Getting Predictive Analytics Right – The Challenges

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Challenges ahead for businesses implementing Predictive Analytics.

Predictive Analytics is one tool that many organisations are turning to to enhance their bottom line and gain competitive advantage. In recent years its moved away somewhat from being solely the domain of mathematicians and statisticians. Modern software has and continues to make it more accessible for organisations, though getting the best from it may still be challenging and a learning process!

According to research conducted by the Ventana research group, the biggest challenges faced by organisations today undertaking predictive analytics are firstly surmounting the difficulties around preparing data for analysis, and secondly, getting the required access to the data in order to prepare it. In order to get around both of these issues, Ventana report that a number of organisations are moving their data from on premise to cloud based storage solutions. This trend would seem to suggest that in the future cloud based big data analysis tools (including predictive tools) will also grow in importance as scalable predictive analysis solutions will be key as data continues to accumulate.

In a separate post, Ventana discuss the challenges of a skills gap in implementing predictive analytics technology. Research indicates there are significant skills deficits and the performance of  both people and process inhibit the design and deployment of the technology. For example there are skills deficits in Maths and Statistics, technical knowledge and the ability to integrate predictive analytics systems into broader technological systems and domains. In only half of surveyed organisations did business users of the predictive analysis output get involved in its creation, because of the complexity of the mathematics or skills training thats required. In organisations where training is given in predictive analytics technology to solve business problems half were very satisfied with their predictive analytics solution. Where the training was inadequate, percentages dropped substantially. As a variety of tools are used in predictive modelling techniques, it follows that training must be given in multiple tools. Ventana point to Excel and SQL being the most commonly used, but that R, Java and Python increasing in importance. So in effect, organisations need pay more attention to developing a variety of skills, provide training opportunities and seek to combine business and technical acumen. This means in essence the creation of highly skilled cross functional teams, with members drawn from both business and technology.

A point made by Techtarget is that leading organisations use predictive analysis techniques to make correlations that are of use and deliver genuine value to the business. You need to understand very clearly what the business problem is you are trying to solve. There is so much data and so much analysis you can do that its easy to get side tracked into something that doesn’t really solve a business problem or deliver value to a business. So having business user input is key to keep the focus on solving business problems.

To sum up, to really harness the potential of predictive analysis training deficits need to be identified and addressed. Secondly, cross functional groups with diverse skill sets from both technology and the business should  be put together to identify how the predictive analytic technology can deliver real business value and put it into practice in Customer Relationship Management.

References:

Data Preparation is Essential for Predictive Analytics

Skills Gap Challenges Potential of Predictive Analytics

http://searchbusinessanalytics.techtarget.com/feature/Business-focus-is-key-when-applying-predictive-analytics-models

 

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Published by

Data Hothead

Student of Data Analytics, and erstwhile Oracle Applications Consultant.

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