Denodo Execution: A Business Intelligence Journey

In the fast-paced world of business, staying ahead often means addressing challenges head-on and embracing innovative solutions that may not have been considered at the onset. One such challenge emerged when I was faced with the realization that the existing technology strategy for enabling reporting and insights delivery did not align with the business's evolving needs for Business Intelligence (BI), especially their need to move away from a centralized and under-staffed BI team to enabling a self-service capability. This revelation emerged during my first 60 days at a firm at which I had previously worked, after speaking with several data workers and functional area leaders. My journey to finding a solution that would address the gaps identified led to the proposal of a novel approach that would reshape the way data was handled and insights were extracted.

The Challenge: A Need for Business Intelligence

The initial challenge lay in the firm's inability to meet the growing demands of a growing business. The existing strategy assumed that business partners knew precisely what they wanted from the data, which proved to be a limiting factor. This presumption had led technology leaders to embrace a strategy of using data services to create predefined data sets based upon the existing reporting inventory. However, the existing reporting inventory represented in many cases only what was available and the real information needs had yet to be satisfied. Recognizing this gap, the focus shifted to devising a solution that would not only address the current shortcomings but also pave the way for future scalability.

The Novel Solution: Denodo Data Virtualization

My proposed solution took a departure from the conventional approach of centralizing data prior to being able to provide a holistic view of it to the enterprise. Instead, it embraced data virtualization—a strategy that promised a holistic view of the data universe on a far shorter timeline as well as the ability to provide the business with a self-service capability. Specifically, data virtualization empowered the business by introducing an ability to create a semantic layer in memory that enabled the modeling of data so that it aligned with how end users expected to see business data, making it easier to consume and to use. It's important at this point to differentiate between data being available vs. being consumable. The previous data strategy had involved integrating disparate sources and loading them into a central data hub made accessible to the business. The challenge that this approach presented was business users would have to navigate through a trough of data that was not modeled to align with their consumption patterns nor were the names of the data elements aligned with their vocabulary. So, although data was "available" it was only consumable by a handful of tech savvy analysts across the enterprise. Data virtualization remedied that by allowing us to build data models in memory that aligned with the business' way of doing business, views with close alignment to business processes, and the centralization of logic that had previously existed in siloed databases or spreadsheets.

Convincing Stakeholders: A Delicate Balancing Act

Implementing a novel solution is never without its challenges. On the surface, it felt like a "no-brainer", but convincing technology teams and governance boards to divert attention from the official roadmap and to explore a technology that had not been previously considered required a strategic approach. Simultaneously, gaining the confidence of business leaders and addressing their concerns about a new financial obligation and their inherent uncertainty around whether this investment would resolve their information and insights woe was a daunting challenge. For me, the only way to overcome these challenges was to make a diligent, unrelenting, and consistent investment in creating and nurturing relationships across both technology and the business. In retrospect, this campaign was the most important contributing factor to the success of this initiative. Finding great technologists to help me to execute was critical, but the year-long speaking and Q&A tour to garner the support needed to move forward was the key to being able to pull this off.

Conclusion

In conclusion, the journey from recognizing a misalignment in our technology strategy to successfully implementing a transformative solution underscored the imperative of adaptability and strategic foresight in the dynamic landscape of business intelligence. The shift from a centralized and under-staffed BI team to a self-service capability, enabled by data virtualization, not only addressed immediate shortcomings but laid the groundwork for scalable and sustainable growth. The significance of this departure from conventional practices, however, extended beyond technological innovation; it necessitated a strategic and persistent effort to build bridges between technology teams, governance boards, and business leaders. The success of this initiative was not solely contingent on the technical prowess of the solution but, more importantly, on the ability to articulate its value in a language that resonated with our business partners. This journey served as a poignant reminder that technological advancements must align seamlessly with the overarching vision and strategy of the business, emphasizing the paramount importance of effective communication and collaboration in achieving successful and meaningful technological integration.