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Statistical Data Management is a systematic process that transfers raw data to intelligent data and requires the application of an integrated process. Data Collected may be available in various formats: Excel, Access, ASCII, .dat etc. Data collected in various formats needs to be converted and prepared for further analysis based on a carefully documented analysis plan.
The following steps are followed and carefully managed to prepare the data for further analysis:
- Extracting, Transforming and Loading
- Cleaning and Validation
- Verbatim Coding
- Data Tabulation
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Once the steps above are carefully executed, the data is now ready for further Analysis. These steps may seem simple but unless it is carefully planned keeping the final analysis plan in mind, it can be a nightmare. Every step follows a documented plan that leads to final analysis and interpretation of the findings.
The Processed data can now be used for further analysis independent of the choice of statistical technique but dependent of the Analysis plan.
A variety of Standard and Advanced Analysis techniques can be applied including:
- Standard analyses include regression, correlation, PCA, significant testing, etc.
- Advanced analyses are more goal-driven and include conjoint analysis, TURF analysis, GAP analysis, TREND analysis, etc.
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At a time when companies in many industries offer similar products and services and use comparable technologies, in variety of business problems, the use of predictive analysis is the remaining key process of differentiation. As a result, organizations that effectively implement this emerging use of extensive analytics and fact-based decision-making from the top down are staying ahead of their competition.
What we can do for you?
Data Summarization: - Cross Tabs, Correlation etc
Data Visualization: - Bar Plots, Bubble Plots, Control Charts, Histograms, Pareto Charts, Pie Charts, Q-Q Plots etc
Comparing Samples: - Binomial Tests, Fishers Tests, Chi-Square Tests etc
Regression Analysis: - Linear, Ordinal, Log-Linear models, and Non-Linear Regression etc
ANOVA: - Mixed Models, Multiple Comparisons, Random Effects etc
Nonparametric Tests: - Signed Rank Tests, Chi-Square GOF, Non-Parametric Anova etc
Multivariate Analysis: - Discriminant Analysis, Factor Analysis, Cluster Analysis etc
QC Charts and Analysis: - Simple Control Charts, Process and Quality Control Analysis, Pareto Chart Analysis, Six Sigma DMAIC Tools etc
Survival Techniques: - Parametric Survival Models, Cox Regression etc
Time Series: - Auto Correlation, Seasonal Variation Analysis, Lag and Spectrum Plots, ARIMA etc
Data and Text Mining: - Visualizations, CRISP-DM, Text and web Mining
Missing Value Analysis: - MCAR, MAR, Multiple Imputation
Bootstrapping: - Bootstrap Methods, Jack Knifing, Custom Simulations
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The Tabulated and Analyzed Data goes through the final step of Reporting.
Preparing a presentation is definitely an art, is dealt with utmost attention to detail and is developed by an experienced designer. Don't we wear our best suits on a Presentation day?
It is very important to understand the customer's Style in presenting the report to ensure the results are appreciated. It is true in this case that the "first impression is the best". If the Output presented matches the customers taste in terms of simplicity, branding and layout you can rest assured you will get an incentive.
Reporting is typically based on client specified format and this can be done by requesting for a sample report template that they use as a standard. This sample could also be used to come up with a customized design and style that shares the same feel as well as retains the Agency or Company identity. Clients may request reports in more than one format - PPT, EXCEL (Back up file), MS Word etc.
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