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.
Operationalize Predictive Analytics for Significant Business Impact
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
Business Case for Predictive Analytics is Simpler Than You Think
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
IBM Advances Business Experience in Using Advanced Analytics
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
IBM Brings New Innovation in Analytics for Business Insights
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 SPSS Analytic Catalyst Makes Sophisticated Analytics Accessible
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.
Topics: analytic catalyst, driver analysis, Operational Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, Customer & Contact Center, IBM, SPSS
Big Data Analytics Faces a Chasm of Understanding
The challenge with discussing big data analytics is in cutting through the ambiguity that surrounds the term. People often focus on the 3 Vs of big data – volume, variety and velocity – which provides a good lens for big data technology, but only gets us part of the way to understanding big data analytics, and provides even less guidance on how to take advantage of big data analytics to unlock business value.
Topics: Big Data, Microsoft, SAP, SAS, Excel, designed data, Operational Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, Cloud Computing, Customer & Contact Center, Financial Performance, Governance, Risk & Compliance (GRC), IBM, Information Applications, Location Intelligence, Operational Intelligence, Oracle, SPSS
Revolution Analytics Rides R Language into Mainstream Business
Revolution Analytics is a commercial provider of software and services related to enterprise implementations of the open source language R. At its base level, R is a programming language built by statisticians for statistical analysis, data mining and predictive analytics. In a broader sense, it is data analysis software used by data scientists to access data, develop and perform statistical modeling and visualize data. The R community has a growing user base of more than two million worldwide, and more than 4,000 available applications cover specific problem domains across industries. Both the R Project and Revolution Analytics have significant momentum in the enterprise and in academia.
Topics: Big Data, SAS, IT Performance, Operational Performance, Business Analytics, Business Intelligence, Business Performance, Cloud Computing, Information Management, Operational Intelligence, SPSS
IBM’s SPSS Shows Chops in Predictive Analytics
IBM acquired SPSS in late 2009 and has been investing steadily in the business as a key component of its overall business analytics portfolio. Today, SPSS provides an integrated approach to predictive analytics through four software packages: SPSS Data Collection, SPSS Statistics, SPSS Modeler and SPSS Decision Management. SPSS is also integrated with Cognos Insight, IBM’s entry into the visual discovery arena.
Topics: Predictive Analytics, Social Media, IT Performance, Operational Performance, Business Analytics, Business Collaboration, Business Intelligence, Business Performance, Customer & Contact Center, IBM, Workforce Performance, SPSS
Our benchmark research on business analytics finds that just 13 percent of companies overall and 11 percent of finance departments use predictive analytics. I think advanced analytics – especially predictive analytics – should play a larger role in managing organizations. Making it easier to create and consume advanced analytics would help organizations broaden their integration in business planning and execution. This was one of the points that SPSS, an IBM subsidiary that provides analytics, addressed at IBM’s recent analyst summit.
Topics: Big Data, Performance Management, Planning, Predictive Analytics, Marketing, Modeling, Sales Forecasting, Analytics, IBM, Uncategorized, SPSS
