Organisations can significantly increase the value of their data analytics by aggregating data with their partners to get deeper business insights. Imagine being able to run analytics over the data of all the organisations in your industry, or across market segments. While the possibilities of leveraging those insights from sharing data are significant, they also present some challenges and risks, specifically around data security.
Enter data collaboration, a relatively new concept in data management and data-driven analytics that is fast becoming a preferred tool for organizations big and small. All organisations listed on MIT’s 50 Smartest Companies use data collaboration. Data collaboration creates tremendous value in partnerships between organisations, as it allows multiple parties to access and combine data for business insights, without creating copies of entire data sets or having parties share confidential data.
Imagine you are a departmental store looking to grow your business and boost revenues. You know what your customers buy and how much they spend, but how do you get insights on what products they buy that you don’t currently sell? You could collaborate with a payments services provider for insights on what products customers buy that you don’t stock or what they buy from other retailers the day they visit your store.
Data security in a collaborative environment
In a data collaboration environment, data sets from all parties are held in a secure, encrypted repository where only data queries move around, gathering and retrieving aggregated statistical answers. The encryption keys are retained exclusively by the participant who owns the data set, and each participant has the ability to enforce its privacy policies.
What makes data collaboration secure is that data management and data security are implemented at the data layer. Not only does this minimise the risk of data breaches, but it also greatly simplifies data management.
In data collaboration, the data sets of multiple organisations are analysed to obtain combined insight. However, unlike data sharing, each participant involved retains full control of their data at all times.
Data collaboration platforms also eliminate the need for organisations to create APIs. They can also help plan complex solutions to make the data fully accessible, which can take months of intensive pre-work, even before the actual project begins.
Here are some of the best practices to follow when preparing to collaborate on data with other organisations:
Set clear objectives
Before launching into data collaboration, it is critical to establish specific objectives. Since objectives determine the kind of data that will be needed, they must provide the context for all aspects of the execution. Examples of outcomes would be:
- Measure the company's market size
- Understand what motivates the organisation's target audience to purchase other products
- Identify common customer pains and desires
- Besides having specific objectives, it is essential to ask questions about the process, such as:
- What are the questions being asked from the combined data?
- Is the data being used relevant to answering those questions?
- What criteria will be followed when combining the data?
- At what stage of the process will the data be combined?
- Who will analyse the results of the data project?
There are tools and software platforms available to help you in your data collaboration project. However, the tools are only as good as the level of clarity that the objectives provide.
Choose the right data platform
When using data collaboration, it is important to choose a dependable platform to facilitate the workflow. Select a platform that provides specific control for the project's data points. Make sure to use a reliable third party to inspect the data and identify areas of overlap, to ensure that only relevant results are disclosed. (It goes without saying that the third party should not be a member of the data collaboration project.)
Develop KPIs to measure the results
The driving forces behind your organisation’s decision to join a data collaboration project are actionable insights that enable business success. It is important for businesses to develop specific key performance indicators (KPIs) before launching the project. KPIs will allow you to observe the changes in your business that occur while collaboration is taking place.
Don’t forget the paperwork
Despite the safeguards provided by data collaboration platforms, you must ensure the proper handling of the data. Drafting agreements for sharing data can often entail significant transaction costs. One way to get around this challenge is to use Contracts for Data Collaboration (C4DC), which are intended to address the inadequacies of traditional contractual agreements for organisations pursuing a public-private data collaboration.
C4DCs provide a repository of contractual clauses, which are derived from existing data-sharing agreements. Members of the collaborative effort share the repository. Examples of such terms include:
- Participants’ roles and responsibilities
- The data's provenance, purpose, and quality
- Concerns regarding security and privacy
- Use limitations and access provisions
Data collaboration is a powerful force multiplier. When an organisation augments its data and insights with those from other organisations, it leads to better decisions and better business outcomes. Further, it provides the opportunity to learn from the successes and mistakes of others. Done right, data collaboration becomes an ongoing source of fresh opportunities and expansion.
Dan Magid, CEO, Eradani