With the rise in adoption and popularity of Business Intelligence (BI) systems, many organizations are facing the question of how mature their BI systems are and what steps they need to take to boost their BI capabilities and move up in the BI maturity curve to have a competitive edge in the marketplace?
From our experience with successfully carrying out multiple BI projects for our clients, we believe the following 5 key things must be considered as organizations advance in their BI maturity curve.
A robust and scalable BI backed with an agile data warehouse solution with an ability to analyze a variety of subject areas is critical as this can allow business users to identify and address issues in their product or service portfolios. Take an example of a retail organization that captures sales and customer information, but misses out on capturing daily/weekly in-store promotional details. Such a disconnect can result in misaligned sales promotion and marketing campaign, causing operating inefficiencies and revenue loss.
Data latency is a crucial factor when it comes to setting up a data warehousing platform as the cost and complexity of capturing real-time or near real-time can be high. Therefore, organizations need to ask themselves what level of latency they can allow in their operating systems. A high degree of data latency can hinder overall business performance and decision making. An ideal state would be the availability of data when it is needed most.
The utility of data and information captured is determined by the level of validation of accuracy, completeness, and consistency of data. Higher the data quality, better the business adoption of BI applications would be. In industries with higher accountability for businesses expenditure on BI, a Six Sigma reconciliation approach can help ensure that the data flowing into the information management systems is reconciled with source systems on a real-time basis. On the other hand, an outstretched emphasis on data accuracy could become an expensive proposition, more so when businesses could afford to have “good enough” information. Hence, understanding business requirements and drilling them down to data requirements is key to determining the level of data acceptability.
A majority of BI projects often fail to deliver on their business objectives because of lack of user adoption. BI applications are meant to enable users in making both tactical and strategic decisions to help improve operational and overall business performance of an organization. A widespread adoption of user-tailored BI applications across the organization can bring incremental improvements and overall efficiencies to the business.
Lastly, BI applications should provide a 360-degree view of the business. Siloed BI applications often fail to provide an integrated view of the business resulting in their diminishing utility to a particular division or function. The value of these applications is determined based on whether it can provide insights from the cause and effect of retrospective transactions as well as enabling businesses to predicate prospective business outcomes.