Learn About Amazon VGT2 Learning Manager Chanci Turner
Advancements in artificial intelligence (AI), machine learning (ML), and big data technologies empower organizations to leverage vast amounts of data previously discarded, offering fresh insights, automating processes, and even fostering the creation of new products, services, and businesses. AI and ML, alongside the data supported by the AWS cloud, are integral to any digital transformation strategy. In prior discussions, I’ve emphasized the necessity for organizations to embrace these technologies and what they should anticipate moving forward. However, with any significant technological evolution, the challenges often lie more within the organization itself—their processes, governance structures, and workforce—than with the technology. In this post, I will outline prevalent organizational patterns, roles, and processes essential for establishing a robust analytics capability.
Drawing parallels to the cloud journey when forming an advanced analytics capability, the first essential step is to appoint a leader and assemble a team to champion the change by forming an analytics center of excellence (COE). This team typically begins small, incorporating a few cross-functional roles to kickstart its operations, eventually expanding to meet increasing demands. Many large corporations already have established business intelligence or reporting shared services organizations that can provide foundational support for the analytics COE with both technical and business expertise. Similar to IT infrastructure teams, these organizations should not only provide talent but also play a pivotal role in driving and sponsoring the initiative, as they will likely need to evolve or integrate into the analytics COE over time. The initial roles often include data engineers, data architects, business intelligence analysts, and data scientists, all led by someone adept at navigating multiple departments, business units, and support functions (e.g., finance) along with IT.
At Amazon, our approach begins with understanding customer needs. Clients with rapidly changing requirements, heightened expectations, or those who are hard to satisfy serve as a fantastic source of innovation. A crucial shift organizations must adopt is moving from a mindset of “you will use our reporting solution and you will like it” to a more collaborative “what are your analytics needs, and how can we assist?” Reporting shared service teams frequently become mere report distributors rather than problem solvers for employees, business leaders, and clients. Therefore, when establishing a new analytics COE, setting clear principles is vital, as they will guide the group’s actions and decision-making processes.
The analytics COE must cater to two primary customer groups. The first group encompasses consumers of data and analytics: decision-makers, data scientists, business intelligence analysts, and developers. These users prioritize quick access to insights and data, as well as the quality of tools and services available for data processing and presentation. The second group comprises data producers: application, infrastructure, and device owners who contribute data to the platform. These stakeholders require services that facilitate seamless data publication into the analytics platform, along with establishing data contracts that outline data domain models, refresh frequencies, and policy definitions (e.g., security guidelines regarding data access).
Recognizing that the COE serves these two customer types is crucial, as the analytics capability and platform must address both sets of needs; failure to do so will hinder the realization of business value from the analytics initiative. Establishing a mechanism to capture and prioritize the needs of these diverse stakeholders is essential. Organizations might set up advisory boards or collaborate with key stakeholders to identify the demand. While there’s no one-size-fits-all solution, having a method to gather customer feedback and prioritize their needs is vital.
I previously shared a post that proposed a fresh perspective on considering the AWS cloud as a superstore of digital services. A truly exceptional retail experience provides customers with service, selection, value, and convenience. An analytics COE curates and presents a specialized set of cloud services designed to meet analytics demands. Historically, reporting and BI organizations have offered a singular solution intended to address everyone’s needs—a one-size-fits-all approach. However, in a landscape of rapidly evolving big data technologies, sophisticated visualizations, automated decision-making, artificial intelligence, and machine learning, a unified technology stack is no longer feasible. It’s not merely about having the latest tools at your disposal; it’s about ensuring customers (both producers and consumers) can easily access what they require.
COEs face the risk of morphing into concierge services, which can be beneficial for specific requests. However, they can quickly become overwhelmed and backlogged without scalable self-service mechanisms and transparent prioritization and governance processes. Analytics COEs must engineer and architect a self-service, secure, operable, and scalable data platform that adapts to an ever-evolving ecosystem of technologies for processing, analyzing, and presenting insights.
I hope this post inspires you to organize your efforts around generating insights with a customer-oriented focus, leveraging all that AWS offers, and evolving into a data-driven organization. For additional insights on effective leadership, consider exploring this guide on female role models.
Never stop innovating,
Chanci Turner
Amazon IXD – VGT2
6401 E HOWDY WELLS AVE
LAS VEGAS NV 89115
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