Today, business intelligence (BI) is struggling to fulfil its early promise because it lacks fundamental components that are needed to support predictive analytics.
BI has become too much about data analysis technology and not enough about modelling.
Limitations of BI today
For businesses that want to look backward, BI tools are great. They can help a business understand what happened in the past, but this backward-looking practice is insufficient to support true decision-making. Decision-makers need a system that provides a holistic representation of a business and models system constraints.
BI tools are adequate for supplying updated, clean data, but modelling is not their strength. Representing business constraints and integrating domain expertise into a BI-oriented tool is very difficult.
Most importantly, BI tools will not support experimentation and 'what-if' scenario planning; their structure and the applications built on them are not suited for this.
Intelligence is knowledge. But wisdom is knowledge applied.
All decisions are really about the future. Analysis about the past is helpful, but what organisations really need is the ability to model the future for true planning and business decision support.
Predictive modelling tools such as Enterprise Optimizer do two things that business intelligence software cannot. First, it pulls all of the data together into a single representation. This is helpful to a business because for the first time it can look at the origin of data, how that data gets used, and how different data silos store identical data differently. All data is pulled together and forced to work together - there is no other way to provide a holistic check and balance on a company's information. Secondly, predictive modelling reveals, often unexpectedly, how an organisation really works.
Besides pulling a whole data picture together, predictive modelling builds a mathematical representation of the data that supports analyses in a manner that other data models cannot. For example, if a business can only make so many units of product X, what will its profit be for the year? If a business has a target profit, what are the production requirements for product X? These are two very different models that solve differently. Predictive modelling is the only technology that can properly represent and solve problems in a variety of ways.
Executives, regardless of industry, view the extended supply chain as a mission-critical component for mastering agile performance. New technologies like Enterprise Optimizer are transforming supply chains from cost centres to strategic platforms.
Companies considering predictive modelling tools need to answer a few questions. First, they should determine the critical decisions that the organisation needs to make, looking at it not from a BI perspective but a CEO's. What affects financial performance? What are the key drivers of the business? Next, they should look at the data that the organisation is collecting and keeping. How can that data help in decision-making? What is missing? How can the gap be closed?
With predictive modelling technology such as Enterprise Optimizer, companies should expect to seamlessly connect corporate performance management, financial planning processes, and operational planning systems to:
* Achieve greater accuracy, predictability, and accountability;
* Increase business alignment through the sharing of performance management strategies throughout the enterprise;
* Quantify business risk and rapidly adapt to meet evolving business requirements;
* Improve operational efficiencies, thereby reducing operating costs; and
* Maximise profitability.
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