SPSS Statistics is loaded with powerful analytic techniques and timesaving capabilities to help you quickly and easily find new insights in your data.
Here's a look at the newest features and enhancements designed to help you:

Gain deeper predictive insights from large and complex datasets.

Reveal relationships and trends hidden in geospatial data.

Speed deployment and return on investment.
Discover causal relationships in time series data
Uncover hidden causal relationships among large numbers of time series using the Temporal Causal Modeling (TCM) technique. SPSS Statistics enables you to feed many time series into TCM to find out which series are causally related, and can automatically determine the best predictors for each target series.
Integrate, explore and model location and time data
SPSS Statistics includes geospatial analytics capabilities to help you explore the relationship between data elements that are tied to a geographic location.

Discover trends over time and space  Use the SpatioTemporal Prediction (STP) technique to fit linear models for measurements taken over time at locations in 2D and 3D space, so you can predict how those areas may change over time.

Create association rules that incorporate geospatial attributes  Find associations between spatial and nonspatial attributes using the Generalized Spatial Association Rule (GSAR). It uses historical data such as location, type of event and the time an event happened to describe the occurrences of events, such as crimes or disease outbreaks.
Choose from a wider range of R programming options
Develop and test R programs using a fullfeatured, integrated R development environment within SPSS Statistics. You can also write R functions that use SPSS Statistics functionality with command syntax from within R, and return results to R.
Enhance categorical analysis outcomes
Use a wider range of categorical principal component analysis (CATPCA) capabilities, including:

Nonparametric bootstrapping for more stable estimates

Clustering of cases in addition to variables

New rotation options for better convergence

An easier way to use continuous variables
Create next generation web output
SPSS Statistics web reports have been completely redesigned, with more interactivity and functionality and web server support.
Bulk load data for faster performance
SPSS Statistics writes the data to a text data file, and then the bulk loader script writes the text data back to the database, providing superior performance when handling large datasets.
In addition, SPSS Statistics:

Enables users of Stata 13 to import, read and write Stata 913 files within SPSS Statistics.

Supports enterprise users who need to access the software with their employee identification badges and badge readers.
What's new in version 23?
Geospatial Association Rules
Using geospatial association rules, you can find patterns in data based on both the spatial and nonspatial properties. For example, you might identify patterns in crime data by location and demographic attributes. From these patterns, you can build rules that predict where certain types of crimes are likely to occur.
This procedure is available in the Base Statistics option.
Spatial Temporal Prediction
Spatial temporal prediction uses data that contains location data, input fields for prediction (predictors), a time field, and a target field. Each location has numerous rows in the data that represents the values of each predictor at each time interval at each location.
This procedure is available in the Base Statistics option.
Temporal Causal Models
Temporal causal modeling attempts to discover key causal relationships in time series data. In temporal causal modeling, you specify a set of target series and a set of candidate inputs to those targets. The procedure then builds an autoregressive time series model for each target and includes only those inputs that have a causal relationship with the target. This approach differs from traditional time series modeling where you must explicitly specify the predictors for a target series. Since temporal causal modeling typically involves building models for multiple related time series, the result is referred to as a model system.
Temporal causal modeling procedures are available in the Forecasting option.
Bulk Loading to a database
When you export data to a database, bulk loading submits data to the database in batches instead of one record at a time. This action can make the operation much faster, particularly for large data files.
Programmability enhancements

You can now run R programs that use functions in the R Integration Package for IBM® SPSS® Statistics from any external R process, such as an R IDE or the R interpreter. You can also now run SPSS Statistics command syntax from R.

Extension commands that are implemented in Python or R now support the use of the TO and ALL keywords in variable lists.

IBM SPSS Statistics  Essentials for R and IBM SPSS Statistics  Essentials for Python now include many more extension commands, with associated custom dialogs. Also, help for all extension commands that are installed with Essentials for R and Essentials for Python is now available by pressing the F1 key in the syntax editor.