[7/23] Use Cases: A Cost-Effective Unified Data Platform for Small Organizations

By Eric Burt on November 30, 2020 in Analytics, Data Engineering

Use Cases

After the data has been stored within a data warehouse, it can easily be accessed through user interfaces, BI tools, or SQL queries. Data warehouses are built to be easily connected with BI platforms, third-party applications, and programming languages.

The use cases for having a modern data infrastructure are vast and exceed the scope of this paper. A shortlist of benefits can be summed up in the following bullets:

  • Faster Data Insights
    • Quickly access data in one centralized location
    • Streamlined data access, democratization, and permissions control for analytic, business intelligence, and software engineering teams
    • Storing raw data in a data warehouse allows for the quick computation of higher-level aggregations and metrics without the slow manual process of gathering data from different sources and locations
  • Improved Business Intelligence Capabilities
    • Integrating multiple data sources allows us to make decisions and visualizations based on all of our data
    • Customizable, ad hoc reporting across multiple data sources
    • Ability to go back in time by storing snapshots and historical data
  • Data Science Application Development
    • Having access to data in its raw and tabular format allows us to build software applications that interact with an organization’s data, such as:
      • Scheduled and automated custom reports, KPIs, and alerts
      • Leverage artificial intelligence, machine learning, and deep learning
        • Predictive Analytics – Predicting Future Growth and Bottlenecks
        • Recommendation Systems
        • Automatic Fraud Detection (Orders)
  • Fast, scalable, and automated ETL data pipelines
    • Data Cleansing: detect and remove incomplete, inaccurate, incorrect, duplicate, or irrelevant parts of a dataset
    • Normalization to reduce data redundancy and improve data integrity
    • Cloud environment (AWS) allows us to scale vertically and horizontally as data size changes
    • Open-source applications such as Apache Airflow will enable us to author, schedule, and monitor ETL workflows
  • Quickly integrate new software, frameworks, visualization, and BI platforms
    • increased data democratization – everyone has access to the data they need and the tools they need to interact with it
      • Data democratization allows collaboration between all teams that use our data (Marketing, Analytics, Software Engineering, Executives)
    • Able to quickly and easily swap in and out different software for different users
      • Easily integrate other visualization (Google Data Studio, Tableau)and business intelligence platforms for BI / Marketing users (Looker, Mode)
      • Seamlessly integrate advanced open-source analytic languages and frameworks such as Python, R, SQL
      • Open-source big data analytics frameworks (Apache Spark, Dask, Hadoop Ecosystem)

Data can be easily cross analyzed against different sources as it is centralized in a single location. Permissions can be given to users at various levels to control who has access to specific data. Data can be analyzed over time as a data warehouse can store decades of past data.

Having access to data in its raw and tabular format allows us to build software applications that can interact with our data. Applications include software that computes scheduled and automated custom reports, KPIs, and alerts. The power of artificial intelligence, machine learning, and deep learning can also be leveraged to do predictive analytics, automatic fraud detection, and recommendation systems. Furthermore, custom made dashboard web applications can be made to visualize and interact with the data warehouse.

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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.

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