable.
A holistic view of data allows businesses to see the big picture and make better decisions. By sharing data with partners, businesses receive a more complete picture of the market and their customers. However, they’re also able to develop more effective strategies and make better-informed decisions.
The collaboration between Ford Motor Company and Google serves as a prime example. In 2021, Ford partnered with Google to leverage its AI and data analysis capabilities. By pooling their resources and sharing data, Ford improved everything from manufacturing processes to customer experiences.
Furthermore, a comprehensive view of data helps businesses see what’s happening and make better choices.
Enhanced user experience
Shared data enables enterprises to create more personalized and customized products that better satisfy customer needs. Enhanced user experience and increased customer satisfaction can lead to better outcomes.
Sephora’s Beauty Insider Program is an exemplary case. By collecting and analyzing purchase and browsing data from its members, Sephora tailors product recommendations and offers to each individual’s preferences and skin type. The personalized shopping experience has enhanced user satisfaction and increased customer loyalty, driving repeat business.
Incorporating strategic data sharing into their product development processes enables enterprises to create innovative and impactful products. As a side benefit, it provides businesses with products that resonate with their target audience and drive success in the market.
Data sharing issues and constraints
Strategic data sharing is not without its challenges. Businesses must address data reliability, compatibility, privacy, and ethical concerns to ensure the success of their data-sharing efforts. By tackling these challenges head-on, businesses can implement practical solutions.
Here are some specific examples of how businesses can address these challenges:
- Data trustworthiness, strengthened by data quality checks to ensure that shared data is accurate and reliable. Encryption and other security measures can protect data from unauthorized access.
- Interoperability, strengthened by universal data standards and protocols, ensures that data shared is accessible to other businesses. Businesses may develop APIs to enable other businesses to access their data.
- Privacy, which relies on anonymization and pseudonymization techniques to protect the privacy of individuals who share data. Businesses should obtain consent from individuals before sharing their data.
- Ethics, reflected in codes of conduct that outline ethical principles for data sharing. Businesses should create internal policies and procedures to ensure that data is shared in an ethical manner.
Tools and platforms that facilitating strategic data sharing
The success of strategic data sharing depends on using the right tools to address a critical need. By using the right technologies and platforms, businesses find it easier to:
- Easily share and integrate data
- Improve product outcomes
- Open up new business opportunities
Cloud-based data-sharing platforms, such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform, support strategic data sharing for product development. They offer scalable and secure capabilities so enterprises can easily share data with business partners.
Enterprises may also use data integration and visualization tools to share and combine data to make it easier to understand. Data-heavy businesses that store data in the cloud benefit from better management and decision-making. These tools allow businesses to implement data-sharing initiatives and improve their products and services.
Revelate facilitates data sharing
Harrison’s chronometer was a solution to a profound global challenge. In the same way, strategic data sharing isn’t just about exchanging information; it serves as a vehicle for enterprises to optimize data product outcomes and foster innovation. Understanding the evolution and strategic role of data sharing helps businesses capitalize on its potential. This insight directly influences product outcomes by leveraging shared data. As they navigate the data realm, platforms like Revelate are pivotal.
Revelate is a self-service data marketplace that helps enterprises pursue data sharing. It streamlines and consolidates all data sharing processes into a single environment, and it can be integrated with data sharing platforms like Snowflake and Databricks. Revelate enables businesses to share data across multiple platforms and harness the full potential of their data assets. Use Revelate to improve your bottom line.
Frequently Asked Questions
What is data sharing?
Data sharing is the practice of providing access to data resources to multiple users, applications, or enterprises, enabling secure data access without compromising data integrity. It involves technologies, practices, legal frameworks, and cultural elements that support collaboration and learning between stakeholders.
What are the types of data sharing?
Data sharing can take on various forms, such as collaborative sharing, bilateral sharing, data pooling and data integration. These strategies enable data to be shared safely and securely to ensure its protection.
What is the process of sharing data?
Data sharing makes data resources available to multiple applications, users, or enterprises, without compromising data integrity. This involves technologies, practices, legal frameworks, and cultural elements.
What is a data product?
Data products are platforms or tools that use data to achieve their desired outcomes. They can range from technological products to elements that analyze data and produce results.
What is the difference between strategic data and general data sharing?
Strategic data sharing involves leveraging data to achieve business goals, while general data sharing is mainly used for exchanging data without any particular purpose.
Unlock Your Data's Potential with Revelate
Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!