In my previous column titled ‘With Great Data Comes Great Responsibility’ we touched on how harnessing the power of data is essential for strategic and sustainable growth. Farsighted CMOs are already experiencing the challenges of transforming their current target-based operating models towards a wholly customer-centric and data-driven way of working. They will have noticed that this journey of data transformation demands the convergence of strategies, operations, and technologies at an enterprise level. It also requires significant time and resources. So why should an organisation endeavour to go down this path? Because without a data-driven operating model (DDOM), as many CMOs and even CEOs have discovered, their teams may be looking up at the same stars, but “they are likely to see such different things” as Jon Snow, Winterfell’s unsung hero put it.
For customer centricity to become a way of life, businesses have to immerse themselves into a data-driven operating model, or DDOM. Simply put, this is a way of working that centres the business around the customer journey and drives the business toward strategic objectives with informed insights. At a fundamental level, this means that right from individual employees on the periphery of the organisation to the C-suite, any decision that impacts the overall customer experience has to be guided by insights, and not relied on by intuition or educated guesswork, a drastic shift from the old method.
1) Collaboration is key
While DDOM is remarkably powerful in driving efficiencies and value there are certain areas of collaboration that are a prerequisite for its success. To begin with, using a top-down, bottom-up strategy is advised to make your data integration manageable. This is because typically, for many companies, data is scattered throughout the organisation, often siloed among teams, and staggering in its scale. A collaborative “top-down, bottom-up” approach can help secure a sense of balance across the enterprise. For example, starting from the “top,” a business can look at its customer journey stages to determine the most important business challenges and which KPIs could lead to the best answers. In this case, often the business side has the closest view of the customer journey, so it would be that team spearheading this effort.
It would then be the tech and data team’s task to identify the data assets generated across the customer journey that can be fed into the business KPIs. It is here that the “bottom-up” aspect requires the tech team to map out these data assets and their sources, as well as to document the level of effort required to integrate each source. The tech and data team thus, gains a sense of which quick wins to pursue and which sources would require more resources and effort. This information would prove useful in developing a road map for future data integration. With the business team determining the priorities and the tech and data team identifying effort, the organisation gains a starting point for building a single source of truth.
2) Empower the people
A single source of truth, however, is pointless unless widely adopted. It is crucial here that the CIO understands this and encourages everyone in the organisation, regardless of function or technical prowess, to explore data first hand. An interesting way to do this could be by following a customer-centric strategy and developing different reporting experiences for different personas with the business as their customer. By tailoring these experiences to the business audience, the tech and data team can make it easy and intuitive for everyone in the business to delve into the data.
3) Put up a robust data governance strategy
Once DDOM has been introduced across the wider business, the growing number of users accessing data may potentially reintroduce the issue of differing interpretations and challenge the single source of truth that had been established. To combat this, a data governance strategy is effective in underscoring cross-organisational accountability.
For example, each DDOM KPI is assigned a VP sponsor as the face of the KPI, a business steward to respond to any questions about what a KPI means, and a technology steward to handle any questions about the data behind the KPI. This effectively eliminates confusion or conflicting interpretations of the data that may arise with data empowerment.
A data-driven operating model fundamentally aims to eliminate the silos that hinder an organisation, whether data silos, functional silos, or others. It is only through a cross-organisational partnership with technology and data and the development of a robust DDOM that an organisation will be able to accelerate its journey from data to actionable customer insights. And ultimately, this is what will enable businesses to deliver better customer experiences at every stage of their journey.
Disclaimer: The views expressed in the article above are those of the authors’ and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.
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