Digital transformation is a hard and long endeavor, especially for companies with legacy technology stacks, processes or cultures. However, it is no longer a discretionary initiative. Digitization is a secular trend, and the expectations from customers and associates to engage digitally are becoming table stakes.
It is important to understand that digital transformation does not just include the digital experience/engagement layer, which is easy for business leaders to see and comprehend. Digital transformation requires transformation across all four layers and across the intersection of these layers. Because of this, the transformation of the data layer is a critical enabler for the overall digital transformation. Unfortunately, that is not well understood by many business leaders, which continues to limit the progress that could be made.
Key Layers In The Digital Ecosystem
• Experience/Engagement Layer: In this layer, prospects, customers and/or internal associates engage digitally to perform certain activities. This layer is visible, and business leaders understand it relatively better. User experience research, user experience design, various digital engagement and experience capabilities across web, mobile, voice, email, omnichannel, personalization, etc. belong in this layer.
• Application Layer: Typically, the core business logic and capabilities belong in this layer (e.g., search capability, transaction processing capability, payment processing capability, analytical insights, artificial intelligence capabilities).
• Data Layer: Management of data belongs in this layer (e.g., data lakes, data cleansing and curation, data security, data governance, data warehousing, master data management).
• Infrastructure Layer: This layer manages the core infrastructure, including on-premise and cloud infrastructure.
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Intersection Of Data Layer With Other Layers In The Digital Ecosystem
In today’s digital economy, most businesses are based on the use and trade of information. Thus, data is what provides context and meaning to customers in the experience/engagement layer. Without appropriate data, the experience/engagement layer is limited to a good-looking and slick design but not much substance behind the experience. The same is true in the application layer, as almost all the business logic and capabilities are based on information and data. Quality of data and the ability to access and process data is critical to the application layer. A similar story exists in the infrastructure layer as well. Whether on-premise or in the cloud, we are primarily storing data.
To illustrate the concept, let’s use the example of offering a financial product to end customers in a digital manner. In the experience/engagement layer, offering an intuitive experience to customers for educating, purchasing and getting guidance on the product requires a deeper understanding of the customers and their preferences and providing information and guidance to them in a personalized manner. All of this requires collecting, storing and generating insights from the data for personalization and then connecting the personalization to the digital experience, which requires the appropriate transformation of the data layer.
In the application layer, analytical and artificial intelligence capabilities need to be developed and offered, which connects back to the required transformation of the data layer. To allow the data layer to enable the desired experience, the data needs to be stored in a modern cloud-based infrastructure that will allow real-time and flexible access and scalable performance.
However, many organizations continue to face challenges in investing and focusing on the transformation of the data layer in a long-term sustained manner for a variety of reasons.
• Business value from the transformation of the data layer is still hard for senior business leaders to understand and appreciate.
• They have not invested in treating data as an asset, they are way behind, and they do not have the will and money to come out of the situation.
• They do not treat data capabilities as products and do not have product managers focused on data capabilities.
• They do not have a coherent long-term data strategy.
• They have dabbled in AI and had some successes but do not have a long-term and scalable AI strategy.
For organizations to achieve their long-term digital transformation objectives, it is imperative that they invest in the transformation of the data layer in a sustained manner. To make that happen, the onus is both on product/data leaders and business leaders. Product/data leaders need to continue to educate other senior business leaders and position the value of investing in the data layer. Senior business leaders must do their part in educating themselves and allow investments in the data layer in a sustained manner.