It’s all about “big data” these days, or so it seems. The term itself may seem a bit intimidating. Indeed, today’s businesses are throwing off a staggering amount of statistics, metrics and other raw material like never before.
But there are ways to “get small” with your data, and ultimately make better, smarter decisions that benefit your business.
This is where we dive into the business intelligence life cycle. Understanding how it works and taking control of the cycle requires the discipline of appropriate predictive modeling and other management solutions.
Our research demonstrates that a properly managed life cycle provides a solid foundation for relevant predictive analytics and a framework for creating higher and more sustainable business value.
Adopting business intelligence solutions poses a number of challenges. Examples of organizational challenges include:
CIO perspective—Chief Information Officers need to improve their information management capabilities.
Business perspective—Managers report they frequently miss useful information and often act on the wrong information.
Data growth perspective—Data volumes, both structured and “semi-structured,” is growing at a rate of 30 to 60 percent annually.
Common functional challenges include:
Obtaining an integrated and accurate view of customer information.
Incorrect payee information, leading to fraud and inadequate collections.
Inability to aggregate, analyze and project operational costs effectively.
Lack of integrated buying information.
Inability to agree on key performance indicators.
Inability to forward data across processes.
Given the organizational and functional challenges, corporate boards are asking tough questions on how to manage and disseminate business intelligence. These include:
Do we have explicit strategies for data and information management? How do we know they are truly consistent with our business goals?
How do we make business intelligence widely available to decision makers throughout the organization?
Have there been times when crucial decisions couldn’t be made because of a lack of good information?
Do our decision makers spend too much time analyzing data and too little time acting on insights gained from it?
How do we manage the increase in volume and variety of data sources, including unstructured information?
What should we be doing next?
How do we prepare for cloud applications?
How can we continue to drive down IT and process costs?
Working with our partners at Microsoft and Avanade, we’re applying deep, industry-specific insights to provide answers for clients and help them create sophisticated data analytics capabilities.