Are governance and agility really at odds with each other?

REGISTER NOW - 3 Secrets to Effective Self-Service Data Preparation Governance with Jen Underwood of Impact Analytix
Self-service analytics solutions seem like a dream come true for analysts. They allow data workers to be agile in creating compelling and insightful data-driven recommendations for increasing revenue, cutting costs and competing more effectively.
The rapid adoption of analytics software has empowered the average business user to do big, exciting things with data, but a series of small, disconnected data silos are forming in organizations that invest heavily in these tools. These data silos make it difficult to understand where data comes from, how it was produced and modified, and how up-to-date it actually is. Furthermore, the entire analytical process loses all elements of accountability and collaboration, which leads to finger-pointing, mistrust and arguments over whose reporting is correct.
Simply put, the self-service analytics explosion has introduced confusion and security risks without proper governance measures. However, enforcing governance policies is an undesirable fight for IT departments that are already, battling perceptions to establish IT as strategic advantage, not a reactive, inhibiting force.
This is ultimately why many people feel that IT governance and self-service agility are at odds with each other.

Can governance and agility co-exist?

Many business users historically have shunned restrictive governance policies because they perceive the rules and guidelines as a hindrance to speed and agility. However, without proper governance, you sacrifice the ability to scale up and sustainably create impactful analytics in the long run. If people don’t trust the integrity of your data, then investigations and interrogations become commonplace and you waste time answering how you used data in your analysis, rather than answering what we could be doing better based on the results.
The good news is: Governance and agility are just different sides of the same coin. They both aim at preserving an organization’s operational integrity for proactive decision-making. With the proper checks and balances in place, governance helps enable agility in the use of data.
Data governance is the overall management of the availability, usability, integrity, and security of data usage within an enterprise to improve visibility, control, and trust in data.
A well-defined governance framework is the foundation that enables IT and business users to dive into data exploration. Ensuring the safety and accuracy of the data is critical in building confidence in the resulting insights and analytics.

Data governance in self-service analytics with data preparation

Within the context of self-service data preparation, data governance should address the following critical capabilities to balance control and business empowerment:

• Guidelines and processes on how data is stored, archived, backed up, shared, and protected to ensure accountability and accuracy.
• Data stewardship capabilities by setting controlled access to centralized database and reports based on predefined rules to ensure the security of the data.
• Data cleansing capabilities to identify, collate, and remove duplicate information in a centralized data marketplace.
• Data masking capabilities to allow the use of aggregated data in analysis and reports without compromising the privacy of individuals.
• Data lineage tracing, from the web or the cloud, to ensure visibility into the source, time of origin, and parties that have accessed or made revisions to the data source.
• Data usage tracking to better understand user activities and data needs throughout the organization that need to be addressed.

Have your cake and eat it too with ‘controlled collaboration’

Providing data workers with a means to work together, using trusted, governed data sets, in a way that bridges IT to the disparate data silos is the key to ensuring agility while maintaining governance and security in data preparation and usage.
This is the concept of controlled collaboration - offered through Monarch Swarm.
Responsibly empowering business users with the right level of governance is critical for long-term success. With the help of gamification and data socialization aided by machine learning that increases visibility and accountability, you can now introduce agility in BI without foregoing proper governance and security.
Employ systems and software that allow you to carefully balance empowerment and governance so that you can address security, regulatory, compliance, and privacy gaps before they become an issue for your organization.
 
NOTE: If you’re interested in learning more about self-service analytics governance best practices, and Monarch Swarm, REGISTER NOW for 3 Secrets to Effective Self-Service Data Preparation Governance with Jen Underwood of Impact Analytix.