In Part 1 of our series, we delved into the foundational steps necessary for companies to transition toward more advanced analytics and AI through the development of an Analytics Capability Roadmap (ACR). This roadmap is crucial for businesses at the early stages of analytics maturity, aiming to shift their focus beyond just descriptive analytics toward predictive and prescriptive analytics. We discussed the significance of defining use cases or projects, evolving the underlying analytics platform, and enhancing team and individual competencies as pivotal areas of focus. Particularly, we emphasized that the challenges in transitioning to advanced analytics are often not technical but lie in addressing the human, cultural, and organizational barriers. Achieving a balance between basic BI use cases and advanced analytics, prioritizing impactful projects, and building a robust analytics platform incrementally were highlighted as essential strategies. Moreover, the development of non-technical skills among analytics professionals, such as working with non-technical stakeholders and managing expectations effectively, was identified as a critical factor for success.

As we move into Part 2, let’s explore how these capabilities evolve over a three-year period, the strategic planning involved, and the actionable steps companies must undertake to not only achieve but sustain analytics maturity, ultimately outperforming industry peers.

IIA RAN clients have access to the full ACR framework and supplemental resources here.

Year One: Earn Goodwill

In the initial year of implementing an ACR for enterprises at the nascent stages of analytics maturity, the focus is on grassroots engagement and understanding the existing analytical landscape within a distributed organizational structure. This phase involves building relationships and goodwill with business leaders and the informal network of analytical talent already embedded across the enterprise. These internal partners, who have been meeting their departmental needs through various means such as outsourcing, assigning analytical tasks to existing team members, or hiring dedicated analytics staff, are pivotal. By engaging with them, one can gain insights into the business needs that drove the decentralized analytics efforts, the data sources they rely on, and the analyses they perform. This outreach is crucial for understanding the broader analytics ecosystem within the organization and identifying systemic barriers to efficiency and effectiveness, such as delays in data access or gaps in cross-functional communication.

Understanding these dynamics is foundational, revealing the competencies and barriers within the current analytics practice from a ground-level perspective. This knowledge is not just about acknowledging the existence of distributed analytics skills and efforts but also about recognizing the value in these dispersed capabilities and the challenges they face. For instance, learning that access to customized data sets is a significant bottleneck or that certain analysts possess unique, siloed knowledge about obtaining specific insights demonstrates where improvements can be made. This year-one competency development is about more than just building a centralized analytics hub; it's about deeply understanding and integrating the distributed analytics prowess that already contributes to the organization's operations. Through direct engagement with both leaders and analytical practitioners, the groundwork is laid for a more cohesive, efficient, and mature enterprise analytics ecosystem.

Year Two: Create an Analytics Community

In the second year of enhancing your enterprise's analytics capabilities, the emphasis shifts toward fostering a more interconnected analytical community within your organization. Recognizing the distributed analytical workforce's knack for identifying subtle demand signals, it's crucial to develop systems that enable these insights to be integrated into broader planning processes. This involves creating a framework where analysts across departments can connect, share their expertise, and understand the broader community they are part of. Such efforts not only make these individuals feel valued and connected but also facilitate the emergence of synergies and efficient pathways for collaboration. For instance, enabling a sales analyst to easily collaborate with a supply chain expert skilled in Bayesian modeling, or smoothing out the process for ad hoc data requests, can significantly enhance analytical outputs. Establishing a Community of Practice (CoP) or a similar entity helps in organizing this distributed knowledge, making repeat processes more visible and actionable for process improvement and support.

This phase is about artfully crafting an environment that encourages collaboration and leverages the diverse skill sets within the distributed analytics community. It's a time to observe emerging patterns, demand signals, and friction points that can inform adjustments in organizational structure and process flows for better efficiency. By doing so, a CoP acts as a central force, drawing out the untapped demand for analytics and identifying areas of opportunity and improvement that remain hidden in a fragmented environment. This initiative not only bridges gaps in analytics practice across the enterprise but also positions these front-line analysts as ambassadors, advocating for analytical successes directly within their business units. Year two is about laying down the infrastructure that reveals and optimizes the internal competencies and information flow, ensuring that the enterprise's analytics ecosystem is robust, responsive, and continuously evolving.

Year Three: Reorg Based on Analytics Competency

By the third year of refining your enterprise's analytics capabilities, a clear delineation of required skills and optimal placement of staff becomes paramount. It's the stage where the structure and reporting lines are implemented to support the strategic vision. The objective is to position individuals with high Emotional Quotient (EQ) skills closer to the business functions to effectively identify, articulate, and translate analytics opportunities into actionable projects. These individuals, often referred to as analytics translators, catalysts, or consultants, play a crucial role in bridging the gap between the demand for analytics from business units and the supply side, which consists of data and IT resources. Their ability to communicate complex analytical outputs in an understandable and actionable manner is key to driving analytics integration across the enterprise.

On the other hand, team members with a strong analytical and technical acumen, the high Intellectual Quotient (IQ) team, are tasked with the deep and creative application of analytics to solve business problems. They must not only possess a broad understanding of analytical techniques but also have the capacity to innovate and articulate data or tool requirements to enhance analytics application. Beyond these core competencies, by year three, the focus expands to include adjacent skills critical to the analytics ecosystem's success. This includes project management to oversee the progression and impact of analytics projects and the delivery of usable data, ensuring that opportunities are not only identified by the high EQ team but also effectively executed by the high IQ technical team. These evolving competencies and structural adjustments underscore a holistic approach to embedding analytics into the fabric of the enterprise, ensuring its operational and strategic objectives are met through data-driven insights.

Final Thoughts

As we look back on this three-year journey, it might feel like a causal chain of events. Of course, the actual experience will be must messier than what we’ve outlined here, but the spirit of the ACR is to strive for a strategic, phased approach aimed at building a cohesive, efficient, and mature analytics ecosystem.

The first year lays the groundwork by engaging with the distributed analytical talent within the organization, understanding their challenges, and recognizing the value of their contributions. This initial phase is crucial for identifying barriers and opportunities for improvement within the existing analytics practice. Moving into the second year, shift your focus to fostering a connected analytics community, establishing a Community of Practice (CoP) to encourage collaboration, share expertise, and integrate distributed insights into broader planning processes. This not only optimizes the internal flow of information but also positions front-line analysts as key ambassadors of analytics within their units. By the third year, the emphasis is on reorganizing based on analytics competency, positioning individuals with high EQ skills closer to business functions to act as translators and catalysts, while those with high IQ skills focus on technical analytical applications. This strategic placement, along with the development of adjacent skills like project management, is critical for embedding analytics into the enterprise's operations, driving data-driven decision-making, and achieving strategic objectives. Through this phased approach, the enterprise not only builds a robust analytics infrastructure but also cultivates a culture that values and leverages data-driven insights across all levels.

As a data and analytics leader, you’re the bridge between the past and the future of your company and that’s no small feat. The ACR roadmap will help you take this bridge one step at a time with confidence.