Today’s organizations face a barrage of new competitors, channels, opportunities and expectations. To connect with customers and achieve competitive advantage, they must embrace constant digital transformation.
This is where startups excel. Agile, entrepreneurial and creative, they are outpacing big business in developing valuable new tools, technologies and methodologies. Their contribution to the US economy is growing, with the 2018/2019 Global Entrepreneurship Monitor finding that 15.6% of the U.S. population aged 18-64 was involved in startups in 2018, a 3% increase since 2016.
In large enterprises that struggle to drive internal change due to their own risk aversion or management processes, leveraging the innovation capabilities of a startup can be the best way to disrupt the marketplace and solve critical business issues.
Driving this digital disruption is big data – from customer interactions, databases, media, the web and cloud, and the rapidly growing internet of things (IoT). By harnessing this data for analytics, startups can supercharge their efforts. That’s where a partnership between a data-rich enterprise organization and a startup can be the perfect symbiosis.
Where such partnerships often stall, however, is in working out how this data will be shared and accessed. By handing their data to a startup, the enterprise might fear losing control of it and security risks. Likewise, the startup’s ability to innovate could be stifled without a clear data sharing and governance framework that allows them to easily access the data and insights they need, when they need it.
So how can a large enterprise collaborate effectively – and securely – with a data-driven startup?
Crawl, walk, run approach
Most organizations find themselves somewhere on the data collaboration maturity curve. When launching a data collaboration project between an enterprise and a startup it can be best to crawl and walk before you run.
Crawl: Launch a Proof-of-Concept as an entry-level project. Controlled exploration of datasets is a great place to start with only a few stakeholders and a startup partner.
Walk: Move onto an extended project. This may involve a larger group of stakeholders and a startup, such as an ML tool, engaging with raw data on a monthly recurring schedule.
Run: This is the enterprise level where multiple projects are being run with different partners and datasets. This requires a robust data governance strategy and team of people to execute correctly.
Facilitate innovation with secure data collaboration technology
So you’re ready to ‘crawl’ with data collaboration, but how do you facilitate data-driven innovation with a startup? Such partnerships often require months of preparation. Both parties will need to meet key stakeholders, set relationships, and start building the trust needed for collaboration. For data projects, they will need to find a way to safely license and exchange data between the two organizations.
This can be achieved much more quickly when there is a data exchange platform inside of which these parameters can be set and enforced. The Data Republic platform provides this in the form of a virtual ‘diplomatic zone,’ where analysts can provision, share and analyze data in a secure, encrypted cloud environment. Data Republic provides a Data Sandbox, allowing startups access to data and enterprises peace of mind that security and privacy is upheld.
The Data Sandbox allows all the complexities of external data sharing to be managed and controlled in a single location and platform. Using this technology, external developers could participate in innovation events such as hackathons within days rather than weeks; or your enterprise could build a pipeline of projects to rapidly evaluate hundreds of startup capability partners. In all cases, data-driven innovation and insights can be derived far more quickly.
Protect both parties with legal agreements and data licenses
Establishing a data collaboration arrangement with a startup can be challenging. They will need to comply with the privacy and InfoSec requirements of their bigger partner, without being unduly restricted in their ability to access data and apply it creatively to problems.
Good governance is a critical part of any data collaboration project. However, even large organizations can lack a consistent set of data governance rules which can be applied across different teams or departments – and much less external partners. Even when such rules exist, scaling them up can be difficult and legally complex. In the case of a major financial customer, we found that finalizing the agreements on technology, contracts and methodologies used in data projects could take up to 18 months.
Data Republic’s platform allowed this customer to significantly accelerate its data-collaboration capabilities. This was achieved through the common legal framework all organizations agree to, plus project-base data license agreements. Once the legal and licenses are approved, governance measures are built into the platform including user permissions, approval workflows, secure analytics workspaces and audit trails. Due to these features, negotiation time for data sharing was reduced 18-fold for the financial customer, and useful customer insights could be co-developed with business partners in hours rather than months.
Safeguard personal privacy
When it comes to data sharing, privacy is paramount as more countries and jurisdictions introduce stringent personal privacy laws. According to the Ponemon Institute, the average cost of a breach in the US reached $8.19 million in 2018, more than double the worldwide average.
Privacy protection is core to Data Republic’s governance controls. Analytics is performed inside a quarantined development environment which allows data partners to access the data insights and reports they need, while keeping sensitive data off limits. No personal information is allowed to be exchanged on the platform. This means that even if a data breach does occur, it can’t be traced back to personal records.
It’s often said that data is the new oil: a driver of business growth and change. For large enterprises, secure data exchange with startups – enabled by a strong framework of governance and controls – can be one of the most effective ways to kickstart data-driven innovation, solve business problems, and stay ahead of competition.