User Interface Evolution: The Arrival of DX
In today's business world, data powers all operational functions, from sales and finance to marketing and operations. However, the data ecosystem is fragmented, making it difficult to unify workflows and provide a smooth experience between tools. This issue is often referred to as the broken Data Experience, a topic that has been a focus of discussion in the industry for some time.
A good Data Experience is defined as excellence in the areas of Discovery, Community, and Health. It means that everyone in the company knows where and how to find the best data for their specific project. Moreover, it enables employees to see how other people are using data, allowing them to reuse and leverage the work their team members have already done. Healthy data, on the other hand, is easily discoverable, understandable, and valuable to the people who need to use it. It is observable data, with its status always being explicit.
To improve the Data Experience in a fragmented data ecosystem, best practices focus on breaking down data silos, prioritizing integration strategically, iterating on data quality, and orchestrating data flows effectively for unified insights and actions.
One approach is to use AI platforms that automate ETL (Extract, Transform, Load) processes and integrate disparate structured and unstructured data, enabling a unified data view for better decision-making and innovation. Another strategy is to prioritize integration based on business lifecycle needs, focusing on connecting systems that unlock immediate value and support key lifecycle stages.
Continuous iteration on data loads and quality is also crucial. Instead of expecting perfection before starting, it is better to treat initial data imports as baselines and build in checkpoints to adjust and improve over time, especially for critical data powering core functions.
To prevent overwhelming systems and causing noise, it is essential to "skim off the data fat" by migrating only clean, relevant, and useful data. This approach improves system performance and data clarity.
Lastly, implementing intelligent data orchestration can coordinate complex data flows in real-time from multiple sources, creating unified golden records ready for activation in downstream systems. This harmonizes fragmented data and enables personalized, seamless customer experiences.
By following these best practices, businesses can foster an integrated, high-quality, and accessible data ecosystem that supports informed decisions, operational efficiency, and innovation while minimizing costs and redundant efforts. A good Data Experience can take many shapes, as long as it is built upon a rock-solid discovery pillar, a strong sense of community within data teams, and a healthy data ecosystem.
- To build a good Data Experience within the home-and-garden sector, businesses could focus on breaking down data silos in their finance and operations, prioritizing integration strategically, iterating on data quality, and orchestrating data flows effectively for unified insights and actions.
- By automating ETL processes and integrating structured and unstructured data from various sources, technology can play a significant role in improving the Data Experience in the lifestyle industry, providing a unified data view for better decision-making and innovation.
- In the field of business, data-and-cloud-computing solutions can help streamline workflows by skimming off excess data, migrating only clean, relevant, and useful data to improve system performance and data clarity, thus enhancing the overall Data Experience.
