![]() Gramm-Leach-Bliley Act ( GLBA) – mandates that financial institutions ( defined broadly) ensure the security and confidentiality of customer information at all times, even while the information is in transit including file, message, and email transfers.Uncontrolled behavior simply can’t be allowed. Existing and emerging regulations now require enterprises that collect customer data to manage it carefully. Governance and security are not optional. It’s hard to keep the trains running on time amid the creative chaos of self-service analytics. The lack of cohesion makes collaboration more difficult, adds latency to workflows, creates infrastructure silos, and complicates analytics management and deployment. Without visibility into decentralized development, the organization loses track of its data sources and data catalog, and can’t standardize metrics. Data flowing into uncontrolled workspaces complicates security and governance. Many of them would rather not own the repetitive deployment phase of analytics and the continuous documentation that is necessary for proper governance.įrom the CDO’s perspective, self-service analytics spur innovation, but can be difficult to manage. Users like the creative side of projects. After all, users tend to focus on their immediate goals – adherence to governance and policies is secondary. #DEFINITION IF ANALYTICAL SANDVOX CODE#If they create analytics on an island, the source code may never be reintegrated into centralized source control. They will copy data to their laptop, data science appliance, or cloud service in order to use their preferred toolchain. If an enterprise constrains freedom, users rebel. Decentralized development creates a many-to-many relationship between development and production, but that activity must be governed. No small task and if it isn’t done correctly, heads will roll.įigure 1: Most analytics organizations have a mix of self-service and centralized tools that combine to deliver insight to their end customers. Recognizing the benefit of empowering users with data, an enterprise still has to manage all of that grass-roots innovation to safeguard personal information, adhere to regulations and keep practices and definitions consistent. One major brokerage firm equipped 16,000+ employees with self-service analytics tools and saw a significant increase in the creation of analytics. Users and analysts across the enterprise develop and deploy analytics for a variety of consumers. The advent of self-service tools has created a many-to-many relationship between analytics development and production (Figure 1). However, centralized development teams can’t keep up with the never-ending flow of requests and ideas from business users. CDO’s find themselves managing a delicate balance between centralization and freedom.Ĭentralization enables data organizations to control access to sensitive information, standardize metrics, clean/wrangle data, deliver data and analytics on an enterprise level, and control operations. Enterprises are searching for ways to control self-service users from a governance perspective without stifling innovation. While data democracy improves productivity, self-service analytics also bring a fair amount of chaos. Enterprises have adopted self-service analytics in order to promote innovation – self-service tools are ubiquitous. ![]()
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