Data Analysis

The emergence of Big Data has grabbed the attention of analysts as the size of data sets in certain disciplines grows exponentially with vastly improved data collection technologies. At the other end of the scale are the recurring requirements in business to have reliable sales forecasts for a territory or region, to make investment decisions given multiple variables, and to identify the differences between treatments.

Regardless of the size of the data set, Logical Analysis adopts a linear approach to providing the client with business intelligence.

• Objective Setting
  • It is important to understand exactly what conclusions can be drawn from a given data exercise and whether those conclusions will allow for a valid interpretation of the underlying trends.
• Source Review
  • In much the same way as for data migration, the data in existing sets has to be tested for irregularities. If there are inconsistencies, the relevant data cleansing tools are used to remove potential sources of error.
• Instruments
  • Where the project involves the collection of data beyond existing sets, instruments are prepared, tested and linked to storage. Testing goes beyond performing data trials and must address any subsequent concerns about the validity of measures.Recalibration is a last resort especially if, as a result, data already collected is made redundant.
• Statistical Framework
  • The framework establishes the specific routines to be used in analysing the data sets. It also defines the relationships to be tested, the minimum standards for using a particular routine and the rules for interpreting results. The framework is strictly adhered to because any departure in the statistical routines, for example, could limit the reliability of decisions.
• Communication Strategy
  • The results may have to be communicated to a broad audience, some of whom might not have analytic skills or the time to make interpretations. The traditional reporting style using single dimensional charting is still employed although Logical Analysis is working on advanced technologies for multi-dimensional visualisation of data trends.
• Data Management
  • Logical Analysis can install the operational system in-house and provide ongoing management to ensure that the system returns reliable information over time. Alternatively, data feeds can be established for the ongoing analysis to be conducted on the Logical Analysis platform or with a cloud provider.

Logical Analysis is not limited to any one tool set in providing data analytic services. Skills are available to program routines on SAP Business Objects, IBM SPSS/Cognos and SAS platforms. In certain situations, Logical Analysis will also deploy inR, Tableau, Mathematica, Stata and Statistica.