Can Traditional EDW Support the Chief Analytics Officer?
Some enterprises may believe they can continue to rely on existing and proven enterprise data warehouses (EDWs) and management techniques to consolidate their data and run it through their analytical engines. But CAOs know that traditional EDWs are rigid and hard to change.
Traditional warehouse platforms and methodologies are reactive – they chiefly deliver reports; they don’t understand business change, are not flexible, are forced to be more technology oriented, don’t incorporate methodologies that align with business lifecycles, cannot scale for demand and don’t execute with agility.
Data in existing EDWs is aggregated, making it almost impossible to access at granular levels that analytics demands, leading to performance issues
Access to data in most EDWs require operational clearances which means that data scientists may not have access to relevant or complete data sets across functional/divisional silos. For example, marketing data may not be shown to finance data scientist. Rigid functional siloes in which data is trapped (like product development, production, sales and marketing, finance, supply chain etc.) can pose a serious challenge.
Above all is the daunting fact that EDW appliances are expensive and as the data volume grows, so do IT investments.
Looked at another way, the CAO has a formidable challenge to bring—and maintain—the enterprise’s data and analytical practices up to speed. But the larger question before every forward-thinking CAO is: what is a better platform for changing and unpredictable business needs?