Posts Tagged ‘data mining’

IBM Cognos TM1 offers powerful enterprise planning, forecasting, and analysis capabilities. These benefits are best realized by extending the solution across departments for a consolidated, integrated planning process, leveraging business-specific financial models that mirror business strategy. Our years of experience implementing TM1 have shown an increasing demand on support personnel to coordinate data transferring and […]

A technology company has literally participated in thousands of projects over the years. At some point the group decided it wants to determine what factors or characteristics may influence the (hopefully successful) competition of current and future implementation projects. Thankfully, they have maintained records in on every project that they were involved in and that […]

Coming from a TM1 background (more business than statistics), it is easy to get stated with modeling once you determine your modeling objective, and Modeler can help with that. IBM SPSS Modeler offers an intuitive interface that will appeal to a wide range of users from the non-technical business user to the statistician, data miner […]

The discovering of relationships within data (between fields) is an important part of any data mining project (in the Crisp-DM methodology, this is described as part of the “Data Understanding” stage). This “relationship discovery” is part of developing a predictive model but is also helpful in answering specific business questions -perhaps even what originally motivated […]

SPSS CLEM is the control Language for Expression Manipulation, which is used to build expressions within SPSS Modeler streams. CLEM is actually used in a number of SPSS “nodes” (among these are the Select and Derive nodes) and you can check the product documentation to see the extended list. CLEM expressions are constructed from: Values, […]

Another interesting feature of SPSS Modeler is its built-in ability to sample data. It is pretty typical to have (in one or more files) hundreds of thousands of records to process, and using complete sets of data during testing can take a huge amount of your time and is inefficient in terms of computer processing […]

Consider the scenario where you have to convert information held in a “categorical field” into a “collection of flag fields” – found in a transactional source. For example, suppose you have a file of transactions that includes (among other fields) a customer identifier (“who”) and a product identifier (“what”).  This file of transactional data indicates […]

Having recently completed the course “IBM SPSS Modeler & Data Mining” offered by Global Knowledge, I was looking to find more opportunities to do some modeling with SPSS Modeler. So, when I read in the news recently, about college recruiters using predictive techniques to determine the probability of a particular recruit graduating on time, I […]

If the pressure to obtain and implement Customer Relationship Management software by healthcare organizations is any indication, decision makers are recognizing the increasing importance of consumer knowledge in the race to improve patient satisfaction scores. Indeed, today, patient insights can lead healthcare organizations to their best opportunities for growth and restoration of profitability far more […]

CRISP stands for Cross Industry Standard Process. It is a process model that describes commonly used approaches that experts use to tackle problems. Typically, you’ll hear of CRISP in the context of CRISP-DM, defining a process or methodology that breaks the process of data mining into six major phases. A little more about the CRISP […]

One of the most important aspects in the progression of business intelligence is the use of buzzwords. These words drive interest in the field and provide common language between the world of technology and business to steer initiatives. However, in an ever-changing industry it is difficult to pinpoint standard definitions. Buzzwords often overextend their original […]

IBM SPSS has been getting a lot of attention lately due to increasing interest in buzz words such as big data, predictive analytics, data mining and statistical analysis. Additionally, the latest version of SPSS Statistics (version 21.0) was also released this month. But what is SPSS and how does it fit within the current solutions […]