One of the key findings in our latest benchmark research into predictive analytics is that companies are incorporating predictive analytics into their operational systems more often than was the case three years ago. The research found that companies are less inclined to purchase stand-alone predictive analytics tools (29% vs 44% three years ago) and more inclined to purchase predictive analytics built into business intelligence systems (23% vs 20%), applications (12% vs 8%), databases (9% vs 7%) and middleware (9% vs 2%). This trend is not surprising since operationalizing predictive analytics – that is, building predictive analytics directly into business process workflows – improves companies’ ability to gain competitive advantage: those that deploy predictive analytics within business processes are more likely to say they gain competitive advantage and improve revenue through predictive analytics than those that don’t.
Topics: Big Data, Microsoft, Predictive Analytics, SAS, Social Media, alteryx, Customer Performance, Operational Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, Operational Intelligence, Oracle, Information Optimization, SPSS, Rapidminer
Our benchmark research into predictive analytics shows that lack of resources, including budget and skills, is the number-one business barrier to the effective deployment and use of predictive analytics; awareness – that is, an understanding of how to apply predictive analytics to business problems – is second. In order to secure resources and address awareness problems a business case needs to be created and communicated clearly wherever appropriate across the organization. A business case presents the reasoning for initiating a project or task. A compelling business case communicates the nature of the proposed project and the arguments, both quantified and unquantifiable, for its deployment.
Topics: Big Data, Microsoft, Predictive Analytics, SAS, Social Media, alteryx, Customer Performance, Operational Performance, Analytics, Business Analytics, Business Intelligence, Operational Intelligence, Oracle, Information Optimization, SPSS, Rapidminer
The developed world has an embarrassment of riches when it comes to information technology. Individuals walk around with far more computing power and data storage in their pockets than was required to send men to the moon. People routinely hold on their laps what would have been considered a supercomputer a generation ago. There is a wealth of information available on the Web. And the costs of these information assets are a tiny fraction of what they were decades ago. Consumer products have been at the forefront in utilizing information technology capabilities. The list of innovations is staggering. The “smart” phone is positively brilliant. Games are now a far bigger business than motion pictures.
Topics: Big Data, Mobile, Predictive Analytics, Sales Performance, Social Media, Customer Experience, Performance, Operational Performance, Analytics, Business Analytics, Business Collaboration, Business Intelligence, Business Performance, Customer & Contact Center, Financial Performance, IBM, finance, Sales Performance Management, Social, Financial Performance Management, SPSS
Like every large technology corporation today, IBM faces an innovator’s dilemma in at least some of its business. That phrase comes from Clayton Christensen’s seminal work, The Innovator’s Dilemma, originally published in 1997, which documents the dynamics of disruptive markets and their impacts on organizations. Christensen makes the key point that an innovative company can succeed or fail depending on what it does with the cash generated by continuing operations. In the case of IBM, it puts around US$6 billion a year into research and development; in recent years much of this investment has gone into research on big data and analytics, two of the hottest areas in 21st century business technology. At the company’s recent Information On Demand (IOD) conference in Las Vegas, presenters showed off much of this innovative portfolio.
Topics: Predictive Analytics, IT Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, Customer & Contact Center, IBM, Information Applications, Data Discovery, Discovery, Information Discovery, SPSS
IBM’s SPSS Analytic Catalyst enables business users to conduct the kind of advanced analysis that has been reserved for expert users of statistical software. As analytic modeling becomes more important to businesses and models proliferate in organizations, the ability to give domain experts advanced analytic capabilities can condense the analytic process and make the results available sooner for business use. Benefiting from IBM’s research and development in natural-language processing and its statistical modeling expertise, IBM SPSS Analytic Catalyst can automatically choose an appropriate model, execute the model, test it and explain it in plain English.