Posts Tagged ‘predictive analytics’

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 […]

Recent studies have shown that a high correlation exists between an organization’s ability to create timely and accurate financial reporting and analysis and the increased performance of that company. But why? If you think about it, it makes sense. Companies that embrace automating finance processes can redirect their finance resources to perform more value-add analysis, […]

InsightSquared is a Software-as-a-Service (SaaS) company that is focused on bringing traditional business intelligence solutions to small and mid-sized businesses. They recently completed Series A funding of $4.5 million led by Atlas Venture with participation from Bessemer Venture Partners, NextView Ventures and salesforce.com. Some product features include: Activity Tracking Sales Forecasting Data Quality Monitoring Ratios & KPI’s Nightly Status Emails […]

Research In a recent post (SPSS – Making good on an investment), I spoke about a study on the ROI of SPSS. This time, I would like to call out a study performed by Ventana Research of San Ramon, Calif. in March 2012. This study reports on organizations already using predictive analytics: March 7, 2012 […]

Research (for example, “The Real ROI from SPSS”, NUCLEUS Research 2005), shows that IBM SPSS is a safe investment and indicate that 94 percent of SPSS customers achieve a positive return within less than a year! These returns are found by way of reduced costs, increased productivity, increased employee and customer satisfaction and greater visibility. […]

Dummy Coding with IBM SPSS To understand what is meant by dummy coding, you need to understand 2 forms of data: Qualitative or Quantitative? “Qualitative data describes items in terms of some quality or categorization while Quantitative data are described in terms of quantity (and in which a range of numerical values are used without […]

  Chi-Squared Challenging using SPSS A Chi-Square Challenge (or Test) procedure organizes your data pond variables into groups and computes a chi-square statistic. Here is the specific definition: “The chi-square (chi, the Greek letter pronounced “kye”) statistic is a statistical technique used to determine if a “distribution of observed frequencies” differs from the “theoretical expected […]

You can use the IBM SPSS Split File feature to split your data pond into separate groups for further analysis –based on the values of one or more grouping variables. If you select multiple grouping variables, cases are grouped by each variable within categories of the preceding variable on the Groups Based On list. For […]

There are various types of windows in IBM SPSS Statistics: The Data Editor Window – used to manage your data pond. The Viewer Window – where all results of your data analysis are displayed. The Chart Editor Window – here you can change the colors, select different type fonts or sizes, switch the horizontal and vertical […]

Computing New Variables IBM SPSS Statistics offers more than 70 built-in functions to compute new variables based on your corporate logic. These include: • Arithmetic functions • Statistical functions • Distribution functions • Logical functions • Date and time aggregation and extraction functions • Missing-value functions • Cross-case functions • String functions   These functions […]

Of the Conventional Analytics Mind   Using a conventional mindset, data ponds are identified and analyzed to build predictive models: Data ponds are identified for evaluation Data scientist performs detailed descriptive analysis Based upon the descriptive analysis a “outcome of interest” is established Using established guidelines a modeling technique is carefully chosen A predictive model […]