How to increase your analytics team’s productivity by sharing and reusing data sets
- Datameer, Inc.
- January 13, 2020
Data is knowledge. And just like sharing knowledge, data sharing and reuse provide many benefits, including:
- Making the organization’s optimal problem-solving experiences reusable.
- Enabling better, faster decision-making.
- Stimulating innovation and growth.
- Reducing the loss of know-how within the analytics team(s).
Data sharing and reuse encourage more connection and collaboration between analysts, resulting in important new findings for an enterprise. Sharing resources is also incredibly efficient. Furthermore, this reuse of assets will dramatically increase data acquisition ROI as departments pool budgets to make costly data purchasing decisions. Even with all these benefits, business practitioners, specifically analytics managers, hardly consider interdepartmental data sharing and reuse.
The previous challenges associated with sharing data no longer exist and yet persist in analytics managers’ minds. There will always be the need for teams to protect specific data for the sake of privacy and confidentiality. That aside, the perceived problems around sharing and reusing data in organizations are now solved by off-the-shelf solutions, like Datameer, which are available today.
Challenge The Status Quo
Today’s enterprise analytics environment is not organized around openness – but it can and should be. Knowledge sharing may occur in a team of analysts but, even then, that knowledge is hardly ever disseminated beyond a department. Given this compartmentalized hierarchy, sharing and reusing data assets never cross the mind of managers. Managers can apply a few key tactics to foster best practices in data sharing and reuse.
Data Sharing & Reuse In Practice
There are long-standing academic initiatives to share data and these initiatives can be adopted by private enterprises. A 2008 publication entitled “Towards a Data Sharing Culture” recognized the importance of sharing research and data within the academic community. The authors created an excellent data sharing framework that analytics managers and business practitioners can use to promote sharing and reuse in their enterprise. Managers can adopt best practices through an optimal mix of leadership tactics and infrastructure development.
Incentivize Through Leadership
Measuring, recognizing, and rewarding your team’s data-sharing contributions is the most important aspect of this initiative. Analytics managers can measure the contributions of their team members then recognize those team members for their contributions.
Management can encourage their direct reports to monitor the purposes for which their data are reused. When analysts monitor their data contributions, it not only instills a sense of ownership and motivation, it allows the entire team to quantify the value of their data contributions.
Users can see all their contributions directly in Datameer. They can share their assets and also see where their assets are being used within the community. Furthermore, they can see who and how their assets are being utilized.
Create An Environment For Sharing
Leaders should also consider infrastructure as a best practice. Evaluate how assets are accessed today by asking two questions:
1. Is the data centralized, federated or distributed?
A federated data environment will maximize data reuse. In a federated environment, separate datasets are combined to provide a virtual common data set. The environment requires strict data security standards for only the federated participants. Federated data environments provide easy retrieval and aggregation for analysts and that inspires reuse. The trade-off is that a federated environment requires participant adoption to be successful.
2. Is access controlled, and who determines permissions?
A growing number of organizations are transitioning from central access control to access and permissions at the local level. Local data producers prefer this because they retain control over their assets and are willing to take on the ongoing access decision role. Local access control facilitates sharing, not only from within a direct team but also from sharing more openly with the broader analytics community. Organizations gain comfort with the risks and benefits of becoming more nimble and moving from a centralized authority.
To conclude, by managing knowledge and sharing data properly, employees gain valuable information and deliver better results. There are easy ways to inspire sharing and reuse in the analytics community. The organization stimulates innovation and achieves growth much easier. Furthermore, customers appreciate a company that can demonstrate its widespread expertise and uses it to their benefit. Also, check our blog post titled “Attributes of Companies That Use Analytics Successfully” for additional insights into what drives enterprise analytics success.
Datameer Data Transformation
Datameer SaaS Data Transformation is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake. The multi-persona UI, with no-code, low-code, and code (SQL) tools, brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data. Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance. Direct integration into Snowflake keeps data secure and lowers costs by leveraging Snowflake’s scalable compute and storage.
Datameer facilitates collaboration and data model sharing and reuse amongst technical and non-technical team members, each using their skills and knowledge. The rich set of data documentation and user-supplied attributes, comments, tags, and more enable teams to share knowledge about your data across the entire analytics community.
Learn more about our innovative SaaS data transformation solution by scheduling a personalized demo today!