In today’s data-driven business environment, a common misconception is viewing data analytics purely as the development of dashboards using tools like Power BI or Tableau, with CSV and Excel files as data sources. This perspective often overlooks the crucial step of incorporating an underlying infrastructure that automatically extracts, cleans, stores, and refreshes data.
This article delves into why this comprehensive approach is indispensable for successful data analytics implementation and how it can lead to significant time and cost savings.
The Misunderstanding of Data Analytics’ Full Scope
Many companies undervalue the importance of an automated data architecture in their data analytics process. This oversight usually leads to reliance on manual data exports and updates, a labor-intensive and inefficient method. This practice can lead to frustrated BI specialists who find themselves repeatedly building reports from the ground up with every data refresh.
The Pitfalls of Manual Reporting
Creating dashboards that depend on manual data exports and updates is far from an automated process. Each time a data update happens, BI specialists must painstakingly construct reports from the beginning, a time-consuming and frustrating task.
Moreover, data exports from tools and databases may change format, causing additional difficulties and frustrations for those building dashboards.
The Long-Term Benefits of a Robust Infrastructure
Investing in a well-designed architecture and databases that can automatically power BI dashboards might initially delay the delivery of reports to key stakeholders. However, this investment pays off in the long run by ensuring dashboards operate autonomously, without an analyst’s intervention for data refreshes, and continue to work even during weekends.
Implementing an ETL process at the outset that checks and highlights changes simplifies data management and guarantees an always up-to-date dashboard. It also liberates BI specialists to concentrate on more strategic tasks and in-depth data analysis, rather than just report building.
Automated Architecture: The Key to Scalability and Efficiency
An automated architecture also prepares companies to scale their data analytics operations as they grow. Manual processes become increasingly inefficient and error-prone with the growth in data volume. Automated processes, however, can effortlessly manage large data volumes without compromising accuracy or speed.
Conclusion: Embracing Automated Architecture in Data Analytics
In summary, businesses must grasp that data analytics extends beyond just creating dashboards. It’s essential to commit resources to establishing the necessary architecture and databases that automatically power BI dashboards. While this approach might initially require more patience and higher investment, it leads to long-term time and resource savings, enabling BI specialists to focus on strategic tasks and data analysis.
By automating data collection, storage, and update processes, companies can achieve scalability, efficiency, and precision in their data analytics endeavors.
*the article has been written with the assistance of ChatGPT and the image has been generated using Midjourney
