The traditional Business Intelligence solutions that have been used over the past two decades have majorly been IT-driven and provided users with historical reporting. The underlying data architecture consisted of a centralized data storage solution- an Enterprise Data Warehouse (EDW).
EDWs are often connected to a large network of source systems and act as a central data repository. The data, before being used in reports and dashboards, is standardized, cleansed, and transformed in the EDW. While traditional BI solutions provided a lot of historical information, they are posing newer challenges.
Primarily, having to depend on IT specialists for data analysis is becoming a huge challenge for the business users.
Decision-makers today need access to accurate, real-time insights, and they need them swiftly – without depending and waiting for someone from the IT team to generate the report.
Other major challenges associated with traditional BI solutions include:
- Lack of On-Demand Analysis Capabilities – BI users do not prefer having to wait for analytical information. instead, they wish the BI solution to offer self-service capabilities in which they can analyze data sets themselves as per their own understanding and requirements, at any given time.
- Predictive and Prescriptive Analyses – Traditional BI solutions only provide insights into ‘what happened?’. However, companies today need a forward-thinking approach and wish to have insights into the future – ‘what will happen.’ and ‘what should we do.’ This is possible through a predictive and prescriptive analysis and modelling of data.
Unstructured data – Traditional BI platforms can harness only structured data. BI users today want to leverage data of various formats – semi-structured, unstructured, as well as third party data.