With a modern data stack, a small business can unify their data across different sources. Modern infrastructure would lead to faster data insights, improved BI capabilities, data science, high-performance processing and transformations, and the ability to integrate new software, frameworks, and BI tools quickly.
While the current system works well enough for fundamental analysis, it still suffers from a legacy infrastructure setup’s weaknesses. Analyzing data across multiple sources is not possible since data is kept in separate storage across different services. The time taken to grant access to and merge different data sets is inefficient.
Raw data is also hidden behind user interfaces. The data behind these visualizations are impossible to access using BI tools and languages such as SQL, Python, and R. With the prevalence of data science, machine learning, and deep learning, a small business can dramatically overhaul their analytical and BI capacity with access to their raw data. Having ownership of their data will allow them to create and customize data science applications like recommendation systems, predictive analytics, custom dashboards, and automated reports.
What is needed is a unified data platform consisting of high-performance data pipelines and a centralized data warehouse using a modern cloud data infrastructure.
This post is part of a 23 part mini-series about implementing a cost-effective modern data infrastructure for a small organization. This is a small part of a whitepaper that will be released at the end of this series.