Gramener Making insights obvious Gramener Visualisation is a fast, flexible product that lets you tell the story of your business, in pictures. NON-INTRUSTIVE INSIGHTFUL SCALABLE Handle terabytes of data Gramener visualises your data quickly and non-intrusively Pull in data from any source, without IT intervention Use the right visualisation to tell the story of your analysis Unlimited data. Near real-time. With no manual intervention. via non-traditional analytics visualised in real-time Gramener transforms your data into concise dashboards that make your business problem & solution visually obvious. Our designs, based on cognitive research, help your eye find important messages quickly. Our experience guides your Visualisations towards actionable decisions. SYSTEM REQUIREMENTS HARDWARE 2 core processor 1 GHz 4 GB RAM 500 GB hard disk SOFTWARE Windows XP / Windows Server 2003 / Windows Vista / Windows 7 DATA SECURITY o Product does not hold any data for storage o Input data needs to be fed explicitly - does not scan sources or ports o Processing does not need remote or network access o All outputs are saved to the identified location only Easily complies with any organizational security policy TECHNOLOGY STACK ARCHITECTURE Python 2.7 is the underlying language Raw data is passed to the server as a plain text file Numpy 1.6.1 for numerical computations SciPy 0.10 for statistical transformations Tornado 2.0 as the templating engine Tabular 0.0.8 for loading tabular data RSA 3.0.1 for license key validation Cygwin for text file processing Template engine … and transformed via custom-built templates. Analytics engine Visualisation library The output of the process is a static PDF or XHTML file … which is shared manually with endusers VISUALISATION LIBRARY Our library has an exhaustive set of custom visualisation charts and templates across industries and functions : • Telecom • Sales & Marketing • Manufacturing • Finance • Contract Farming • Production • Retail • Procurement SOME BUILT-IN STATISTICAL MODELS.. VISUALISATION MODELS PREDICTIVE ANALYTICS Pairwise correlation: Determine most / Linear regression: Determine if output least influences amongst variables increases in proportion to the input Hierarchical clustering: Group variables into related clusters Discriminant analysis: Create a variable that classifies input into distinct groups Segmented averages: Categorize Bayesian classification: Model to metrics for most significant influencers probabilistically classify input into groups Comparison of means: Determine numerical significance of impact Support vector machines: Perform nonlinear transformations for classification ANOVA: Determine statistical significance of impact of a variable Auto regression: Predict future values of variables based on past correlations Principal component analysis: Identify Decision trees: Identify simple but variables most influencing the output optimal rules for classification into groups Spectral analysis: Identify seasonal patterns in time-series data Neural networks: Predict outcome by modeling neural structure of the brain Prioritised metrics: Depict performance weighted by a priority Markov chains: Predict a series by combining chains of patterns in the input [email protected] gramener.com +91 40 6454 5009 blog.gramener.com Hyderabad Plot 9/2, Survey No 64 HUDA Techno Enclave Phase 2, Madhapur Andhra Pradesh 500 081 India.