Self-Service Data Analytics and Governance for Managers. Nathan E. Myers

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Self-Service Data Analytics and Governance for Managers - Nathan E. Myers


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to be excited about. If an innovation is developed in one part of their organization that has wide applicability and opportunities for replication across the shop, it is their responsibility to put in place an overarching clearinghouse apparatus to capture and scale these opportunities. Perhaps most critical of all is that executives feel convinced that risks are well documented and understood across the enterprise, and that strong policies and procedures are in place to guide the organization in active risk management.

      In Chapter 5, we will discuss further the need to address risk through active risk governance, as operators themselves develop processing solutions with the employ of self-service data analytics tools.

      The data analytics toolkit is growing at a rapid pace, with many off-the-shelf tools that can be customized to perform routinized processing tasks. By shoehorning an unstructured process into a self-service data analytics tool, analysts and operators can structure work into a repeatable process that is stable, documented, and robust – even tactically mimicking a system-based process. Self-service analytics is a form of business intelligence (BI) in which line-of-business professionals are enabled to perform queries; extract, transform, and load (ETL) and data enrichment activities; and to structure their work in tools, with only nominal IT support. Self-service analytics is often characterized by simple-to-use BI tools with basic analysis capabilities and an underlying data model that has been simplified or scaled down for ease of understanding and straightforward data access.

      End-user analytics tools and business intelligence tooling can be readily deployed to automate small bits and pieces of processes in and around systems. Importantly, the involvement of core technology teams is not required to build them, as they would be for a far larger application rollout. When vendor software licensing costs are weighed up against time savings, the average cost of employees, and the additional productivity that can be enjoyed as a result of tool deployment, a significant return on investment (ROI) is evident. End-user tooling can be engaged by virtually everyone in an organization that is able to identify appropriate use cases and to navigate the increasingly accessible and user-friendly functionality.

      Having set the stage, it is now appropriate to introduce one of the key topics of this book, which is the need for strong self-service data analytics governance. Many readers may already have begun to replace their spreadsheet-based end-user computing (EUC) tooling with tactical data analytics tools. We have already discussed the significant benefits available in putting flexible, user-configurable tools into the hands of users. Once the seal has been broken, expect widespread deployment at scale.

      Governance, or lack thereof, is perhaps the strongest harbinger of control and stability, in an environment where self-service data analytics is prevalent. Effective governance is particularly critical due to the expected growth pattern of data analytics adoption, once the floodgates are opened. Without the benefit of governance to keep pace with the decentralization of development capabilities, organizations can find themselves struggling to demonstrate process effectiveness; they may not have clear visibility into the degree to which they are dependent on off-the-shelf software applications; they may lack adequate information upon which to base risk assessments; or they may get it abjectly wrong. Governance must provide guidelines aimed at ensuring the quality and integrity of processing inputs; that processing solutions implemented are appropriate, adequately tested, and operate effectively; that minimum standards of project documentation are met; and that risk assessment and mitigation activities can be demonstrated in the thoughtful deployment of analytics tooling.

      The shift from centralizing processing within systems to the decentralized development model, where end-users are equipped to independently source data and to flexibly structure processing without the involvement of IT, necessitates a commensurate shift in controls. In the past, the controls safeguarding the enterprise from various IT general and application risks were centralized around the core technology stack. With the advent of self-service data analytics tools, increased development capabilities are placed directly into the hands of end-users. Controls embedded in systems are rendered irrelevant, to the extent that processing is done outside of them. This evolution has dramatically shifted the risk environment.


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