Data Stored Well is Data Used Well Prescience Decision Solutions November 24, 2022

Data Stored Well is Data Used Well

There is no doubt that over the many years, data has evolved into an indispensable source, offering valuable insights to help make critical decisions. How important data is can be acknowledged by the fact that today it is considered equivalent to oil, soil, water and oxygen. But what is equally important is the need to store and organize data in a manner that aids seamless, accurate and efficient decision making. Data warehousing helps in consolidating, storing and organizing data. While data warehousing has undergone significant changes in its architecture and methodology, there is an urgent need to establish a paradigm to support next-gen technologies such as big data and cloud computing along with modern analytics and reporting requirements. Data warehouses, when deployed effectively, are valuable in organizing data and eliminating redundancies. A well-designed warehouse can provide information in a timely manner to drive effective decision making. With a significant surge in analytical applications and prescriptive analytics, the importance of organized and clean data has definitely increased. A modern data warehouse architecture must have the following functions to support the evolving needs of an enterprise:

  • Manage and integrate both structured and unstructured data types
  • Integrate support for advanced analytics processing to support new, advanced analytics use cases
  • Support near-real-time or real-time access and analysis at a scale (and cost) that was not previously practical
  • Move data to and from cloud services as it is with on-premises data sources and services Integrate transparently multiple platforms in a unified data warehouse architecture
  • Support ad-hoc data / reporting requests

A modern data warehouse must have the capabilities to store all kinds of data—structured, unstructured, semi-structured or data streaming. In addition, a data warehouse must also perform functions such as ingestion, storage, processing and reporting under one umbrella. Here are some of the key aspects that a modern data warehouse must have in today’s data-driven business landscape:



While data warehouse projects are among the most visible and expensive initiatives an organization can undertake, they are also among the most likely to fail. According to Gartner, more than 50 percent of data warehouses fail to make it to user acceptance. With data becoming a critical element for an enterprise’s business operations today, it is imperative that data warehousing projects are executed and implemented successfully. Some of the reasons why data warehousing projects fail are:

  • Not answering the big question – Why does an organization need a data warehouse?
  • Using the Big Bang approach – Delivering usable business functionality and building data warehouse incrementally.
  • Shortening testing and involving business at a much later stage for validation
  • Neglecting maintenance – With enterprise data and analytics requirements changing constantly, a data warehouse project has no end date.

Every data warehousing engagement should identify and implement certain best practices for optimal technological and business returns on investement.


A robust, modern data warehouse must have the capabilities to easily consolidate all the data at any scale and provide deep, comprehensive insights through analytical dashboards, operational reports or advanced analytics for all users.


With a robust data quality process and data governance framework in place, data management and quality will improve over time. Some of the multiple benefits organizations can reap by putting in place robust data governance capabilities are:

  • Better insights from data analytics
  • Accountability for data
  • Reduction in rework and costs
  • Ability to track lineage and hence better business and IT agility
  • Better compliance and reduced costs for compliance reporting