Is predictive analytics for the future of BI?

Opinion

Is predictive analytics for the future of BI?

by Peter Walker, UK country manager, Information Builders

Business intelligence (BI) projects remain a priority for many organisations, with areas such as predictive analytics gaining traction as more and more companies look to leverage their existing data resources with a view to better extrapolating trends, improving product quality and increasing their competitive advantage.

Traditionally, most BI technologies, including dashboards and reporting tools, use historical and real-time data to identify trends and answer questions related to what has happened. Predictive analytics, on the other hand, churns through large volumes of both historical and real-time information, building a model that can be used to understand why something happened and to project what might happen, therefore enabling organisations to understand what the next action should be and what are the best, and worst, outcomes for a given situation.

Predictive analytics is used to determine the probable future outcome of an event or the likelihood of a situation occurring. It is also used to automatically analyse large volumes of data with different variables, including clustering, market basket analysis and decision analytics. Despite the crunch on IT budgets, it appears that businesses are willing to invest in predictive analytics. Recent studies conducted by analyst house Gartner showed that analytics is one of the top ten strategic technologies for 2011. According to this report, organisations will continue to look at analytics as a crucial tool to make their business more efficient, smarter and agile. Predictive analytics has matured as a core business practice enabling organisations to differentiate from their competitors and increase their market share. It encompasses a number of analytical tools, across a number of different industries, from statistical analysis to qualitative measures, can improve decision-making about everything from which products to innovate to whether marketing budgets are being most effectively deployed.

For example, a credit card company might have a loyalty program in place to improve customer retention. The primary challenge is predicting the loss of customer. In today's world, the credit card company could utilise their data and apply a statistical model that understands customer behaviour in order to predict and score the likelihood of a customer leaving and implement appropriate actions before their customers move to one of their competitors.  

Finance is not the only industry that has benefited from predictive analytics. Online books and music stores have also taken advantage of this technology to gain further insights into their data. Many sites provide additional consumer information based on the type of book or music track purchased. These additional details are generated by predictive analytics to potentially up-sell or cross-sell other related products and services to customers.

Alongside these benefits, there are certain factors that suppliers should look to advise customers on before they select and implement their chosen technology. Even before entering into an agreement with the customer, it is important that the vendor works with the customer to carry out a thorough audit of needs before deploying the appropriate predictive analytics tools for their business. In addition, during the critical selection process, vendors should provide input around capabilities such as ease of use and scalability. The more diversified the business, the more functions and customised models are required. The scalability of the technology and its ability to handle expanded functionality should also be verified and based on an organisation's expected growth.

From the buyer's perspective, they want business users at all levels to be able to make decisions based on accurate, validated future predictions, instead of relying on instincts. In light of this, suppliers should always look to provide appropriate users with the training and expertise required to best utilise this type of technology.  This can include identifying relevant data, building a number of predictive models, and evaluating the most appropriate of these, and deploying the model into the business intelligence strategy.

In summary, any company implementing predictive analytics will definitely benefit from the tool. Although most large enterprises use some sort of traditional BI tool or platform, these tools do not always provide predictive analytics functionality. Incorporating predictive analytics into an existing BI infrastructure can provide businesses with a real competitive advantage.

Consequently, the integration of BI tools is a key consideration when selecting a predictive analytics tool, as is its integration with business critical applications such as its enterprise resource planning (ERP) solution, customer relationship management tool (CRM), and supply chain management (SCM) application etc. Ultimately, since predictive analytics is currently the only way to analyse and monitor the business trends of the past, present, and future, selecting the right tool can be a key success factor in a company's BI strategy.

This was first published in March 2011

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