Implementing Data Warehouse in the World of Modern Data

The prime objective of the data warehouse is to enable and support business intelligence (BI) activities, especially analytics. Data warehouses have been empowering businesses to derive valuable insights and improve decision making. The data within a warehouse could be extracted from a wide range of sources, both structured and semi-structured. In the long run, data warehouses build a historical view to the data that is priceless to analysts.

Cloud data warehouses have become the core to revitalizing digital transformation strategies. With Gartner forecasting the cloud deployment of databases to reach 75% by 2022, new services are emerging that are driven by the cloud and becoming the go-to solution for data management.

With the explosion of the data and its disruption over the past few years, rapid changes have been witnessed in their design and stores. Speeding up the deployment of data warehouse using automation must be a top priority for organizations to boost time-to-market and agility. Following are key considerations to data warehouse design, which are helping organizations morph their warehouses to be the single source of accessing and utilizing data.

Key Considerations for Designing the Data Warehouse

  • Bringing In Both Views – Outside In and Inside Out: The internal IT and data experts bring in the current know-how, the understanding of the data and processes unique to the business. Combining that external view from industry experts, SMEs, and product owners helps with rearranging the data. This, in turn, allows to arrange the data in a manner that best suits the business objectives, thereby delivering a better user experience.
  • Data Modelling to Best Suit the Businesses: While storing the data in one place and in a consistent format is necessary, arranging the data to use it as a powerful knowledge tool is a critical activity. Modelling the data is also about changing it into bytes of knowledge so that it becomes a powerful business tool.
  • Data Cleansing: Given the myriad data sources, for both structured and semi-structured data, feed them into the warehouse after cleansing, in the right quality, and post-approval benefits organizations in multiple ways. This can be done by applying data cleansing techniques, quality techniques, reducing anomalies, if any, de-duplicating the data, and improving the quality.
  • Allowing Data Access while Establishing the Warehouse: While the warehouse design and implementation is a long-drawn process to achieve perfection, allowing access to the data early is a key aspect to consider. This helps the internal and external users to work through the data and realize its value early. In the process, it helps build a robust warehouse/datastore over the period of time.
  • Implementing Data Lakes for Added Flexibility: By nature, data warehouses are structured, thereby limiting the type of outcomes that could be gained. With semi-structured, unstructured, and streaming data in play today, data lakes are more viable due to their ability to store data in their original format. This opens up more possibilities for businesses to analyze their data and leverage it to enhance their performance efficiency.
  • Building Meaningful Stores: Although data disruption is critical to businesses, designing their own engines and using it in the best way possible is a must. One needs to consider that every business unit has a different purpose for the data and that every bit of data is unique in its own way. A ‘one shoe fits all approach to build the warehouse or data stores would still work in today’s scenario. However, it would be more beneficial to build data stores that serve a purpose, so that the data distribution is not only designed for specific unit needs but also is useful across units, practical, and better serves the businesses. Allowing the use of shared data via marketplaces and other options facilitates organizations to reduce storage costs and enables a consistent set of data across units while mitigating clutter.

Connecting the Dots and Decluttering Them

Data storage feasibility is important, however, what matters the most is the ability of the user to access data, work with it, connect the dots, and harness valuable insights. Data is useless if it is just stored. It will only lead to clutters and turn the warehouse into a landfill. Keeping the warehouses and purpose-driven stores, nimble and aligning it with challenges that the business wants to solve, will enable organizations to turn the data into a powerful growth enabler for today’s businesses.