Taming the Wild West of Self-Service Data Preparation
This post was originally published on Jen Underwood's blog.:
The self-service data preparation and analytics movement started with the best of intentions: help make organizations more data-driven, agile and confident in their business decisions. Instead, it’s become a bit of a Wild West. Everyone is working in silos that create inefficient operations – ungoverned processes, duplication of efforts, lack of a single source of truth – and popular analytics tools have fed the chaos. They don’t show data lineage or track changes.
Unsurprisingly, an expensive mistrust problem with self-service reporting is developing within many organizations. It’s also creating vulnerabilities in security, regulatory compliance and privacy (think the European Union’s General Data Protection Regulation (GDPR) personal data legislation set to take effect in May 2018). Going back to the old days when data was strictly controlled by IT is not a good option. The business still needs to function day-to-day, not wait months for a report.
At the end of the day, both data governance and agility have the same goal: preserving an organization’s operational integrity for proactive decision-making. With the proper checks and balances in place, governance actually enables agility in the use of data.
This data marketplace enables users to search and browse cataloged data, metadata and data preparation models indexed by user, type, application and unique data values to break down data silos, eliminates duplicate work and improves data validity. The results are improved data governance, easier data discovery, faster collaboration and better data quality.
For more practical self-service data preparation governance considerations, tips and guidelines to balance agility with the enterprise need for data governance, watch Three Secrets to Effective Self-Service Data Preparation Governance.
The self-service data preparation and analytics movement started with the best of intentions: help make organizations more data-driven, agile and confident in their business decisions. Instead, it’s become a bit of a Wild West. Everyone is working in silos that create inefficient operations – ungoverned processes, duplication of efforts, lack of a single source of truth – and popular analytics tools have fed the chaos. They don’t show data lineage or track changes.
Unsurprisingly, an expensive mistrust problem with self-service reporting is developing within many organizations. It’s also creating vulnerabilities in security, regulatory compliance and privacy (think the European Union’s General Data Protection Regulation (GDPR) personal data legislation set to take effect in May 2018). Going back to the old days when data was strictly controlled by IT is not a good option. The business still needs to function day-to-day, not wait months for a report.
Governance Doesn’t Need to Slow You Down
Data governance provides the management framework for the availability, usability, integrity and security of data usage in an enterprise. It improves visibility, control and trust in data, and by ensuring the safety and accuracy of data, promotes greater confidence in the resulting insights and analytics.At the end of the day, both data governance and agility have the same goal: preserving an organization’s operational integrity for proactive decision-making. With the proper checks and balances in place, governance actually enables agility in the use of data.
Strike a Balance
How do you strike a balance between data governance and business agility? Businesses are shifting from self-service to team-based, enterprise data preparation and analytics so that collaboration, socialization and governance can be achieved.- Engage users – Provide an easy-to-use tool that encourages users to centralize their data and gives secure access to otherwise hard-to-reach IT managed data. Leverage social functionality by providing users with feedback on their contributions and recognition. Create a marketplace that supports self-service with personal, team-specific and corporate-wide data assets.
- Share experience – Help users identify data, processes and other individuals that may be relevant to their work with machine learning-based suggestions. Partner with subject matter experts and data curators to oversee content and quality. Allow analysts to share and collaborate.
- Be efficient – Focus on the most used data in the organization. Employ machine learning to identify trends in usage and adoption and to identify potential risks. Drive standardization and reuse through the marketplace to drive awareness and encourage adoption.
Harnessing Controlled Collaboration
Controlled collaboration is the key to ensuring agility while maintaining governance and security in data preparation and usage, and a team-driven, enterprise data preparation and socialization platform, such as Altair Knowledge Studio, can provide a centralized location for data workers to come together with trusted data sets.This data marketplace enables users to search and browse cataloged data, metadata and data preparation models indexed by user, type, application and unique data values to break down data silos, eliminates duplicate work and improves data validity. The results are improved data governance, easier data discovery, faster collaboration and better data quality.
For more practical self-service data preparation governance considerations, tips and guidelines to balance agility with the enterprise need for data governance, watch Three Secrets to Effective Self-Service Data Preparation Governance.
